Enterprise Information Management with SAP - Amazon S3 · Welcome to the second edition of...

Reading SampleThis sample provides an explanation of what enterprise information management really is, and what it means for any organization. You’ll also find a chapter that introduces a tool that will help you manage your information: SAP PowerDesigner, a modeling and design-time metadata management platform for information management designs.

Brague, Dichmann, Keller, Kuppe, On

Enterprise Information Management with SAP605 Pages, 2014, $69.95/€69.95 ISBN 978-1-4932-1045-9

www.sap-press.com/3666

“Introduction”

”Introducing Enterprise Information Management”

”SAP PowerDesigner”

Contents

Index

The Authors

First-hand knowledge.

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Introduction

Welcome to the second edition of Enterprise Information Management with SAP!The goal of this book continues to be to introduce readers to the concepts ofEnterprise Information Management (EIM), provide examples of how SAP’s EIMsolutions are used today, and offer technical instructions on performing some ofthe most common EIM tasks in SAP. The second edition includes updates to chap-ters on SAP Data Services, SAP HANA, SAP Information Steward, SAP Master DataGovernance, SAP Information Lifecycle Management, and SAP Extended Enter-prise Content Management by OpenText, which are based on recent releases, aswell as some new chapters on SAP Rapid Deployment solutions, SAP PowerDe-signer, and SAP Hana Cloud Integration.

Target Groups of the Book

This book is intended for both experienced practitioners and those who are newto managing, governing, and maximizing the use of information that impactsenterprises. Specifically, it will be of use to business process experts, architects,data stewards, data owners, business process owners, analysts, and developerswho are new to the topic of EIM in SAP. While there are several specific “how tobuild” and “how this works” sections, the book content requires no previousknowledge of EIM or SAP’s solutions for EIM.

This book is also intended for existing information management experts whoneed to expand their skills from a specific EIM domain to broader informationmanagement strategies. This target group won’t need to reference all chapters,but will be interested in new capability information provided in many (e.g., thelatest release information and new products available).

Structure of the Book

This book is divided into two parts:

� Part I: SAP’s Enterprise Information Management Strategy and PortfolioThis part of the book starts by introducing EIM and its main concepts, includinginformation governance and big data. After you understand the ideas behind

1045.book Seite 17 Montag, 25. August 2014 4:41 16

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Introduction

EIM, we move on to an overview of the solutions for EIM within SAP’s portfo-lio, offering brief explanations of the main EIM solutions, as well as the rapiddeployment paradigm for those solutions. Finally, Part I concludes with real-lifeexamples of how SAP’s EIM solutions are used by several different customers.

� Part II: Working with SAP’s Enterprise Information Management SolutionsThis part of the book focuses on how to get started using SAP’s solutions forEIM. Part II includes product details on topics ranging from understanding thecurrent state of your data, to managing unstructured content and gettingstarted with master data governance. This section focuses on select parts ofSAP’s EIM offerings with the goal of providing practical examples and step-by-step instructions for key SAP capabilities. You’ll learn how to model your infor-mation landscape (SAP PowerDesigner), get started assessing and monitoringyour data (SAP Information Steward), integrate both on-premise and cloud datasources (SAP Data Services and SAP HANA Cloud Integration), use data qualitytransforms (SAP Data Services), turn text data into data points (SAP Data Ser-vices), govern your master data (SAP Master Data Governance), manage struc-tured and unstructured content that impacts business processes (SAP ExtendedContent Management by OpenText), and set retention rules and retire informa-tion (SAP Information Lifecycle Management).

With the division of the book into two major parts, you can read the differentparts as you need them. Part I is critical to understanding EIM and the role it playsin SAP’s strategy and portfolio. In Part II, you can access information and insightabout the EIM capabilities that are most applicable to your projects, planning, andinformation management strategy.

More specifically, the book consists of the following chapters:

� Chapter 1: Introducing Enterprise Information ManagementThis chapter provides an introduction to the concept of EIM. It defines EIM,discusses common use cases and business drivers for EIM, discusses the impactof big data on EIM, explains SAP’s strategy for EIM, and discusses commonuser roles of people and organizations that are normally involved in EIM. You’llalso get an introduction to NeedsEIM Inc., which is the fictional company usedas a basis for examples throughout the book.

� Chapter 2: Introducing Information GovernanceInformation governance is the practice of overseeing the management of yourenterprise’s information. It touches all aspects of EIM and must be considered inany EIM strategy. This chapter provides tips for developing your governance


Introduction

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standards and processes, and maps governance activities to technology enablersfor these standards and processes.

� Chapter 3: Big Data with SAP HANA, Hadoop, and EIMThis chapter introduces Big Data in the context of SAP’s solutions for EIM. Spe-cifically, it focuses on the role of SAP HANA and Hadoop.

� Chapter 4: SAP’s Solutions for Enterprise Information ManagementThis chapter describes SAP’s solutions for EIM, introducing and providingoverviews of specific products. After reading this chapter, you will be able toquickly identify which chapters in Part II are of the most interest to you.

� Chapter 5: Rapid-Deployment Solutions for Enterprise Information Management This chapter explains the rapid-deployment paradigm for EIM solutions withpredefined best practices, setting a foundation for the deployment of SAP EIMsolutions.

� Chapter 6: Practical Examples of EIMThis chapter discusses specific examples of EIM application by various custom-ers. Content discussed includes recommendations for your EIM architecture(written by Procter & Gamble), the evolution of SAP Data Services (written byNational Vision), and tips for successful Enterprise Content Managementprojects (written by Belgian Railways). In addition, there are other customer-written sections on data migration, managing master data, data archiving strat-egy recommendations, and recommendations for positioning different SAPtools for data and process integration.

� Chapter 7: SAP PowerDesignerThis chapter focuses on the discipline of enterprise information architecture,and how SAP PowerDesigner enables you to understand your current informa-tion landscape, align business information with technical implementation, andplan for change.

� Chapter 8: SAP HANA Cloud IntegrationChapter 8 introduces SAP HANA Cloud Integration as SAP’s solution for deliv-ering integration between on-premise and cloud applications.

� Chapter 9: SAP Data ServicesChapter 9 introduces SAP Data Services as a data foundation for EIM. Itdescribes the components and architecture of SAP Data Services and walks youthrough specific examples of how to start doing data integration, data quality,and text data processing with SAP Data Services.


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Introduction

� Chapter 10: SAP Information StewardThis chapter introduces SAP Information Steward, which can be used for pro-filing and getting to know the current state of your data. This chapter discussescataloging your data assets, performing data profiling, and monitoring yourdata quality over time.

� Chapter 11: SAP Master Data GovernanceChapter 11 describes how to get started using SAP Master Data Governance foryour master data governance initiatives. It includes a description of SAP-pro-vided master data governance processes and explains how to create customgovernance processes. It also describes the use of SAP Business Workflow andBRFplus for governing master data. Finally, the chapter gives an example ofusing SAP Information Steward in conjunction with SAP Master Data Gover-nance for monitoring and remediating master data.

� Chapter 12: SAP Information Lifecycle ManagementChapter 12 provides background information on the concept of informationlifecycle management. It then specifically introduces SAP Information LifecycleManagement, offering discussions of retention management, system decom-missioning, and how SAP Information Lifecycle Management works to supportthe lifecycle of information.

� Chapter 13: SAP Extended Enterprise Content Management by OpenText Chapter 13 discusses the major features of SAP Extended Enterprise ContentManagement by OpenText, how it uses SAP ArchiveLink, and how it workswith the SAP Business Suite.

� Online AppendicesThere are several appendices to assist you: Appendix A covers advanced dataquality capabilities, Appendix B provides details on SAP’s migration content,and Appendix C provides tips for your first data archiving projects. The appen-dices and an example spreadsheet for monitoring your data migration projectscan be downloaded from the book’s website at http://www.sap-press.com/3666.

Acknowledgments

This second edition would not have been possible without the incredible effortsof a diverse set of authors that contributed to the first edition of this book, guidedto success by the spirited leadership of Ginger Gatling. They laid down a solidfoundation to build upon.


Introduction

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The second edition brought back some familiar faces as well as some new, aspir-ing authors. Without exception, each brought fresh energy and commitment toprovide valuable updates and new content to the book. It was a pleasure to workwith each and every one of them, and I feel extremely appreciative for the extratime many put forth to make their updates meaningful and to keep the book ontrack. In addition, there were many other people that took time out of theiralready busy schedules to provide a fresh perspective or critical eye to the mate-rial. A special thank you to John Schitka, Ken Beutler, Marie Goodell, ConnieChan, Yingwu Gao, Bharath Ajendla, Anthony Hill, Michael Hill, and Niels Wei-gel—your willingness to contribute and provide feedback was truly appreciated.Finally, I would like to acknowledge my manager, Subha Ramachandran, for sup-porting this project as a priority for me and others in the organization.

All of the royalties from this book will continue to be donated to Doctors WithoutBorders (Médecins Sans Frontières). Your purchase of this book helps us support aninternational medical humanitarian organization that delivers emergency aid inmany countries. Thank you for enabling us to provide financial support to thisimportant organization and its critical mission.

I hope the book becomes a valuable resource to you and your understanding ofEnterprise Information Management with SAP. Enjoy!

Corrie BragueEnterprise Information Management Product ManagementSAP Labs, LLC


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This chapter introduces Enterprise Information Management, including common use cases and big data. It also provides an overview of SAP’s strategy for Enterprise Information Management.

1 Introducing Enterprise Information Management

Cloud, big data, and social media are powering new opportunities for companiesthat can leverage information-driven insights in real time to respond to customerpreferences, identify operational efficiencies, and in some cases, create completelynew business models. To achieve transformative business results, best-run busi-nesses treat information as a corporate asset. It’s carefully managed, thoughtfullygoverned, strategically used, and sensibly controlled.

Effective management of enterprise information can help your organization runfaster. As a result, you can achieve new business outcomes: understanding andretaining your customers, getting the most from your suppliers, ensuring compli-ance without increasing your risk, and providing internal transparency to driveoperational and strategic decisions.

SAP helps businesses run better and more simply by enabling IT to more easilymanage and optimize enterprise information. SAP solutions for Enterprise Infor-mation Management (EIM) provide the critical capabilities to architect, integrate,improve, manage, associate, and archive all information. This chapter introducesEIM and explains what it is, why it’s important to organizations, how it fits intoSAP’s strategy, and some typical user roles. Finally, the chapter concludes byintroducing NeedsEIM Inc., a fictional company that we’ll use throughout thebook to illustrate EIM principles.

1.1 Defining Enterprise Information Management

On Gartner’s IT glossary page, Enterprise Information Management is defined as“an integrative discipline for structuring, describing and governing information


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Introducing Enterprise Information Management1

assets across organizational and technological boundaries to improve efficiency,promote transparency, and enable business insight.”1

EIM involves a strategic and governed execution of the following disciplines: enter-prise architecture, data integration, data quality, master data management, contentmanagement, and lifecycle management. It addresses the management of all typesof information, including traditional structured data, semi-structured and unstruc-tured data, and content such as documents, emails, audio, video, and so forth.

To optimize the use and cost of managing information, we must first understand itslifecycle. The active management and governance of information helps in avoidingthe costs that are associated with blind information hoarding. The risk of havingtoo much information is just as real as not having enough when you need it.

Figure 1.1 shows a typical spend on information over time. This is a technologyand resources spend curve. What may be surprising for most organizations is theincrease in spend during the off-boarding phase. Many companies spend a lot ofmoney maintaining information that is out of control. Is the information still used?In what systems? Can you decommission those systems? Are you managing piecesof information that are no longer used?

Figure 1.1 Typical Spend on Information Over Time

1 Source: http://www.gartner.com/it-glossary/enterprise-information-management-eim/

Spen

d

Time

On-boarding Active use Off-boarding


Defining Enterprise Information Management 1.1

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As illustrated in Figure 1.1, there’s an associated cost in bringing information intoan organization, using the information, and hopefully retiring the informationafter it’s no longer producing value. The idea that organizations really just dothree things with information—on-board, actively use, and then off-board—ispowerful when thinking about EIM solutions.

After information is brought into your organization, it’s required for many usesbeyond its original purpose. Hence, it’s advantageous to prepare the informationfor these manifold uses. That way, the effort to repurpose information during theactive-use phase is greatly reduced. When the information is no longer required, itshould be off-boarded or retired in a manner that meets your organization’s legaland business requirements. The truth is that most organizations don’t proactivelyconsider the reuse and eventual off-boarding of information, which ends up cost-ing millions in IT resources due to maintaining systems that are no longer used.

If you adopt an information strategy, the spend changes to what is shown inFigure 1.2.

Figure 1.2 Spend on Information with an Enterprise Information Management Strategy

Figure 1.2 also provides detailed examples of the types of activities involvedwith EIM across the typical lifecycle of information. In the on-boarding phase,activities include the creation of information through online user creation,

Spen

d

Time

On-boarding� Creation� Migration� Import� Retention Policy Planning

Active use� Preparedness� Migration� Import

Off-boarding� Archival� Deletion� Decommissioning


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integration of processes that involves the creation of new information, importof information, and migration of information. Additionally, the on-boardingphase should include lifecycle planning (e.g., how long the information shouldbe retained). Implementing governance and retention policies as the informa-tion is on-boarded dramatically lowers the cost of information over its effec-tive lifetime. Notice that there is still some spend increase as information isactively used. This is from incrementally improving, enriching, and preparinginformation for alternative uses. The key to bending the cost curve down isunderstanding that information has tremendous value beyond its original pur-pose and proactively planning for that in your EIM strategy. The result is thatthe spend curve goes down over time in the active-use phase as information issimply repurposed. Again, this can be achieved because the incremental cost isjust the provisioning of existing known and trusted information—as opposedto starting over for each new information initiative.

Next, we’ll look at an example of information flow through a company and thendiscuss how this relates to information management.

1.1.1 Example of Information Flow through a Company

NeedsEIM Inc. is a fictional company that’s based on real customer examples.We’ll explain NeedsEIM Inc. in detail in Section 1.7 and again throughout Part IIof the book when we describe how to use various EIM capabilities. For now, wewant to introduce NeedsEIM and the types of information it must deal with,including how information flows through the company. This leads to a discussionabout the types of information included in EIM.

Figure 1.3 depicts the business processes of NeedsEIM. It manufactures retaildurable goods, and the majority of its manufacturing is outsourced. This businessmodel results in a complex and diverse supplier network that impacts mostdepartments. The major departments include finance, which must deal with sup-plier payments, and the engineering and contracts department, which must coor-dinate contracts and technical spec drawings with the manufacturers.

The IT department must deal with diverse systems, including SAP and non-SAPsystems. The procurement department is responsible for the supplier relation-ships and ensuring the company gets the most from its suppliers. The sales depart-ment is always looking for new and creative sales channels, including opportuni-ties in the supplier population.



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Figure 1.3 NeedsEIM Inc.

As an example of information flow through NeedsEIM Inc., let’s look at the pro-cess of contract negotiations with a supplier:

1. The proposed supplier must be researched for due diligence, including type ofproducts or services provided, similar customers serviced, reference calls withcurrent customers, quality history, financial and credit ratings, reliability andtrustworthiness, and general reputation.

� This involves emails, online research, and getting information from externalsources such as Dun & Bradstreet.

� This information is shared among the finance, engineering, procurement,and contracts departments.

2. Assuming the due diligence indicates that the supplier is approved, the suppliermaster data needs to be created and distributed to related systems. The scope,projects, pricing, contracts, and legal documents must be created.

� This involves most departments and includes sales if the durable goods pricepoint might be impacted.

� The supplier sends and receives legal, technical, financial, and other infor-mation.

Outsourced ManufacturingHighly diverse and complex supplier network

NeedsEIM, Inc.Manufactures retail durable goods

Contracts!

Procurement!

Finance

!

IT!

Sales!Engineering!


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3. After the contracts are negotiated, the supplier requires ongoing communica-tion, including technical drawings, bills of materials, and other informationrequired to do the work. In addition, financial documents such as invoices, pur-chase orders, and so on are exchanged.

� This includes a lot of collaboration among engineering, contracts, procure-ment, and the supplier.

Figure 1.4 shows the information as it needs to flow through each department.Departments use the information with their perspective in mind: They store it,update it, download it, and ensure that it meets the requirements for their depart-ment’s role with the supplier.

Figure 1.4 Example Information Flow for NeedsEIM Inc.

NeedsEIM, Inc.Manufactures retail durable goods

Contracts

!

Procurement!

Finance

!

IT!

Sales!

Engineering

!



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As you can see in Figure 1.4, the reality is that information is often required bymany departments. Sometimes when the information doesn’t move from onedepartment to another due to application, political, and/or departmental silos,departments create their own “tribal” versions of the information, and eachdepartment has a different sense of its ownership of the information. (We’ll talkmore about tribal information in Section 1.3.2.)

Earlier, we mentioned several kinds of information needed for negotiations witha supplier. This includes detailed information on the supplier, external referencesfor the supplier, pricing and detailed contract information, engineering docu-ments of what the supplier will provide or build for NeedsEIM Inc., as well as bill-ing, invoicing, and all the typical supplier interactions. The next section will breakthis down further into types of information that are required and how this infor-mation is included in EIM.

1.1.2 Types of Information Included in Enterprise Information Management

Figure 1.5 shows the types of information that are included in SAP solutions forEIM that will be covered in this book.

Figure 1.5 Types of Information Included in Enterprise Information Management

These information types are relevant for most companies, including NeedsEIMInc. The following provides more information about these types:

Create RetireInformation Governance

“The carshould self-drive on thehighway”

HTTP


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1 Structured data This includes the familiar data that’s used within an application system (e.g.,customers, products, and sales orders); for example, supplier information suchas name, address, credit information, contact information, and so on. This alsoincludes all purchase orders, sales orders, and other data that’s related to thissupplier.

2 Desktop documents These include Microsoft Word, Microsoft Excel, Adobe Acrobat, and otherdesktop application documents. This data is stored across the enterprise onshared drives and laptops, which means that much of it isn’t controlled at anenterprise level. This content may be critical to the application data, so youneed to manage it with the same importance as the structured data in the data-base. Examples include purchasing documents (e.g., invoices), contracts withsuppliers, legal documents, résumés, and HR documents, to name a few.

3 Pictures, scanned documents, videos, and other images These could be scanned invoices, videos, pictures of products that are sold in acatalog, and drawings of products that are being designed and built. Thesebecome part of the content that needs to be managed and related to the struc-tured data when required. Managing content that’s associated with a core busi-ness process is becoming increasingly important to process efficiency and reg-ulatory compliance. Examples of such content include engineering documentsthat are to be shared with suppliers, pictures of raw materials, routine mainte-nance records in asset management, invoices, and expense report receipts.

4 Semi-structured data This is information such as RSS feeds, blogs and posts, emails associated withpurchasing documents, and other semi-structured information that’s importantto the enterprise.

5 Text data In Figure 1.5, the piece of information that reads “The car should self-drive onthe highway” may come from a survey or be a comment on a social media orother website and, by itself, might not be important. However, if you’re look-ing at car design over the next five years, and 60% of the comments you receiveinclude something about self-driving, this comment warrants further investiga-tion. Information management includes looking into text you receive and anal-ysis to determine sentiment, feedback, input, or actions that should be takenbased on comments. Examples of text data include comments from supplier’s


Common Use Cases for EIM 1.2

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manufacturing process, feedback from internal departments, and comments onsurveys and service tickets.

As you can see, EIM includes the support of traditional structured data andunstructured information, from the moment of creation through retirement. Theretirement of data and information has the same value as creation. After informa-tion is no longer needed, it becomes a liability—a legal liability, a cost liability, orsome other kind of liability. The entire life span of the data and information, andthe governance of that information, is covered in EIM.

1.2 Common Use Cases for EIM

There are many use cases for EIM solutions. Three of the primary scenarios includethe support of operational, analytical, and information governance initiatives.

1.2.1 EIM for Operational Initiatives

This scenario covers the use of EIM in the operation and execution of businessprocesses and tasks that happen throughout the day. It has very broad applica-tions, from ensuring that material replenishment data is set correctly, to customerdata quality management, to migrating new data from a merger, to ensuring thatall contracts and documents are available for the business process, to removingdata that is no longer required.

SAP solutions for EIM provide trusted data to drive and deliver best practice busi-ness processes. This value includes the ability to holistically manage data withinbusiness processes, ensuring the quality and ability to reuse the data.

Here are a few examples of operational uses of EIM:

� Cloud integrationAs more business applications are running in the cloud, organizations need away to integrate business processes and data between on-premise and cloudsystems.

� Data migration due to mergers, acquisitions, and global implementationsacross all industriesInformation management lowers the risk of business and application disrup-tion during mergers, acquisitions, and new application implementations.


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� Harmonized master data across line of businessesHarmonized master data across disparate applications enables a single view ofmaster data across the enterprise.

� Compliance and regulations in the financial industryThe financial industry has requirements for financial risk-related data analysis.All data must meet quality levels and industry standards, and all associated con-tent (e.g., documents and invoices) must be correctly associated to financialcontracts.

� Suspect tracking in public safety organizationsFederal, local, and state agencies must share information on criminal activity andsuspect tracking. Information management ensures that each new suspect iscompared to others to confirm that it’s a unique suspect. Data quality rules canensure that the most up-to-date information is available for suspect tracking.

� Retaining and deleting information in the pharmaceutical industryDuring the development of new medicines, all documents and governmentstandards must be adhered to through various stages of research, development,trial, and release. When the compliance period has ended, information shouldbe removed unless it’s required for a legal hold.

� Fraud detection in telecommunication and other industriesTelecommunications, media, high tech, and utilities share similar requirementsfor capturing, addressing, and mitigating fraudulent activity. Large volumes ofdata and real-time transactions place these industries at increased risk, as per-petrators can be “on and gone” before they are caught using traditional time-consuming software reporting methods provided by vendors today. Informa-tion management enables the filtering of diverse data to determine where thecompany is losing money across a broad spectrum of applications and businessprocesses.

� Plant maintenance compliance and data assessmentEnsuring that the virtual plant aligns with the physical plant, information man-agement ensures that maintenance plans and documents are associated witheach asset, asset tags are accurate, functional location information is complete,and all asset document and maintenance guides are available on the plantfloor.

� Data quality and data assessment in the retail industryThe retail industry requires high data quality; for instance, retailers must knowarticle data throughout all stores where the articles are sold. For retailers, data


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quality and assessment is an ongoing business process; it includes, for example,tracking articles that have not been maintained in required stores, articles miss-ing valid sales price conditions, articles missing required procurement data,and articles with duplicate EAN codes.

Notice that many of these examples are focused on ensuring that information ismanaged, is available, is reliable, and serves the operational business process; thelist can go on and on.

Chapter 6 provides more detailed real-world and practical scenarios for EIM.

1.2.2 EIM for Analytical Use Cases

EIM has a long history in business intelligence (BI) and analytics. If you look atsome definitions of EIM online, you’ll see statements saying that EIM drivesdecision-making analytics. Many of the operational use cases mentioned previ-ously also fit into operational reporting and have some reuse for strategic report-ing and analytics as well. Some examples of EIM for BI and analytics include thefollowing:

� Big data analysisTo unlock the potential of big data sources, EIM provides the capabilities toaccess and understand data from any source and variety, including Hadoop,and integrates it with existing data for better analysis of customer sentiment,fraud detection, new innovation opportunities, and competitive insights.

� Analysis of supplier spendAnalysis of who are the top suppliers, how much they spend, and payment andcredit issues can only be done if supplier records are transparent and harmo-nized, cleansed, and de-duplicated. When making decisions that are related tothe supply network, the supplier data must be accurate and trusted.

� True cost assessment of manufacturing goods in the manufacturing industryAnalyze total costs for making and delivering products. Crossing multiple busi-ness domains, data must be cleansed, duplicates removed, and correlations cre-ated to ensure that analysis provides accurate information.

� Bring together timely, accurate, and actionable data to provide insights intothe factors impacting sales and customer behaviorSilos of data sources and applications, limited business user access, and depen-dence on IT to create reports limits the ability of a business to gain insights on


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sales and customer behavior. Information management brings together the dataand provides data lineage and analysis so the users can create reports and knowwhere the data is coming from.

� Text mining to understand opinion and sentimentText and rich media content that’s accessible on the web or on social mediasites contain a lot of information that can be analyzed and used for sentimentanalysis to get a better understanding of consumer opinions about a product oridea.

1.2.3 EIM for Information Governance

A primary use case for EIM is the management and governance of informationas a strategic asset, usually referred to as information governance. Informationgovernance is a discipline that oversees the management of your enterprise’sinformation. Without it, there is no EIM. Information governance involves peo-ple, processes, policies, and technologies in support of managing informationacross the organization. It’s advisable to have some degree of information gov-ernance in place for any EIM use case, analytical or operational, as this providesa framework for the enterprise to reuse policies, standards, and organizationalbest practices.

Information governance is the linchpin of EIM that empowers business users toown and manage data as a strategic asset, governs data in the business process tooptimize operational performance and ensure compliance, and establishes trust instructured and unstructured information by ensuring data quality throughout itslifecycle.

Information governance will be a common thread throughout the book and willbe covered in more detail in Chapter 2.

1.3 Common Drivers for EIM

Information can be a strategic weapon if an organization manages enterpriseassets such as capital. Treating information as an organizational asset recognizesthat it moves from a single-purpose use to something that must be managed formultiple uses.


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Common business problems that require an EIM strategy often may not have thewords information, enterprise, management, data, or governance in them. The busi-ness issues driving initiatives for EIM include (but are not limited to) trucks goingout at the wrong weight, deliveries to the wrong location, hazardous products notin compliance with government standards, customer satisfaction issues, incorrectbilling, misunderstood supplier networks, services that don’t align with customerdemand, lack of compliance with a government mandate that impacts paymentsor revenue, and so on. Many process issues are the result of a lack of an informa-tion management strategy—from poor-quality data to master data not beingupdated correctly, to not having the documents required for order processing, tofinancial documents not aligned with sales documents, to different parts of theorganization using similar terms in different ways.

Adoption of EIM capabilities is usually driven by a few fundamental needs—responding to a growing set of compliance requirements, improved operationalefficiency, and the strategic application of information to better manage yourorganization and gain competitive advantage.

Next, we discuss specific examples of issues as drivers of EIM adoption.

1.3.1 Operational Efficiency as a Driver of EIM

Operational efficiency includes many moving parts to ensure the company has animproved operational margin. From the EIM perspective, operational efficiencyincludes the provisioning and preparation of data so that it can be used to keepthe business running well. The following subsections describe typical operationalefficiency scenarios and the role of EIM.

Improving Payment Processing

The time that’s taken to collect payments and the improvement of payment pro-cessing is critical in all industries, and is heavily impacted by the quality of the data.One example is the healthcare industry, in which it’s critical to ensure that hospitalscollect what they should from government agencies such as Medicare and Medicaidin the United States. Effectively provisioning data from disparate systems ensuresdata compliance with U.S. laws for Medicare and Medicaid and enables hospitals toreceive their payments, having an impact in the millions of dollars.


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Ensuring a Successful SAP ERP Go-Live

An SAP customer was implementing a new SAP system and had to migrate datafrom many non-SAP data sources. The customer was concerned about the largevolumes of data to migrate from both the parent company and a variety of sub-sidiaries. It was critical that the entire business not be on hold during the migra-tion, and the data from the migration had to be loaded accurately and safely. Therequirement from the customer was a single, integrated application providing ahigh degree of visibility that could be easily rolled out to multiple subsidiaries,eliminating hours of custom coding to load data into the SAP system. SAP’s EIMsolutions were used to extract data from third-party applications and support asmooth transfer to a new environment. This automated approach saved valuableresources and expedited data migration processes, resulting in a smooth—and on-time—go-live, reducing the overall cost of the implementation.

Consolidating Systems to Improve Information Management and Reduce IT Spend

An SAP customer ran 80% of its business with several SAP systems and wantedto reduce IT costs and improve transparency of information across the systems.The company had 8 SAP systems when only 3 were needed and more than 400non-SAP systems, most of which could be retired. EIM’s role in this includedthe assessment, alignment, migration, and retirement of data and legacy sys-tems and ensuring that the 3 remaining SAP systems had accurate and timelyinformation.

Speaking the Same Language to Increase Operational Efficiency

Another SAP customer had issues where no one spoke the same language. Forexample, the term margin covered different realities depending on the depart-ment and employees concerned. To set things right, the company specified fourobjectives for itself: to centralize its data in a common environment; to secure thedata; to make the data more reliable, especially for management access; and tostandardize its vocabulary for indicators. EIM accelerates employee access toinformation and, as a result, saves significantly on the amount of time required toperform routine tasks. Teams made enormous gains in responsiveness. Where itpreviously took one week for data to be available after accounts were closed, theoperation is now instantaneous.


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1.3.2 Information as an Organizational Asset

All organizations have assets—capital, employees, materials, brands, and physicaland intellectual property—that are all managed carefully. Information is similar,as it, too, is an asset that must be managed and protected. With the right EIMstrategy, information can be leveraged and used as an organizational asset. We’llnow discuss some specific examples of how actual companies use information asan organizational asset.

Improving Patient Care and Payer Response

An SAP customer is a large hospital conglomerate focused on first-class patient careand creating innovative ways to improve care. First-class patient care requires themanagement of information in large volumes and with daunting complexity. EIMwas used to extract, transform, integrate, cleanse, load, and correlate patientrecords from many diverse systems for analysis by doctors and line managers. Thecleansed and aligned data enabled line managers to improve operational efficiency(including aligning information across multiple hospitals). The project extendedthe use of information such that doctors now have the ability to “slice and dice”information as needed on patient groups and to provide recommended treatmentsand wellness programs based on trends, including re-admittance trends, long-termperformance of different treatments, and so on. The other focus of the project wasto ensure a high quality level of data provided to and by patients. The improveddata quality improved patient service, which led to improved payments by payers,resulting in the collection of several million outstanding dollars.

Growing Past “Tribal Knowledge” to “Enterprise Information”

A large SAP customer had a wealth of information that was vitally needed acrossdepartmental lines, but the information—documents, spreadsheets, manuals—was locked up in information silos. Shared—or nonshared—hard drives, separateportals, and multiple content repositories held the data, with no central search oraccess capability. This resulted in “tribal knowledge;” the different departmentscould usually find the information that their employees created, but this informa-tion wasn’t effectively shared with other departments. By implementing a strate-gic Enterprise Content Management (ECM) and global search capability, the cus-tomer was able to create a single enterprise information store that all employeescould search and use, regardless of department.


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Improving Data Quality for Customer Interactions

Another SAP customer had a goal to create a 360-degree view of customer data forsales, marketing, and service. EIM was used to consolidate heterogeneous datainto a single database; integrate structures and processes across sales, marketing,and service; and ensure a systematic information exchange between field salesand sales support. Improved customer data quality strengthened dialogue withthese customers and systematized customer-related processes across sales, mar-keting, and service. The data quality improvement and improved transparencydrove a new structured quotation process. The new process provides time savingsand fewer errors when creating quotations.

1.3.3 Compliance as a Driver of EIM

As governmental regulations and controls increase, and the cost of legal issues dueto data issues rises, compliance plays a key role in most industries. Every companyand its network of suppliers that produce a durable good that ends up in a shop-ping cart have compliance requirements. Other organizations, such as utilities,government agencies, security, and financial service providers, are also subject toregulatory and compliance issues. In addition to industries, countries have importand export regulations that impact the ability to do business globally.

In the following subsections, we discuss some general examples of complianceissues that indicate a need for an information management strategy.

Keeping Data Too Long

For regulatory compliance, companies must ensure that they keep retention-rel-evant data for a minimum period of time, as defined by retention laws. They mustalso ensure that certain data is purged from the system. For example, data privacylaws mandate the destruction of person-related data after a specified period oftime. In Germany, companies must delete data from rejected job applicants intheir HR systems not earlier than 6 months, but not later than 12 months, afterthe applicant was rejected. Failing to comply with these regulations may result inlarge fines for companies. Another example is a pharmaceutical company thatmust keep information related to a new clinical trial for a number of years. Afterthat time has passed, the information should be deleted. Not deleting sensitiveinformation after the required retention period increases the risk from potentiallawsuits.


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Building According to Specs

Remember the Mars probe? Launched in 1999, the Mars Climate Orbiter wasdesigned to gather amazing data to help scientists better understand the universe.However, none of that amazing data was gathered from the $125 million venturebecause groups working across the globe failed to operate under similar units ofmeasure. Specifically, the American units of measurement used in constructionhad to be converted to metric units for operation. Core information managementprinciples could have helped alleviate this risk by documenting the data defini-tions and outlining use of that data throughout the data’s lifecycle.

Maintaining Industry Standards for Data

Some external standards apply to entire industries. For example, global standards(GS1 standards) aim to help companies exchange information in the same format,thereby increasing the efficiency and visibility of supply and demand chains glo-bally.2 To participate, however, you not only need to understand the relevant GS1standard, but you must also fully understand your data, the data model, and cur-rent data quality levels. Without this baseline understanding, your use of GS1would be flawed at best, and you would miss golden optimization opportunities.

1.4 Impact of Big Data on EIM

It’s well documented that the volume of data created in organizations is large andgrowing at an unprecedented velocity. Organizational datastores are now com-monly measured in terabytes or even petabytes. There are many reasons for theunparalleled growth in datastores: social media, compliance and regulatoryrequirements, transactional data, sensory data (such as data from real-time shopfloor sensors), multimedia content, mobile devices, RFID-enabled devices, the“internet of things” (connected devices), the never-ending quest to improve orga-nizational effectiveness, and the list goes on. The fact is that data creation hasbecome a by-product of nearly all individual and organizational activities.

Moreover, the reason data is preserved and reused is that it has value well beyondits original use. We dare to say that the value of the data created to automate busi-ness processes may in some cases be greater than the process itself. Today, the

2 Source: http://www.GS1.org/about/overview


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market has christened the phenomena of organizations’ desire to harness thegreat torrent of data, as well as the velocity, variety, and variability of informationknown as big data. Figure 1.6 is a representation of the volume, velocity, variety,and variability of data. It remains to be seen if the term big data will stick. How-ever, as long as organizations can create value through data, the continued growthand importance of data will be immutable. Fortunately, advancements in compu-tational power, storage capacity, information access and management, and analyt-ics are progressing at an equally impressive rate. Two such advancements areHadoop and SAP HANA (to be discussed further in Chapter 3). The combinationof massively greater amounts of data with the tools and talent to analyze it prom-ises to launch the next wave of innovation and productivity and even spawn newbusiness models.

Figure 1.6 Information Growing in Volume, Velocity, Variety, and Variability

One example of new innovation provided by the ability to manage and analyzebig data is in the healthcare industry, specifically related to the area of cancer

CRM DataG

PS

DemandSp

eed

Velocity

Transactions

Opp

ortu

niti

es

Service Calls

Customer

Sales Orders

Inventory

Emai

ls

Tweets

Planning

Things

Mobile

Instant Messages


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treatment. The human genome contains 6 billion DNA base pairs; as the genomesequence for each patient will be decrypted in the near future, these billions ofdata points must be managed. Add to that documentation and features such asspeech recognition, and you’ll end up with 20 terabytes per patient.

The velocity of data collection is building daily, and you must manage and makesense of your data on the fly. You need to remain flexible through instability andchange. You can’t underestimate the pace of innovation, and you don’t want to beplaying catch-up with your competitors. If planning and implementing a coherentdata management strategy seems daunting when your organization owns a fewterabytes of data, how difficult will it be when you own thousands of terabytes?

The best way to realize the promise of big data, today and in the future, is todevelop and adopt an EIM strategy. This strategy should cover your entire enter-prise to take advantage of the benefits of sharing information and aggregatingdata across your organization. Typical topics that must be considered for aneffective EIM strategy include interoperable data models, architectures for ana-lytical and transactional data, integration architecture, analytical architecture,and information security and compliance. The goal is to have data that is share-able and can be leveraged over time within and across business units. Thedeployment of SAP solutions for EIM within a defined EIM strategy is a key start-ing point. The alternative is to have massive amounts of disintegrated and unre-liable data analyzed fast.

“Garbage in, garbage out” is one of the oldest adages in information processing;when the volume of data reaches the big data stage, getting productive use ofpoorly managed information becomes the equivalent of searching for a pricelessantique in a landfill.

1.5 SAP’s Strategy for EIM

SAP recognizes the importance of maximizing the value of enterprise informationin support of any data-driven analytical, operational, or governance initiatives. Toachieve this, organizations need a comprehensive suite of solutions providing thecapabilities from architect to archive. Figure 1.7 shows SAP solutions for EIM.

SAP solutions for EIM are comprehensive in functionality, including capabilities tosupport enterprise architecture, data integration, data quality, master data manage-ment, enterprise content management, and information lifecycle management.


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This chapter introduces SAP PowerDesigner as a modeling and design-time metadata management platform for information management designs.

7 SAP PowerDesigner

All enterprises today are or will be faced with a transformative event, such as reg-ulation changes, merger and acquisition activity, or enablement of new businessmodels from new technologies (e.g., cloud and in-memory). You need to be ableto treat information as a corporate asset to succeed with such business transfor-mation. This chapter focuses on the discipline of enterprise information architec-ture (EIA) as part of SAP Enterprise Information Management (EIM), and howtools such as SAP PowerDesigner, a modeling and design-time metadata manage-ment platform, enable you to understand your current information landscape,align business information with technical implementation, and plan for change.

Architecture is about planning for, designing, and executing change. SAP PowerDe-signer (hereafter PowerDesigner)’s value is best realized when we use the currentstate information models, captured and documented in the tool, to help us plan thenext generation business. Transformation needs a plan, and designing future stateversions of data models, aligned to the current conceptual data model (CDM) andbusiness glossary, ensures we make a united step forward in any step along the way.

Adding technical details in logical data models (LDMs) and physical data models(PDMs), together with specialized analytics models, ensures that we can commu-nicate details to the responsible database development teams. PowerDesigner’sunique Link and Sync technology streamlines impact analysis and design-timechange management, reducing the time, cost, and risk associated with change.

In this chapter, we’ll explore enterprise information architecture, including thedifferent model types, the core components of each, and how they work togetherto make a complete view of information for designers. This chapter will alsocover how the repository helps with tasks such as managing model-to-modeldependencies and impact analysis. You’ll learn the value that architecting, or


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planning, provides to all organizations that are faced with managing complexchange in information systems.

7.1 SAP PowerDesigner in the SAP Landscape

PowerDesigner provides architecture and modeling capabilities to all organiza-tions and is uniquely integrated into many SAP products. PowerDesigner is inte-grated with SAP Business Suite and the SAP HANA Cloud Platform (HCP). Withinthe EIM landscape, PowerDesigner is integrated with SAP Information Steward(hereafter Information Steward), SAP BusinessObjects, and SAP ReplicationServer (hereafter Replication Server). PowerDesigner is also a key element ofIntelligent Business Operations powered by SAP.

7.1.1 SAP Business Suite

PowerDesigner can connect to the SAP Business Suite and create a PDM repre-senting the data dictionary by reading the business and technical metadata fromSAP Business Suite. This is very useful when looking at SAP Business Suite as thestandard definition for any homemade applications built around common datasets, or for when preparing for an enterprise data warehouse and extracting datafrom SAP Business Suite to populate the warehouse as one of the key sources.

7.1.2 SAP HANA Cloud Platform

SAP HANA has a repository that’s used for the development and implementationof data structures that is optimized for helping developers get the most out of SAPHANA’s unique in-memory capability. PowerDesigner can write to the SAPHANA repository or read from it. Reading the SAP HANA repository creates orupdates a PDM in PowerDesigner. PowerDesigner can also take a PDM thatincludes SAP HANA-specific attribute and analytic views and create new, ormerge with existing, repository objects.

7.1.3 SAP Information Steward, SAP BusinessObjects Universes, and Replication

PowerDesigner’s repository is read by Information Steward, enabling people toread metadata from PowerDesigner’s PDMs, LDMs, and CDMs. This allows all the


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known metadata, both operational and architectural, to be visible to the datasteward as he manages the quality of information sources in operation.

PowerDesigner’s dimensional diagram can create SAP BusinessObjects universes.PowerDesigner can read a universe and create a new, or merge with an existing,dimensional diagram.

PowerDesigner can reverse engineer Replication Server’s catalog to create ormerge with an existing data movement model. This data movement model cangenerate new replication definitions. Special patterns exist to streamline use casesof replication and SAP Data Services (Data Services) together to implement real-time loading and other scenarios.

7.2 Defining and Describing Business Information with the Enterprise Glossary

An enterprise glossary helps everyone define and describe information assets andrelated technology. It lists business terms in business language, independent ofany data characteristics. One term can relate to multiple data items (atomic dataelements), and a data item can have multiple terms associated with it.

In PowerDesigner, the enterprise glossary is a global service provided by therepository that is available to all users. It contains all terms, synonyms, andrelated terms, grouped by nested term categories. A glossary term identifies theterm (Name) and provides a standard abbreviation for the term (Code) and a def-inition (Description). The glossary term will be created within a category folder(Category) and may also be further defined in an external system and referencedvia a URL (Reference URL). As you can see in Figure 7.1 in the next subsection, thebusiness term “commission” is defined, and every time the word commissionappears in the design (such as a table or column name), the standard abbreviation

Example

NeedsEIM Inc. defines its information model to have a customer entity that can have acustomer address attribute, which is combining the terms customer and address togetherto make up its name.


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of “CMSN” will be used in the name. You can also see that this term is approvedin the Status box, so you know it’s the right definition for this term.

PowerDesigner’s glossary is meant to be a direct reflection of the business glos-sary in Information Steward. Information Steward is used to capture, define, andmanage the glossary terms and relate them to the metadata of operational sys-tems, while in PowerDesigner, the same terms can be imported and then used tostandardize names for all new information assets that are defined in any model.

7.2.1 Glossary Terms for Naming Standards Enforcement

Using a common business language ensures that when users collaborate acrossbusiness units, or outside the company, they’re all using the same concepts in thesame way. This is a critical part of establishing enterprise information architectureand a key component of any data dictionary. The enterprise glossary (see Figure7.1) can be used to manage naming standards for all design models in PowerDe-signer. The Name field is used for name lookup, and any name that matches a termis linked to that term. If there are any aliases associated, when you begin to typethe alias, PowerDesigner detects the use of an alias and indicates that there is apreferred term to use in lieu of the alias. This helps establish the enterprise use ofthe preferred term and further increases understandability and readability of allmodels as everyone will be using the standard terms.

Figure 7.1 A Glossary Term in SAP PowerDesigner


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7.2.2 Naming Standards Definitions

PowerDesigner can be configured to use the glossary to ensure all names usedthroughout a model are found within the list of terms. To configure PowerDe-signer to use the enterprise glossary, follow these steps:

1. Select Tools � Model Options, and then select Naming Convention.

2. Check Enable glossary for autocompletion and compliance checking.

3. Select the Name to Code tab, and set Conversion Table to glossary terms.

You can combine multiple terms into one name (e.g., “Customer Address” usingterms “Customer” and “Address”).

You can also enable automatic conversions of names to implementation conceptCode values. In PowerDesigner, the Name field is the business language descrip-tor, while the Code field represents the name used for the object when convertedinto any sort of implementation code (e.g., when used in a CREATE TABLE state-ment).

7.3 The Conceptual Data Model

PowerDesigner supports the definition of a CDM. For an organization to treatinformation as a corporate asset, all information sources should be derived froma common definition, or a core concept. A CDM is meant to model a single defi-nition of any data asset, independent of both the storage paradigm (relational,hierarchical) and the physical characteristics of the systems that will ultimatelystore them.

The enterprise CDM also represents the sum of all use cases for a given data con-cept. Any entity defined in the enterprise CDM will have all the attributes neededfor all processes or all applications. For example, the enterprise CDM entity forcustomer will have all attributes together, whether used for order, relationship,support management, and more; while LDMs and PDMs that represent the indi-vidual systems will have their own subset of these attributes. This will help ensurethat any attributes that are shared between implementations follow a commonstandard and will reduce the impedance mismatches found when you later needto integrate these data sets together.


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Let’s review the core components of an enterprise CDM by looking at elements,attributes, data items, and domains in the following sections.

7.3.1 Conceptual Data Elements, Attributes, and Data Items

PowerDesigner manages enterprise CDM concepts such as entities, attributes,data items, and domains. These four concepts make up the core of the CDM, andwe’ll discuss them in more detail in the following subsections.

Entities

Entities are structured elements that define a core business concept that you need tokeep account of, such as product, customer, or delivery. Anything the business as awhole needs to account for and keep records of should be represented by an entityin the CDM. A CDM’s entity should represent a single global view of all possibleattributes that the concept may need for any given use case or business process.

Attributes and Data Items

In PowerDesigner, entity attributes and data items are separate but tightly relatedconcepts. Data items in PowerDesigner represent a unique data cell—a singlevalue of a specific type for a specific purpose. Examples of data items are Cus-tomer Name, Delivery Date, Product Description, or Phone Number.

Because data items exist independent of the entity attributes they represent, youcan use them as a data dictionary, or list of all atomic data managed in the enter-prise. This list of data items, or the data dictionary, is useful to communicate withthe data stewards to ensure you have the right definition for the data independentof any use in an entity or any physical implementation in a database.

Entity attributes are a relationship, or link, between an entity and a data item. Forexample, when the Customer entity is related to the Customer Name data item,the Customer entity will have an attribute called Customer Name. Any changesmade to the data Item will be reflected in the attribute as well.

Domains

Domains provide another level of data standardization. A domain is a named set ofcommon data characteristics for any number of data items (and therefore all



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attributes using that data item). For example, a domain called Name can define thedata type, length, and other common characteristics of any name type of data itemin the model. Anything using “Name” (e.g., Product Name, Customer Name, orCompany Name) that is also using the Name domain will share this common char-acteristic. The key difference between a domain and a data item in PowerDesigneris that the data item is a direct representation of an attribute on one or more enti-ties and carries a name representing a cell of information, while the domain is acommon set of data characteristics used by one or more data items and doesn’trepresent a cell of information itself, just its common structural characteristics.

7.3.2 Separation of Domains, Data Items, and Entity Attributes

The key advantages to this separation of entities, data items, and domains are free-dom of expression and improved standardization.

Domains standardize common data characteristics for any information you needto manage for the business, regardless of what you call it. This ensures a consistentuse of data structures for all attributes that are of a common concept, such asmoney, name, or phone number. When data items follow a common standarddomain like this, comparing and integrating data is a lot easier. You won’t need tocreate complex transformation code to make the two different data elementsmatch in form and structure, so you can get right to comparing values.

7.3.3 Entity Relationships

The enterprise CDM would not be complete without the relationships that aredefined between the entities. The CDM is essentially an Entity-Relationship Dia-gram (ERD). The relationships between the entities complete the understandingof the business data the CDM represents. There are two major types of relation-ships in the CDM: the ones that represent how two entities are connected to eachother, and the ones that represent entities that are, in essence, a specialization ofanother.

Relationships that represent the connections between two entities carry cardinal-ity; that is, the frequency of the instances of each side. You can define relationshipsof cardinality types zero- or one-to-many, many-to-many, and one-to-one (seeFigure 7.2, showing a one-to-many between Customer and Order and a many-to-many between Items and Order). Relationships representing a supertype/subtype,


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also known as an “is-a” relationship, may also be defined in the CDM using theinheritance object. When you define an inheritance, or “is-a” relationship, allattributes of the parent are available attributes of each child.

To define a relationship in PowerDesigner, use the Relationship tool from thetool palette. Follow these steps:

1. Select the Relationship tool, click on one of the entities, and drag to the secondentity to link.

2. To change the cardinality settings, double-click on the relationship line, andyou can change the following:

� Cardinalities, One to Many, Many to Many, or One to One

� The Role name (in both directions) to label the relationship, typically with averb

� Mandatory (on each end), determining whether a parent can exist withoutany children or not, and whether a child can exist without a parent, or not

Figure 7.2 An Example CDM

7.3.4 Best Practices for Building and Maintaining an Enterprise CDM

Business details are discovered over time, not all at once. The definitions of busi-ness terms evolve as the business evolves. New terms are discovered, old termsobsoleted, and existing terms redefined. In the following subsections, we’ll dis-cuss what to keep in mind when defining an enterprise CDM.

Customer

IDSurnameGivenName...

<pi>

...

Employee

Employee IdentifierEmployee nameEmployee Description

<pi><ai><ai>

Shipper

Salaries

Sales

SalariesCommission

Order

OrderIDDescription

<pi>

Is A

Stock Clerk

Hourly Rate



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Version Terms and the Enterprise CDM

Different versions of the enterprise CDM will be attributed to different projects atdifferent stages in their lifecycle. You can do this in PowerDesigner by setting upa configuration in the repository. Configurations are defined in the Repository

menu, under Configurations. You can create a new configuration and then addspecific model versions to it from a select list. Using a PowerDesigner configura-tion, you can indicate which specific versions of the enterprise CDM are related towhich versions of the logical and physical models representing projects andimplemented systems.

Don’t Overload a Single Concept

Let each data item represent a single concept. For example, break address con-cepts into their lowest levels of detail (street number, street name, city, etc.). Youdo this manually in PowerDesigner by creating additional data items for the moregranular elements and removing the complex one. This way, the language that’sused to identify the data item and the meaning of the information it representswill be crisp and clear.

Keep Definitions Granular

If you need too many examples and too many sentences to describe a single busi-ness information concept, then it may be too complex for a single entity or dataitem to represent it. You should consider simplifying the concept to a commondenominator or finding some way to separate it into multiple discrete concepts.In PowerDesigner, you simply create additional entities and attributes to definethese more granular concepts.

Use Synonyms Where Possible

Make sure a common concept shares a common language. Assign synonyms to acommon term in the enterprise glossary so that the preferred term is alwaysknown. You do this in PowerDesigner by double-clicking the term in the glossarybrowser and selecting the Synonyms tab. Any word you enter in the Synonyms listwill be an alternate term defining the same concept as the term itself (now knownas the preferred term). This way, you don’t confuse a different name as somethingwith a completely different concept.


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Keep Obsolete Concepts

If you have a concept that’s no longer needed, it’s better to leave the definition inthe enterprise CDM, marked as obsolete. You can do this in PowerDesigner byunchecking the Generate checkbox, which prevents the concept from movingforward into LDMs and PDMs. This way, any new concepts that are similar won’treuse the old terms and entities, but create new ones. This ensures that there willbe no future confusion with older systems using the original definition of thatconcept.

Don’t Redefine and Reuse

This complements the idea that you should keep obsolete concepts around. Ifsomething has really changed enough that the definition of the concept deviatesfrom the original idea, then a new term, new data item, or new entity should bedefined, and the original one should be kept around for legacy reasons. In Power-Designer, you can mark the old term as Legacy in the Stereotype field, anduncheck the Generate checkbox. A good test of this is whether the original con-cept fits within the new definition, or whether the data sets managed by the con-cept would have to be deliberately segregated to keep them understood.

7.4 Detailing Information Systems with Logical and Physical Data Models

The PowerDesigner LDM and PDM represent the Relational Database Manage-ment Systems (RDBMSs) that implement the data concepts from the enterpriseCDM. These models differ fundamentally from the enterprise CDM in three keyways: scope, structure, and technical considerations.

7.4.1 Scope

LDMs and PDMs are slivers of the enterprise, representing a specific subset of theconcept to be implemented. These models represent a given functional area of thebusiness and their one or more physical databases. While the enterprise CDM hasa single “namespace”—a name can only be used once for the entire enterpriseCDM—the logical and physical layers allow for multiple namespaces, each oneconstrained by a given system boundary.


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LDMs and PDMs design information structures within a given storage paradigm.When targeting an RDBMS, the LDM represents the relational structures andincludes relational concepts such as migrated foreign keys. The PDM adds thevendor- and version-specific RDBMS details such as physical data types, triggersand procedures, and more. Other types of LDMs exist, such as a hierarchical rep-resentation in canonical data models (XML structures) or an object-oriented rep-resentation targeting object-oriented systems design.

7.4.2 Structure and Technical Considerations

LDMs and PDMs contain structure definitions that have nothing to do with busi-ness data definitions, and everything to do with technical considerations for imple-mentation. As shown in Figure 7.3, details such as foreign keys to define how rela-tionships will be stored, or link entities storing the keys of many-to-manyrelationships are foreign to the business; they have no meaning when trying tounderstand a business concept. PDMs may involve denormalizing; for example,combining multiple tables or duplicating columns in more than one table to reducethe number of joins needed in a query and improve application performance.

The LDM helps us prepare for physical implementation, and represents the datastructures for a given functional area. It may represent multiple databases, frommultiple vendor/version RDBMSs. The PDM is an abstraction from the actualdetails of a physical implementation and is useful for application designers anddevelopers to know what information is available. The PDM is there to developthe actual database and adds details such as indexes, views, referential integrityconstraints, triggers, stored procedures, and more.

Each PDM is tightly related to a specific relational database vendor and versionand is intended to be a 1:1 representation of the actual physical database. ThePDM can be created by reverse engineering an existing running database. AnyPDM can be used to generate new Data Definition Language (DDL) files to create

Example

In identifying and managing customer metadata, NeedsEIM Inc. creates an entity for thecustomer concept that has all attributes, including customer name, address, gender, agerange, income bracket, and more. The LDM for the order-to-cash functional area willonly take the name and address attributes. A completely separate LDM for customerrelationship management will take only the demographic attributes.


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a new database, or can be compared to an existing database to update using DDLand Data Movement Language (DML) to change the schema while keeping theexisting data in place.

Figure 7.3 Logical Data Model with Migrated Foreign Keys

7.5 Canonical Data Models, XML Structures, and Other Datastores

Enterprise information architecture goes beyond relational databases andincludes information in all structures within the enterprise. One common repre-sentation of information in nonrelational structures is the XML formatted mes-sages used to communicate between systems. XML Schema Definitions (XSDs)represent the messages and the message structure.

PowerDesigner has a special XML model, shown in Figure 7.4, that represents anXSD directly and can map that model to one or more PDMs to show where thedata in messages is read from or written to.

Order

Order NumberCustomer IDEmployee IdentifierShipper IdentifierSales Identifier

<pi><fi4><fi2><fi1><fi3>

StateIdentifierIdentifierIdentifierIdentifier

Description Long Text

Primary Identifier <pi>

Order Items

Item IDOrder ID

<pi,fi2><pi,fi1>

IdentifierState

Order Items Key <pi>

Customer AddressCustomer IDCustomer NameCustomer Phone

Customer Key <pi>

Customer

AddressIdentifierNamePhone

<pi>

Items

Item IDDescription

<pi> IdentifierLong Text

Primary Identifier <pi>


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Figure 7.4 XML Model in SAP PowerDesigner showing complex type reuse

Many organizations have worked to standardize the structures of message formatsby using a Canonical Data Model, which is an XML model that gathers all the ele-ments of all the messages together and creates a series of XML complex types todefine commonly reused data structures. This Canonical Data Model is a sort ofdata dictionary for the messages themselves.

In PowerDesigner, mappings can be created between the complex type defini-tions and the data model representing how message content can be stored in oneor more physical databases (see Figure 7.5).

Figure 7.5 XML Model Mappings with a PDM

Customer{Customer Type}

CustomerType

AddressIdentifier

NamePhone

ADDRESSIDENTIFIERNAMEPHONE

Client{Customer Type}

CustomerType

AddressIdentifier

NamePhone

ADDRESSIDENTIFIERNAMEPHONE


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Use the Mapping Editor from the Tools menu to define mappings. Then, createthe mapping definitions by dragging the data elements from the left droppingthem to the XML structures on the right.

In PowerDesigner, you can also create a library of commonly reused complextypes and then use shortcuts to reuse these in any number of XML models repre-senting different sets of messages. To do this, create a new XML model in Power-Designer, and either reverse engineer an existing XSD with the complex typesdefined, or use the palette to create new complex types in the model. When youcheck the model into the repository, click the Advanced button, and selectLibrary in the Folder option.

7.6 Data Warehouse Modeling: Movement and Reporting

When you start trying to define and describe the data warehouse and businessanalytics systems, you need to understand data in motion between source systemsand analytics stores. You also want to know the relationship between analyticssystems and the underlying data warehouse database. This helps ensure thatyou’ve identified the right data sources, that you can answer the business ques-tions needed to help in decision making, and that you know what parts of the sys-tem will be affected when changes happen to any given component of the envi-ronment.

PowerDesigner data mappings are captured using the Mapping Editor for easy,drag-and-drop identification of the dependencies between transactional systemsand analytics systems. Follow these steps:

1. Select Mapping Editor from the Tools menu. If this is the first time you’vestarted the Mapping Editor, you’ll be prompted to complete a wizard to iden-tify the sources for the mappings.

2. You may identify one or more PDMs to represent the source for the data ware-house or master datastore.

3. Create mappings by dragging a source data element (table or column) from theleft-hand side to the destination (table or column) on the right. You can also definemappings between an enterprise data warehouse and a series of data marts.

PowerDesigner table definitions allow you to mark mappings as a Fact or Dimen-

sion. To do this, go to the General tab, and select the option from the Dimensional


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type dropdown. At the physical table level, this helps report designers know whattables contain the different types information, which ones represents things thebusiness will measure, and the variables by which we partition them.

In PowerDesigner, you may select Multidimensional Objects, Retrieve Multidi-

mensional Objects from the Tools menu and automatically detect the dimensiontype based on key structures of each table. For tables that have a compound pri-mary key made up of foreign keys migrated from other tables, the logic deter-mines that it’s a likely fact table, and for all other key structures, the table is deter-mined to be a dimension.

Dimensional Modeling

In PowerDesigner, dimensional models represent the analytic reports themselves.The dimensional model is a graphical representation of fact and dimension objects.As shown in Figure 7.6, fact objects represent one or more fact tables comingtogether to make a single fact concept. Dimension objects represent the dimensiontables collapsed into a simpler representation, complete with multiple hierarchiesrepresenting drill-up and drill-down opportunities within the attributes.

Figure 7.6 Dimensional Model with Facts, Dimensions, and Hierarchies

Order

Measure

CustomerIDItemsIDDate IDProduct ID

Location

Location IDStateCityPostal/Zip Code

Time

Time_IDYearMonthDay

Time

<h:1><h:2><h:3><h:4>

<Default> <h>

Product

ItemsIDDescription

Customer

CustomerIDNameAddressPhone

Order—LocationOrder—Time

Order—Product Order—Customer


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These models are created either by selecting New Dimensional Diagram from thePDM’s context menu, or running the wizard from Tools � Multidimensional

Objects � Generate Cube.

7.7 Link and Sync for Impact Analysis and Change Management

PowerDesigner uses the dependencies that are tracked and managed betweenmodels to help facilitate impact analysis and change management. This is knownas PowerDesigner’s Link and Sync technology. This allows CDMs, LDMs, andPDMs to remain synchronized through iterations of change without requiringdesigners, architects, and developers to redo their work.

Link and Sync captures the cross-domain dependencies, such as data used by aprocess step or flow, or the applications that access certain data assets. You canshow all business tasks and all applications that interact with enterprise data.

In the following sections, we’ll discuss how PowerDesigner can be used to createlinks between any objects in any models, and how it automatically managesmodel-to-model synchronization through the model generation engine.

7.7.1 Link and Sync Technology

From the name, you see that Link and Sync has two parts: the Link part and theSync part.

Linking

Linking is when a modeler recognizes a dependency between any two things inPowerDesigner and creates the link. You can create links between any PowerDe-signer model, including models that aren’t directly used for data modeling butfound in information and enterprise architecture, such as the requirements model

Note

While it’s useful to mark tables as fact and dimension in order to identify where in thedatabase the structures for analytics systems will likely be finding information, it’s not adescription of a specific report.


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or the business process model. Linking between such models happens naturallyfor the most part; for example, attaching a list of data elements to a process.

When you define a CRUD matrix in a PowerDesigner Business Process Model(BPM) referencing data in a CDM, you’re creating links. When you create any typeof dependency by drawing a reference, relationship, or inheritance, you’re creat-ing a link. You can also create links by opening any object’s property sheet, goingto the Traceability Links tab, and clicking the New button to select any object inany model.

You also establish links when binding requirements to any object through therequirements traceability matrix. This is easily done in PowerDesigner by simplyopening the requirements traceability matrix, selecting any empty cell, and press-ing the (Space) bar. To remove a link, select a cell that contains a checkmark (iden-tifying the presence of a link), and press the (Space) bar. You can create dependen-cies between any two objects in PowerDesigner using the dependencies matrix,which looks and operates nearly identically to the requirements traceabilitymatrix, but can be established between any two objects, in the same or in differ-ent models. To create a new dependency matrix, simply select New Traceability

Matrix from the model’s pop-up menu in the object browser, and specify theobject types to use for the rows and columns. You can also select which attributewill be used to identify the link, if more than one way to combine these objects ispossible (e.g., reference or inheritance on an entity in a CDM).

Synching

The synchronizing part in PowerDesigner Link and Sync is when one model isgenerated from another. PowerDesigner keeps track of the transformed objectsand their source. When you generate a model from another (for example, whencreating a PDM from an LDM), the sync technology remembers everything. If youthen make changes to the original model, the second generation isn’t a new cre-ation of a new PDM, but a write into the existing one generated the first time.Sync technology publishes only the changes made in the LDM since the last gen-eration. This way, any changes made to the PDM in areas not affected by the LDMchange will be preserved.

To initiate a synch process, use the model generator from the Tools menu. Forexample, to synchronize an LDM to a PDM, open the LDM first, and select


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Generate Physical Data Model from the Tools menu. This initiates the synccompare and presents you the Compare/Merge dialog. After accepting thechanges you want to synchronize, PowerDesigner automatically applies them tothe selected PDM and opens the PDM model when complete.

PowerDesigner’s Merge Models dialog, shown in Figure 7.7, allows you to man-ually override any preserved changes if needed, simply by checking the emptycheckbox next to the detected difference. This is sometimes useful when imple-mentation starts to deviate too far from the original concept, and a reset in a pre-cise area is needed to get the database design back on track.

Figure 7.7 Compare/Merge Showing Preserved Differences

Synchronization ensures that models derived from each other remain aware ofeach other and that dependencies can be tracked at the smallest level. This Synctechnology makes it natural and easy for business analysts, technical analysts,architects, designers, and developers to remain in lockstep while managing con-tinuous change at any level of abstraction.


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7.7.2 Impact Analysis Reporting

The most important use case for keeping all these models linked and synchro-nized together is so that you can determine what will happen if you change any-thing. The Impact Analysis feature in PowerDesigner produces a list of impactedobjects with a tree-like structure. Filters and other tools help scope the analysis toareas of interest. To begin an impact analysis in PowerDesigner, follow thesesteps:

1. Either select Impact Analysis from the Tools menu or right-click on any objectin the browser or diagram area, and select Impact and lineage Analysis fromthe pop-up menu.

2. Generate a diagram view from the tree view by clicking the Generate Diagram

button on the Impact and Lineage Analysis dialog box, as shown in Figure 7.8.This diagram is very useful to collaborate with others in an easy-to-view format(see Figure 7.9).

Figure 7.8 SAP PowerDesigner Impact Analysis Dialog


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Figure 7.9 SAP PowerDesigner Impact Analysis Diagram

Impact analysis makes sure you won’t forget that certain dependencies exist andwill take them into consideration on each and every change request from businessor technical stakeholders. Downstream, you can see what objects will need to bechanged, tested, and verified based on this change. Because you know what data-bases, applications, and systems will be affected, you can get all the right peopleinvolved, and when the change is made to the operational systems, it’s done in away that minimizes any surprises and minimizes the risk of any unplanned down-time.

7.8 Comparing Models

Modeling is a great way to communicate and collaborate with different people onany complex project. To communicate effectively, it’s not always practical to opena modeling tool, navigate through multiple models, and read screens. To helpshare information in any model, PowerDesigner has ways to analyze and reporton that information and then share it with all nonmodelers in the enterprise.

(Order Management Relational Logical Data Model)Entity CUST

(Order Management Oracle 11g Data Model)Table Customer

(Corporate Conceptual Data Model)Entity Customer

(Order Management MS SQL Data Model)Table Customer

(Order Management MS SQL 2008 Data Model)View V_Orders

(Order Management Process BPMN Descriptive)Data Customer

(Order Management Process BPMN Descriptive)Sequence Flow Process Corporate Order

(Order Management Process BPMN Descriptive)Sequence Flow Process Ship Ground Service

(Order Management Process BPMN Descriptive)Sequence Flow Create Order

(Order Management Process BPMN Descriptive)Data Access Ship Local Postal Service Ground.Customer

(Order Management Process BPMN Descriptive)Data Access Process Order.Customer

(Order Management Process BPMN Descriptive)Data Access Process Corporate Order.Customer


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Model comparison is used whenever changes are made to a model and the modelis checked into the repository. To initiate a compare in PowerDesigner, open themodel you want to compare, and select Tools � Compare from the menu. Youmust choose the other model to compare this one to, and select OK to run thecomparison.

Figure 7.10 shows a typical Compare Models dialog for two CDMs. This compari-sons feature is also used when generating changes from one model into anotherwhen using Preserve Modifications. Compare Between can also be run at any time.

Figure 7.10 SAP PowerDesigner Compare Dialog

Model comparison is useful for several reasons. It’s a great way to see if there areany similarities in models from completely different sources. It’s also a great wayto see what changes are made between two different versions of a model, or forunderstanding the gap between current and desired future state.


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Options allow you to narrow the scope of the compare by excluding comments,data types, or other elements. We may force a compare between two objects thatwere not found to be the same by using the Manual Synchronization function.

Yellow and red flags indicate differences, bold and grayed out indicate presenceand absence of whole objects, and the detailed compare window at the bottomshows the exact difference. The compare preview allows you to save the compar-ison as a Microsoft Excel spreadsheet for further analysis.

7.9 Summary

In this chapter, you learned that using PowerDesigner as an integral part of the SAPEIM solution gives you the power to successfully navigate the pitfalls of businesstransformation. PowerDesigner provides the right tools to manage information asa corporate asset today and into the future. PowerDesigner’s unique integrationinto the SAP landscape means designs in the models can easily translate directly tophysical artifacts in databases, data movement, and reporting technologies.

In the next chapter, we’ll discuss SAP HANA Cloud Integration capabilities to con-nect databases and applications on-premise and in the cloud.


7

Contents

Introduction ..................................................................................................... 17

PART I SAP’s Enterprise Information Management Strategy and Portfolio

1 Introducing Enterprise Information Management ................... 25

1.1 Defining Enterprise Information Management ............................... 251.1.1 Example of Information Flow through a Company ............ 281.1.2 Types of Information Included in Enterprise

Information Management ................................................. 311.2 Common Use Cases for EIM .......................................................... 33

1.2.1 EIM for Operational Initiatives ......................................... 331.2.2 EIM for Analytical Use Cases ............................................ 351.2.3 EIM for Information Governance ...................................... 36

1.3 Common Drivers for EIM ............................................................... 361.3.1 Operational Efficiency as a Driver of EIM .......................... 371.3.2 Information as an Organizational Asset ............................ 391.3.3 Compliance as a Driver of EIM ......................................... 40

1.4 Impact of Big Data on EIM ............................................................ 411.5 SAP’s Strategy for EIM ................................................................... 431.6 Typical User Roles in EIM .............................................................. 441.7 Example Company: NeedsEIM Inc. ................................................ 45

1.7.1 CFO Issues ....................................................................... 461.7.2 Purchasing Issues ............................................................. 471.7.3 Sales Issues ...................................................................... 471.7.4 Engineering and Contracts Issues ...................................... 471.7.5 Information Management Challenges

Facing NeedsEIM Inc. ...................................................... 471.8 Summary ....................................................................................... 48

2 Introducing Information Governance ........................................ 49

2.1 Introduction to Information Governance ....................................... 502.2 Evaluating and Developing Your Information Governance Needs

and Resources ............................................................................... 522.2.1 Evaluating Information Governance .................................. 532.2.2 Developing Information Governance ................................ 58


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2.3 Optimizing Existing Infrastructure and Resources ........................... 592.4 Establishing an Information Governance Process: Examples ........... 60

2.4.1 Example 1: Creating a New Reseller ................................. 622.4.2 Example 2: Supplier Registration ...................................... 632.4.3 Example 3: Data Migration ............................................... 66

2.5 Rounding Out Your Information Governance Process .................... 702.5.1 The Impact of Missing Data .............................................. 702.5.2 Gathering Metrics and KPIs to Show Success .................... 722.5.3 Establish a Before-and-After View .................................... 76

2.6 Summary ....................................................................................... 76

3 Big Data with SAP HANA, Hadoop, and EIM ........................... 77

3.1 SAP HANA .................................................................................... 773.1.1 Business Benefits of SAP HANA ........................................ 783.1.2 Basics of SAP HANA ......................................................... 813.1.3 SAP HANA Components and Architecture ........................ 823.1.4 SAP HANA for Analytics and Business Intelligence ............ 853.1.5 SAP HANA as an Application Platform .............................. 863.1.6 SAP Business Suite on SAP HANA .................................... 863.1.7 SAP HANA and the Cloud ................................................ 87

3.2 SAP HANA and EIM ...................................................................... 893.2.1 Data Modeling for SAP HANA .......................................... 893.2.2 Data Provisioning for SAP HANA ...................................... 893.2.3 Data Quality for SAP HANA ............................................. 94

3.3 Big Data and Hadoop .................................................................... 963.3.1 The Rise of Hadoop .......................................................... 963.3.2 Introduction to Hadoop ................................................... 983.3.3 Hadoop 2.0 Architecture: HDFS, YARN,

and MapReduce ............................................................... 993.3.4 Hadoop Ecosystem ........................................................... 1013.3.5 Enterprise Use Cases ........................................................ 1053.3.6 Hadoop in the Enterprise: The Bottom Line ...................... 107

3.4 SAP HANA and Hadoop ................................................................ 1093.4.1 The V’s: Volume, Variety, Velocity ................................... 1093.4.2 SAP HANA: Designed for Enterprises ................................ 1093.4.3 Hadoop as an SAP HANA Extension ................................. 109

3.5 EIM and Hadoop ........................................................................... 1103.5.1 ETL: Data Services and the Information Design Tool ......... 1113.5.2 Unsupported: Information Governance and Information

Lifecycle Management ...................................................... 1113.6 Summary ....................................................................................... 112


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4 SAP’s Solutions for Enterprise Information Management ....... 113

4.1 SAP PowerDesigner ....................................................................... 1154.2 SAP HANA Cloud Integration ........................................................ 118

4.2.1 SAP HANA Cloud Integration for Process Integration ....... 1194.2.2 SAP HANA Cloud Integration for Data Services ................ 120

4.3 SAP Data Services .......................................................................... 1204.3.1 Basics of SAP Data Services .............................................. 1214.3.2 SAP Data Services Integration with SAP Applications ....... 1234.3.3 SAP Data Services Integration with

Non-SAP Applications ...................................................... 1274.3.4 Data Cleansing and Data Validation with

SAP Data Services ............................................................ 1284.3.5 Text Data Processing in SAP Data Services ........................ 130

4.4 SAP Replication Server .................................................................. 1334.4.1 SAP Replication Server Use Cases ..................................... 1334.4.2 Basics of SAP Replication Server ....................................... 1344.4.3 Data Assurance ................................................................ 1364.4.4 SAP Replication Server Integration with SAP

Data Services and SAP PowerDesigner ............................. 1364.5 SAP Data Quality Management, Version for SAP Solutions ............ 1374.6 SAP Information Steward ............................................................... 139

4.6.1 Data Profiling and Data Quality Monitoring ..................... 1414.6.2 Cleansing Rules ................................................................ 1434.6.3 Match Review .................................................................. 1464.6.4 Metadata Analysis ............................................................ 1474.6.5 Business Term Glossary .................................................... 148

4.7 SAP NetWeaver Master Data Management and SAP Master Data Governance ........................................................................... 1494.7.1 SAP NetWeaver Master Data Management ...................... 1504.7.2 SAP Master Data Governance ........................................... 151

4.8 SAP Solutions for Enterprise Content Management ........................ 1544.8.1 Overview of SAP’s ECM Solutions .................................... 1564.8.2 SAP Extended Enterprise Content Management

by OpenText .................................................................... 1604.8.3 SAP Document Access by OpenText and

SAP Archiving by OpenText .............................................. 1644.9 SAP Information Lifecycle Management ......................................... 165

4.9.1 Retention Management .................................................... 1694.9.2 System Decommissioning ................................................. 170


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4.10 Information Governance in SAP ..................................................... 1734.10.1 Information Governance Use Scenario Phasing ................. 1744.10.2 Technology Enablers for Information Governance ............. 176

4.11 NeedsEIM Inc. and SAP’s Solutions for EIM ................................... 1794.12 Summary ....................................................................................... 181

5 Rapid-Deployment Solutions for Enterprise Information Management ............................................................................. 183

5.1 Rapid-Deployment Solutions for Data Migration ........................... 1845.1.1 Introduction to Data Migration ........................................ 1855.1.2 Data Migration Rapid-Deployment Content ..................... 1875.1.3 Getting Started with Rapid Data Migration

Rapid-Deployment Content .............................................. 1895.1.4 SAP Accelerator for Data Migration by

BackOffice Associates ....................................................... 1965.2 Rapid-Deployment Solutions for Information Steward ................... 197

5.2.1 Information Steward Rapid-Deployment Solution Content ............................................................................ 198

5.2.2 Getting Started with Information Steward Rapid-Deployment Solution Content ................................ 201

5.3 Rapid-Deployment Solutions for Master Data Governance ............. 2035.3.1 Master Data Governance Rapid-Deployment

Solution Content .............................................................. 2045.3.2 Getting Started with SAP Master Data Governance

Rapid-Deployment Solution Content ................................ 2065.4 Summary ....................................................................................... 207

6 Practical Examples of EIM ........................................................ 209

6.1 EIM Architecture Recommendations and Experiences by Procter and Gamble ....................................................................... 2096.1.1 Principles of an EIM Architecture ..................................... 2106.1.2 Scope of an EIM Enterprise Architecture .......................... 2126.1.3 Structured Data ................................................................ 2136.1.4 The Dual Database Approach ........................................... 2146.1.5 Typical Information Lifecycle ............................................ 2166.1.6 Data Standards ................................................................. 2206.1.7 Unstructured Data ............................................................ 2216.1.8 Governance ...................................................................... 223


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6.1.9 Role of the Enterprise Information Architecture Organization .................................................................... 228

6.2 Managing Data Migration Projects to Support Mergers and Acquisitions ................................................................................... 2286.2.1 Scoping for a Data Migration Project ................................ 2296.2.2 Data Migration Process Flow ............................................ 2316.2.3 Enrich the Data Using Dun and Bradstreet (D&B) with

Data Services .................................................................... 2366.3 Evolution of SAP Data Services at National Vision ......................... 236

6.3.1 Phase 1: The Enterprise Data Warehouse ......................... 2366.3.2 Phase 2: Enterprise Information Architecture—

Consolidating Source Data ............................................... 2386.3.3 Phase 3: Data Quality and the Customer Hub ................... 2396.3.4 Phase 4: Application Integration and Data Migration ....... 2426.3.5 Phase 5: Next Steps with Data Services ............................ 242

6.4 Recommendations for a Master Data Program ............................... 2436.4.1 Common Enterprise Vision and Goals ............................... 2436.4.2 Master Data Strategy ........................................................ 2436.4.3 Roadmap and Operational Phases .................................... 2446.4.4 Business Process Redesign and Change Management ....... 2446.4.5 Governance ...................................................................... 2446.4.6 Technology Selection ....................................................... 245

6.5 Recommendations for Using SAP Process Integration and SAP Data Services .......................................................................... 2466.5.1 A Common Data Integration Problem .............................. 2466.5.2 A Data Integration Analogy .............................................. 2476.5.3 Creating Prescriptive Guidance to Help Choose

the Proper Tool ................................................................ 2486.5.4 Complex Examples in the Enterprise ................................. 2496.5.5 When All Else Fails… ....................................................... 250

6.6 Ensuring a Successful Enterprise Content Management Project by Belgian Railways ........................................................... 2516.6.1 Building the Business Case ............................................... 2516.6.2 Key Success Factors for Your SAP Extended Enterprise

Content Management by OpenText Project ...................... 2576.7 Recommendations for Creating an Archiving Strategy .................... 261

6.7.1 What Drives a Company into Starting a Data Archiving Project? ............................................................ 261

6.7.2 Who Initiates a Data Archiving Project? ........................... 2626.7.3 Project Sponsorship .......................................................... 263

6.8 Summary ....................................................................................... 266


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PART II Working with SAP’s Enterprise Information Management Solutions

7 SAP PowerDesigner ................................................................... 269

7.1 SAP PowerDesigner in the SAP Landscape ..................................... 2707.1.1 SAP Business Suite ........................................................... 2707.1.2 SAP HANA Cloud Platform ............................................... 2707.1.3 SAP Information Steward, SAP BusinessObjects

Universes, and Replication ............................................... 2707.2 Defining and Describing Business Information with the

Enterprise Glossary ........................................................................ 2717.2.1 Glossary Terms for Naming Standards Enforcement .......... 2727.2.2 Naming Standards Definitions .......................................... 273

7.3 The Conceptual Data Model .......................................................... 2737.3.1 Conceptual Data Elements, Attributes, and Data Items ..... 2747.3.2 Separation of Domains, Data Items, and Entity

Attributes ......................................................................... 2757.3.3 Entity Relationships .......................................................... 2757.3.4 Best Practices for Building and Maintaining an

Enterprise CDM ............................................................... 2767.4 Detailing Information Systems with Logical and Physical

Data Models .................................................................................. 2787.4.1 Scope ............................................................................... 2787.4.2 Structure and Technical Considerations ............................ 279

7.5 Canonical Data Models, XML Structures, and Other Datastores ..... 2807.6 Data Warehouse Modeling: Movement and Reporting .................. 2827.7 Link and Sync for Impact Analysis and Change Management ......... 284

7.7.1 Link and Sync Technology ................................................ 2847.7.2 Impact Analysis Reporting ................................................ 287

7.8 Comparing Models ........................................................................ 2887.9 Summary ....................................................................................... 290

8 SAP HANA Cloud Integration .................................................... 291

8.1 SAP HANA Cloud Integration Architecture .................................... 2928.1.1 SAP HANA Cloud Platform ............................................... 2948.1.2 Customer Environment On-Premise .................................. 2948.1.3 SAP HANA Cloud Integration User Experience ................. 295

8.2 Getting Started with SAP HANA Cloud Integration ........................ 2978.2.1 Blueprinting Phase ........................................................... 2978.2.2 Predefined Templates ....................................................... 298


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8.2.3 Setting Up Your HCI Tenant ............................................. 2998.2.4 Setting Up Your Datastore ................................................ 3008.2.5 Creating a New Project .................................................... 3018.2.6 Moving a Task from a Sandbox to a Production

Environment .................................................................... 3048.3 Summary ....................................................................................... 305

9 SAP Data Services ..................................................................... 307

9.1 Data Integration Scenarios ............................................................. 3079.2 SAP Data Services Platform Architecture ........................................ 309

9.2.1 User Interface Tier ............................................................ 3109.2.2 Server Tier ........................................................................ 313

9.3 SAP Data Services Designer Overview ............................................ 3149.4 Creating Data Sources and Targets ................................................. 318

9.4.1 Connectivity Options for SAP Data Services ...................... 3189.4.2 Connecting to SAP ........................................................... 3219.4.3 Connecting to Hadoop ..................................................... 323

9.5 Creating Your First Job .................................................................. 3249.5.1 Create the Data Flow ....................................................... 3249.5.2 Add a Source to the Data Flow ......................................... 3259.5.3 Add a Query Transform to the Data Flow ......................... 3259.5.4 Add a Target to the Data Flow ......................................... 3259.5.5 Map the Source Data to the Target by Configuring

the Query Transform ........................................................ 3269.5.6 Create the Job and Add the Data Flow to the Job ............. 327

9.6 Basic Transformations Using the Query Transform and Functions ... 3279.7 Overview of Complex Transformations .......................................... 330

9.7.1 Platform Transformations ................................................. 3309.7.2 Data Integrator Transforms ............................................... 332

9.8 Executing and Debugging Your Job ............................................... 3369.9 Exposing a Real-Time Service ......................................................... 337

9.9.1 Create a Real-Time Job ..................................................... 3389.9.2 Create a Real-Time Service ............................................... 3409.9.3 Expose the Real-Time Service as a Web Service ................ 342

9.10 Data Quality Management ............................................................ 3439.10.1 Data Cleansing ................................................................. 3459.10.2 Data Enhancement ........................................................... 3669.10.3 Data Matching ................................................................. 3699.10.4 Using Data Quality beyond Customer Data ...................... 386

9.11 Text Data Processing ..................................................................... 3889.11.1 Introduction to Text Data Processing Capabilities in

SAP Data Services ............................................................ 389


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Contents

9.11.2 Entity Extraction Transform Overview ............................... 3919.11.3 How Extraction Works ...................................................... 3929.11.4 Text Data Processing and NeedsEIM Inc. .......................... 3949.11.5 NeedsEIM Inc. Pain Points ............................................... 3949.11.6 Using the Entity Extraction Transform ............................... 396

9.12 Summary ....................................................................................... 403

10 SAP Information Steward .......................................................... 405

10.1 Cataloging Data Assets and Their Relationships ............................. 40610.1.1 Configuring a Metadata Integrator Source ........................ 40710.1.2 Executing or Scheduling Execution of Metadata

Integration ....................................................................... 40910.2 Establishing a Business Term Glossary ............................................ 41010.3 Profiling Data ................................................................................ 413

10.3.1 Configuration and Setup of Connections and Projects ....... 41410.3.2 Getting Basic Statistical Information about the

Data Content ................................................................... 41710.3.3 Identifying Cross-Field or Cross-Column

Data Relationships ........................................................... 42210.4 Assessing the Quality of Your Data ................................................ 425

10.4.1 Defining Validation Rules Representing Business Requirements ................................................................... 427

10.4.2 Binding Rules to Data Sources for Data Quality Assessment ...................................................................... 431

10.4.3 Executing Rule Tasks and Viewing Results ........................ 43310.5 Monitoring with Data Quality Scorecards ...................................... 437

10.5.1 Components of a Data Quality Scorecard ......................... 43910.5.2 Defining and Setting Up a Data Quality Scorecard ............ 44110.5.3 Viewing the Data Quality Scorecard ................................. 44810.5.4 Identifying Data Quality Impact and Root Cause .............. 45210.5.5 Performing Business Value Analysis .................................. 454

10.6 Quick Starting Data Quality ........................................................... 46110.6.1 Assess the Data Using Column, Advanced, and

Content Type Profiling ...................................................... 46210.6.2 Receive Validation and Cleansing Rule

Recommendations ............................................................ 46210.6.3 Tune the Cleansing and Matching Rules

Using Data Cleansing Advisor ........................................... 46410.6.4 Publish the Cleansing Solution ......................................... 465

10.7 Summary ....................................................................................... 465


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11 SAP Master Data Governance ................................................... 467

11.1 SAP Master Data Governance Overview ........................................ 46811.1.1 Deployment Options ........................................................ 47011.1.2 Change Request and Staging ............................................ 47111.1.3 Process Flow in SAP Master Data Governance .................. 47311.1.4 Use of SAP HANA in SAP MDG ........................................ 475

11.2 Getting Started with SAP Master Data Governance ........................ 47611.2.1 Data Modeling ................................................................. 47611.2.2 User Interface Modeling ................................................... 47811.2.3 Data Quality and Search ................................................... 47811.2.4 Process Modeling ............................................................. 48011.2.5 Data Replication .............................................................. 48111.2.6 Key and Value Mapping ................................................... 48111.2.7 Data Transfer ................................................................... 48311.2.8 Activities beyond Customizing .......................................... 483

11.3 Governance for Custom-Defined Objects: Example ........................ 48411.3.1 Plan and Create Data Model ............................................ 48411.3.2 Define User Interface ....................................................... 48911.3.3 Create a Change Request Process ..................................... 49411.3.4 Assign Processors to the Workflow ................................... 49511.3.5 Test the New Airline Change Request User Interface ........ 496

11.4 Rules-Based Workflows in SAP Master Data Governance ............... 49711.4.1 Classic Workflow and Rules-Based Workflow Using

SAP Business Workflow and BRFplus ................................ 49811.4.2 Designing Your First Rules-Based Workflow in

SAP Master Data Governance ........................................... 50511.5 NeedsEIM Inc.: Master Data Remediation ..................................... 50811.6 Summary ....................................................................................... 511

12 SAP Information Lifecycle Management ................................... 513

12.1 The Basics of Information Lifecycle Management ........................... 51512.1.1 External Drivers ................................................................ 51612.1.2 Internal Drivers ................................................................ 516

12.2 Overview of SAP Information Lifecycle Management ..................... 51612.2.1 Cornerstones of SAP ILM ................................................. 51712.2.2 Data Archiving Basics ....................................................... 51812.2.3 ILM-Aware Storage .......................................................... 52312.2.4 Architecture Required to Run SAP ILM ............................ 527


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12.3 Managing the Lifecycle of Information in Live Systems .................. 52912.3.1 Audit Area ....................................................................... 52912.3.2 Data Destruction .............................................................. 53212.3.3 Legal Hold Management .................................................. 532

12.4 Managing the Lifecycle of Information from Legacy Systems .......... 53412.4.1 Preliminary Steps .............................................................. 53412.4.2 Steps Performed in the Legacy System .............................. 53612.4.3 Steps Performed in the Retention Warehouse System ....... 53712.4.4 Handling Data from Non-SAP Systems During

Decommissioning ............................................................. 53912.4.5 Streamlined System Decommissioning and Reporting ....... 539

12.5 System Decommissioning: Detailed Example ................................. 54212.5.1 Data Extraction ................................................................ 54312.5.2 Data Transfer and Conversion ........................................... 54812.5.3 Reporting ......................................................................... 55512.5.4 Data Destruction .............................................................. 559

12.6 Summary ....................................................................................... 562

13 SAP Extended Enterprise Content Management by OpenText ................................................................................... 563

13.1 Capabilities of SAP Extended ECM ................................................. 56513.1.1 Data and Document Archiving ......................................... 56613.1.2 Records Management ...................................................... 56713.1.3 Content Access ................................................................. 56813.1.4 Document-Centric Workflow ............................................ 56813.1.5 Document Management ................................................... 56813.1.6 Capture ............................................................................ 56913.1.7 Collaboration and Social Media ........................................ 569

13.2 How SAP Extended ECM Works with the SAP Business Suite ......... 57013.3 Integration Content for SAP Business Suite and

SAP Extended ECM ....................................................................... 57213.3.1 SAP ArchiveLink ............................................................... 57213.3.2 Content Management Interoperability Standard and

SAP ECM Integration Layer .............................................. 57413.3.3 SAP Extended ECM Workspaces ....................................... 575

13.4 Summary ....................................................................................... 582

The Authors ..................................................................................................... 583Index................................................................................................................. 591


591

Index

A

Accelerated reporting, 542Access server, 313Active area, 473Address

cleansing, 125, 128cleansing/enhancement, 137correction, 366directories, 362, 363information, 55parsing, 353profiling, 423validated, 137

Address cleanse, 350, 352transform, 344

Advanced profiling, 422results, 424

AIS, 559Alias, 272All-world address directory, 362Ambari, 104Analytical use, 309APIs, 337Application architecture, 117Application integration, 242Application link enabling (ALE), 469, 481Architecture, 209

retention management, 527, 528system decommissioning, 528, 529

Archival data, 220, 223Archive, 541, 565

file, 526hierarchy, 525index, 526

Archive administration datatransfer, 552

Archive Development Kit (ADK), 519Archive Management, 553, 554Archiving, 165

object, 171, 173policies, 176scope, 264strategy, 261using SAP HANA, 262

Archiving objectdefinition, 519SAP ILM-enabled, 527, 548specific customizing, 554work center, 520

Assessment, 59Asynchronous replication, 135Atomic data, 218Attributes, 274Audit area

definition, 529demo, 530product liability, 530set up, 548tax, 530

Audit packagecreate, 556extract to BI, 557

Auditing, 172Automated electronic discovery, 169

B

BAdI, 469change UI for entity type, 478

BAPI, 321Bar codes, 573Best practices methodology, 183Best record strategy, 385BI, 175Big data, 41, 42, 43

processing and analysis, 323SAP HANA vs Hadoop, 109

Binding, 427, 431, 447, 454Blueprint, 297Break group, 373, 376BRFplus, 179, 468, 480, 497, 498, 499, 501

custom validations, 474single value decision table, 502, 503user agent decision table, 502

Bulk data load, 120Business Address Services, 123, 137Business efficiency, 154Business glossary, 115, 148


592

Index

Business intelligence (seeBI), 147, 369, 370

Business process descriptions, 149Business process manager, 45Business process owner, 45Business rule, 232, 425Business Rules Framework, 468Business term glossary, 140, 405, 410Business term taxonomy, 406Business user, 142Business Value Analysis, 454Business-complete data, 548Business-incomplete data, 548

C

Canonical Data Model, 281Capture, 566, 569Cata dictionary, 215CDE, 535, 545

archive data, 545extraction services, 548

Centers of excellence, 54Central Management Console � see CMCCentral Management Server � see CMScFolders, 159Change management, 244, 284Change request, 472, 505, 510

create, 510create process, 494process, 468type, 499, 506UI, 496

Checksum function, 551Cleansing, 232

package, 360, 378process, 352rule, 140, 405, 406

Cleansing Package Builder, 143, 144, 387Cloud, 118

applications, 118bulk data load, 120rapid-deployment, 183real-time data access, 120SAP HANA database, 292

Cluster, 98CMC, 408

connections, 414internal scheduler, 434set up Data Insight project, 416

CMIS, 574Collaboration, 566, 569Compliance, 37, 60, 62

monitoring, 179requirements, 65

Conceptual data model (CDM), 116, 273Condition alias, 502Consolidate, 369Content, 32

access, 565, 568Content Data Extractor tool, 539Content Management Interoperability Ser-

vices � see CMISContext data, 548

extractor, 173, 535Context information, 545Correction, 345CRM, 124, 137, 159

content management, 159Cross-domain dependency, 284Culture dimension, 54Custom extraction rules, 392Customer complaints, 253Customer information, 59Customer relationship management, 62

D

DART Browser, 546Data

administrator, 221analysis, 80analyst, 45, 97, 103, 410, 413, 415, 416architect, 233assessment, 34cleansing, 68consolidation, 383correction, 128, 356, 362distribution, 224domain, 174, 442, 450domains, 55


Index

593

Data (Cont.)elements, 345enhancement, 366, 372integrated, 218integration, 50item, 274lineage, 36, 406, 410loading, 127management, 224, 225move/synchronize across enterprise, 133owner, 44parsing, 356, 387planning, 297policies, 51profiling, 232, 240quality, 34, 39, 40real-time replication, 92retention, 217source, 71, 447sources, 237standardization, 128, 356standardized, 359, 360standards, 54, 220steward, 45stewardship, 209synchronization, 127, 248transfer, 483transformation, 327validation, 127, 128, 312

Data archiving, 167, 168, 169, 173, 256, 261, 263, 265, 518, 566basics, 518process, 522

Data Assurance, 136Data cleanse, 345, 353, 356, 366, 378

data correction, 362data standardization, 356data validation, 364standardization, 358transform, 143, 344

Data cleansing, 121, 127, 231Data Cleansing Advisor, 464Data destruction, 532, 559

in the live database, 532in the retention warehouse, 559security considerations, 561

Data dictionarydata items, 274

Data enrichment, 128Data extract browser, 546Data flow, 238, 317, 322, 335, 385, 396, 397,

400add query transform, 325add source, 325add target, 325add to job, 327create, 303, 324, 339define, 311example, 302GUI, 302move to production, 304

Data governance, 410Data Insight, 406, 409, 414Data Insight project, 415, 437, 439, 448, 454

add table/file, 416define multiple, 415set up, 415set up connection, 415

Data integration, 185, 246, 307, 312, 389bulk data load, 291cloud to cloud, 291on-premise to cloud, 291scenarios, 307

Data integrator transform, 332Data lifecycle, 520Data load, 228Data mart, 126, 210Data migration, 50, 66, 121, 124, 127, 176,

231, 242, 308, 369activities, 188business rules, 232content, 185, 195data enrichment, 236process flow, 231rapid deployment, 184reasons for, 185scope, 229

Data modelactivate, 488create, 485plan, 485


594

Index

Data modeling, 407, 408, 476SAP HANA, 89

Data movement model (DMM), 116Data profiling, 122, 140, 141, 346, 372, 405,

413, 428, 432basic, 419, 422create validation rule, 427project, 415set up task, 417

Data provisioning, 84SAP HANA, 90

Data quality, 67, 68, 127, 131, 132, 138, 139, 148, 174, 176, 181, 186, 225, 226, 232, 239, 240, 307, 312, 378, 387, 389, 392, 414, 436, 437, 451, 452, 453, 465, 478, 479, 509assessment, 431dashboard, 435levels, 51management, 343measurement, 143metrics, 226monitor, 427monitoring, 140, 141, 406, 452process, 130requirements, 225, 405root-cause analysis, 453score, 431, 435, 437scorecard, 426, 442, 446, 448, 452scores, 448telephone patterns, 241

Data Quality Advisor, 145, 461Data quality dimension, 438, 443, 445, 448,

450accuracy, 443, 445completeness, 443conformity, 443, 446, 448consistency, 444integrity, 444timeliness, 444uniqueness, 444

Data quality scorecard, 141, 437bind data sources to, 447components, 439drill into details, 449key data domain, 442tile, 439

Data quality scorecard (Cont.)view, 448view Business Value Analysis, 459

Data replication, 481framework, 469

Data Servicesconnect to Hadoop, 323connect to SAP BW, 322connect to SAP ERP, 321connect to SAP HANA, 323server tier components, 313

Data services connectivity, 292Data Services Workbench, 91Data steward, 68, 72, 142, 143, 144, 178, 438,

443, 448, 502, 507UI, 311

Data warehouse, 126, 214, 218, 308, 333governance, 220modeling, 282

Database administrators, 262Databases, 126Datastore, 318, 396

create, 300, 319import tables, 300

Decision tables, 499Decommission, 26Decommissioning, 168, 539De-duplication, 125, 231, 234, 235, 240, 365,

370, 372Demographic data, 368Dependency profiling, 423Derivation, 478Digital asset management, 155Dimension object, 283Dimensional model, 283Direct linkage, 453Direct marketing, 370Discovery, 59Discrete format, 347Document, 563

archiving, 565, 566management, 155, 566, 568

Document-centric workflow, 566, 568Domain, 274Drawing management, 252DRF, 469DSO, 557


Index

595

Dual database strategy, 214Dun and Bradstreet, 29, 63, 236Duplicate check, 479Duplicate checking, 137

E

Easy Document Management, 159ECM, 43, 50, 113, 154, 156, 162, 165, 251

integrated, 563integration layer, 574workspace, 575, 576, 578

ECMLink, 575Editions, 477EIM, 25, 27, 28, 36, 40, 43, 69

architecture recommendations, 209Hadoop, 110strategy, 43with SAP HANA, 89

E-mail Response Management System, 159Emails, 563Enterprise

application integration, 217search, 160services, 469workspace, 159

Enterprise CDM, 273best practices, 276concepts, 274obsolete definition, 278versions, 277

Enterprise Content Management � see ECMEnterprise data warehouse, 236, 344Enterprise glossary, 271

naming standards, 272synonyms, 277

Enterprise information architecture, 269consolidate source data, 238details, 210role of, 228scope, 212

Enterprise Information Management � see EIM

Entity, 274, 391attribute, 274data item, 274extraction, 391, 397

Entity relationship, 275define in PowerDesigner, 276

Entity type, 476, 485choose for business object, 488create, 485relationships, 477

ERP, 137, 179ETL, 110, 120, 126, 127, 217, 235, 247, 248,

307, 406, 407, 408, 438, 452, 539Executive sponsor, 55Extract, Transform, Load, 541Extraction, transformation, and loading � see

ETLExtractors, 321

F

Fact object, 283Failed record, 450Family match, 373File source, 320Financial impact, 456Financial master data, 152Floor Plan Manager (FPM), 489Flume, 102Form UIBB, 491

G

Generic object services, 582Geo directories, 368Geocode, 240, 241Geocoding, 367, 370Geolocation, 219Geospatial, 366

data, 367Global address cleanse, 350, 353, 357

parse data, 350Global address cleansing, 423, 425Global data manager, 45Global standards, 41Governmental regulations, 40Governmental standards, 55Grammatical parsing, 390GS1, 144Guidelines, 227


596

Index

H

Hadoop, 42, 96, 100, 323as SAP HANA extension, 109bulk data transfer, 101cluster, 98collect logs, 102common use, 105ecosystem, 101HBase, 103HDFS, 99Hive, 102, 108, 109in the enterprise, 107introduction, 98machine learning libraries, 104Mahout, 104MapReduce, 99, 106master node, 98online archive, 106Pig, 103, 106, 108SAP HANA, 109scripting, 103SQL interface, 102strengths and weaknesses, 108Tez, 100worker node, 98

Hadoop Distributed File System (HDFS), 99HANA Cloud Integration for data integration,

120HCI

blueprinting phase, 297connectivity, 291create project, 302create task, 301data flow editor, 296datastore, 300define data extraction, 294integration steps, 297logs, 305on-premise component (HCI Agent), 294Predefined template, 298set up prerequisites, 299set up tenant, 299set up user roles, 299transform type, 303tutorial, 297user experience, 295

HCI Agent, 294HCP

service layers, 292Hive, 102

I

IDEA, 559IDoc, 316, 321, 469, 481, 483ILM, 165, 168, 210

definition, 515drivers and pain points, 515external drivers, 516for legacy data, 534in live systems, 529internal drivers, 516work centers, 519

ILM objectdefinition, 519

ILM-aware storage, 523system, 523

ILM-BC 3.0certification, 523

Images, 32Impact analysis, 228, 284, 410, 453

reporting, 287Implementation methodologies, 183Individual match, 373Industry standards, 34InfoCube, 557Information

access, 51discovery, 50, 76, 175lifecycle, 216platform services, 312, 314policies, 52retention manager, 530security, 209, 227strategy, 54

Information assetData Quality Advisor, 145

Information governance, 26, 28, 33, 49, 52, 55, 58, 63, 67, 68, 76, 108, 110, 114, 139, 173, 177, 189, 209, 223, 225, 232, 239, 244, 260committee, 68council, 67


Index

597

Information governance (Cont.)customized, 153develop, 58establish process, 60evaluate, 53framework, 69preventative, 61technology enablers, 177

Information lifecycle management � also see SAP ILM

Information lifecycle management � see ILMInformation management, 220

scope, 212Information management strategy, 37, 154,

165, 166Information platform services (IPS), 313Information Steward

Data Insight module, 145metadata, 140Metapedia, 410

In-memory cloud platform, 292In-memory computing, 78Integrated ECM, 563Integration flow

web-based UI, 291Integration Platform as a Service (iPaaS), 118Intelligent Driver Assistant, 254iPaaS, 93IPS, 313IRM, 530IT administrator, 414, 416

J

Java Message Service, 316Job

create, 324, 327, 340execute/debug, 336real-time, 338

K

Key mapping, 481, 482Key words, 412Knowledge worker, 72KPI, 73

L

LDMstructure definition, 279

Legacy System Migration Workbench (LSMW), 186

Legacy systems, 172extract data, 173

Legal compliance, 261Legal hold, 34, 170, 179

management, 169setting, 533

Legal hold managementoverview, 532

Legal requirements, 51, 168Lifecycle, 27Link and Sync, 284

parts, 284technology, 269

Linking, 284Local reporting, 555, 559Logical data model (LDM), 278

M

Maintenance notification, 162Management reporting, 214Management reporting and analytics, 217Managing content, 32Manual rule binding, 431Map reduce, 100Mapping, 326Mapping Editor, 282MapReduce

text data processing, 323Master data, 29, 34, 37, 64, 68, 149, 151, 176,

177, 181, 215, 216, 477consolidation, 150customer, 152export, import, convert, 483harmonization, 150manage centrally, 224management, 213, 308, 370material, 153program recommendations, 243strategy, 243


598

Index

Master data governance, 499application framework, 469

Master record, 374Match, 369

comparison options, 373configuration, 374criteria, 373, 380group, 374, 382, 384level, 373performance, 377scenario, 373score, 381set, 373standards, 378threshold, 374

Match Criteria Editor, 381Match Editor, 374, 380Match method

weighted scoring, 375Match transform, 344Match Wizard, 374, 379, 380, 381Matching, 128, 129, 132

process, 368routine, 240score, 479standards, 361strategy, 371, 372techniques, 372

Matching methodcombination, 374, 376rule-based, 374weighted scoring, 374

MDG communicator, 493Mergers and acquisitions, 228, 516Metadata, 147, 213, 215, 221, 252, 551

analysis, 140, 147apply, 222management, 139, 147, 452, 453

Metadata integrationexecution, 409

Metadata integrator, 407, 409configure, 407

Metadata management, 405, 406, 413Metapedia, 148, 149, 410

synonym/keyword, 413techniques, 411

Migration, 28, 38Missing data, 72Model comparison, 289Monitor, 139

Monitoring, 437Multiline data, 349Multiline format, 347Multiline hybrid format, 347

N

No-match thresholds, 383Nondiscrete data components, 349Nonparty data, 387Nonrelational data, 109Non-SAP systems, 534NoSQL, 96, 103

datastore, 103

O

OLAP, 78, 97, 126OLTP, 78, 97On-premise

rapid-deployment, 183Oozie, 104Open hub, 322OpenText, 113, 156, 157, 565OpenText Knowledge center, 580Operational analytics, 214Operational data, 215, 216Operational efficiencies, 37, 39, 245Operational master data management, 150Operational reporting, 214Operational use, 308Optimical character recognition (OCR), 254Organizational change management, 175Organizational ownership, 221Output management, 155Output schema, 400

P

PaaS, 88Parallel processing architecture, 96Parsed data, 350, 353, 360Parsed output, 352, 356Parsing, 345Physical data model (PDM), 270, 278

structure definition, 279


Index

599

Physical data models, 116Pig, 103

scripts, 323Platform transformation, 330PLM, 162Point-of-interest, 368Policies, 227Policy

define, 549definition, 178engine, 169implementation, 178set status to live, 550

Policy categoryresidence rules, 549retention rules, 549

Portal Site Management, 157PowerDesigner models, 116Predefined template, 298Predictive analytics, 52, 175

algorithm, 80Pre-parsed data, 353Principle, 211Print list, 531

retrieve, 524Procedures, 227Process modeling, 480Procurement, 162Product liability, 173Product lifecycle management (see

PLM), 159Product quality, 394Profiling task, 418

view results, 421Project, 302

Q

Quality, 37dimension, 440, 509

Query transform, 325, 326, 327

R

Rapid Data Migrationcontent, 189

Rapid Mart, 452

Rapid-deployment solutionsdata migration, 184Information Steward, 197SAP MDG, 203

Real-time data replication and synchroniza-tion, 133

Real-time service, 340expose as web service, 342

Records management, 155, 179, 261, 565, 567Redundancy profiling, 423Reference data, 213, 215Regulatory compliance, 40Replication Server

Data Assurance, 136Reporting

increase performance, 542local, 559

Repository tier, 314Requirements traceability matrix, 285Residence time

definition, 521Retention, 176

limits, 220management, 168, 169policies, 28, 171, 173, 178, 261time unit, 550

Retention management, 256, 518capabilities, 529unstructured data, 531

Retention Management CockpitAdministrator, 537Line of Business, 537

Retention perioddefinition, 521maximum, 550minimum, 550

Retention rules, 223basics, 550

Retention warehouse, 170, 172, 542set up, 173

Retirement, 33Row data

report discrepancies, 136Rule binding, 441, 448, 450Rule tasks

execute, 433Rule-based, 374Rules-based workflow, 496, 499

design, 505


600

Index

S

SAP Accelerator for Data Migration by Back-Office Associates, 196

SAP ArchiveLink, 165, 169, 514, 572, 575attachments, 551documents, 524

SAP Archiving by OpenText, 157, 164, 165, 167, 169, 173, 514

SAP Audit Format, 559SAP Business Process Management, 61, 179SAP Business Suite, 153, 162, 252, 255, 571

standard business processes, 258validations, 474

SAP Business Suite on SAP HANA, 86SAP Business Warehouse (SAP BW), 67, 123,

125, 126, 127, 169, 170, 322, 407, 452, 536connect to retention warehouse, 173reporting, 556

SAP Business Workflow, 66, 74, 151, 179, 259, 468, 494, 497, 498, 499, 573configuration, 505

SAP BusinessObjects BI, 147platform, 121, 125, 126, 312, 408

SAP BusinessObjects Business Intelligence, 126, 179, 407

SAP BusinessObjects Business Intelligence (SAP BusinessObjects BI), 74

SAP BusinessObjects universe, 271SAP BusinessObjects Web Intelligence, 124,

189SAP Cloud Operations, 299SAP Content Server, 160SAP CRM, 123, 138, 344

Customer Interaction Center, 259document access, 164

SAP Customer Relationship Management (SAP CRM), 121, 190, 253

SAP Data Quality Management, 95, 128SDK, 127version for SAP solutions, 137

SAP Data Services, 61, 64, 66, 67, 74, 120, 121, 122, 124, 125, 126, 127, 129, 150, 151, 153, 168, 178, 181, 193, 229, 246, 307, 318, 322, 325, 360, 363, 367, 368, 372, 387, 394, 407, 409, 423, 425, 452, 454, 467, 514address check, 137administration, 311

SAP Data Services (Cont.)architecture, 309batch jobs, 316breakpoints, 336built-in functions, 327call as external service, 480central repository, 314cleansing transformation, 233CMC, 408connect to file source, 320data enhancement, 366data quality, 240data validation, 364Designer, 314enrich data, 236ETL, 237ETL capabilities, 539evolution, 236extract legacy data, 242function categories, 328functions, 327history preservation, 333job, 316, 327, 336job server, 311, 313lineage analysis, 312Local Object Library, 315local repository, 314lookup function, 328major components, 309management console UI, 311mappings, 311metadata, 311migration content, 190object types, 316overlap with SAP PI, 249parsing, 350Project Area, 315query transform, 325Rapid Data Migration, 187real-time job, 316, 338real-time service, 337, 340SAP HANA, 90server tier, 313tool palette, 315update source system, 234use Hadoop, 111


Index

601

SAP Data Services Designer, 310SAP Digital Asset Management, 157SAP Document Access, 157, 164, 169SAP Document Access by OpenText, 514, 524SAP Document Presentment, 157SAP ECC, 124SAP Employee Management, 157SAP Enterprise Asset Management, 162SAP Enterprise Portal, 157, 159, 161, 164,

470, 483, 575SAP ERP, 121, 123, 152, 170, 172, 173, 190

document access, 164migrate data to, 193migration content, 190

SAP Extended ECM, 64, 66, 158, 161, 162, 164, 165, 178, 179, 251, 252, 254, 256, 260, 565, 569, 571ArchiveLink, 572capture, 569customer complaints, 254customize workspace, 579integration with the SAP Business Suite, 570metadata, 576migrate invoices to, 257OpenText, 574printout, 253UI options, 259WebGUI, 578workspace types, 577

SAP Extended Enterprise Content Manage-ment by OpenTextsuccess factors, 257

SAP Folders Management, 159SAP GUI, 579SAP HANA, 42, 48, 77, 106, 123, 125, 126,

127, 170, 309, 323analytics and BI, 85archiving, 262as an application platform, 86basics, 81business benefits, 78components and architecture, 82data modeling, 89data provisioning, 84, 89data quality, 94Hadoop, 109index server, 83

SAP HANA (Cont.)native advanced features, 85real-time trigger-based replication, 92SAP Business Suite, 86the cloud, 87with EIM, 89with SAP MDG, 475XS server, 84

SAP HANA Cloud Integration (HCI), 93, 118SAP HANA Cloud Integration for process inte-

gration, 119SAP HANA Cloud Platform (HCP), 118, 294SAP HANA Enterprise Cloud, 87SAP HANA One, 88SAP HANA Studio, 84SAP Identity Management, 121SAP ILM

architecture, 527cockpit roles, 537conversion, 537, 551conversion, replace old sessions, 554cornerstones, 517data archiving, 518database storage option, 525object, 548retention management, 518retention rules, 531Store Browser, 561system decommissioning, 518

SAP IMG, 476SAP Information Lifecycle Management (ILM),

113, 159, 164, 165, 166, 168, 169, 170, 173, 178, 179, 182, 186, 256, 567legacy functions, 173retention warehouse, 172

SAP Information Steward, 61, 74, 113, 122, 139, 143, 147, 148, 149, 150, 153, 178, 179, 186, 188, 232, 233, 235, 309, 312, 387, 407, 409, 416, 427, 431, 447, 466, 508, 509, 510Business Value Analysis, 456CMC, 408Data Insight project, 414hyperlinked numbers, 420metadata management, 410Quality Dimension attribute, 444


602

Index

SAP Information Steward (Cont.)rapid-deployment solutions, 197read repository, 270SAP HANA, 94statistical information, 413UI, 311

SAP Invoice Management, 157, 257, 259SAP IQ

store archive file, 526store archive index, 526

SAP Landscape Transformation, 186SAP Landscape Transformation Replication

Server, 92SAP LT Replication Server, 514, 539SAP Master Data Governance

rapid-deployment solution, 203SAP HANA, 95

SAP NetWeaver Application Server ABAP, 137, 186, 580

SAP NetWeaver Business Client, 470, 483, 537, 538, 548, 551, 575SAP ILM cockpits, 539

SAP NetWeaver Master Data Management (MDM), 467

SAP NetWeaver Master Data Management (SAP NW MDM), 63, 66, 113, 123, 125, 137, 150, 151, 153, 178, 179, 181, 467, 468, 470, 508assign processors to workflow, 495business activity, 480change request ID, 66configuration steps, 476custom-defined object, 484data quality, 138define UI, 489flex mode, 473generic workflow template, 499import master data, 483maintain SAP ERP attributes, 65master data changes, 234master data hub, 471multi-attribute drill-down, 475process flow, 473reuse mode, 473rules-based workflow, 497run on SAP ERP, 471searches, 479

SAP NetWeaver Master Data Management (SAP NW MDM) (Cont.)trigger workflow, 66UI modeling, 478with SAP HANA, 475

SAP Plant Maintenance (PM), 162SAP Portal Content Management, 157SAP Portal Content Management by Open-

Text, 255SAP PowerDesigner, 115

compare dialog, 286data mapping, 282define relationship, 276dimensional modeling, 283glossary, 272glossary, configure, 273impact analysis, 287library, complex types, 282Link and Sync technology, 284linking, 284mapping, 281model compare, 289realize value, 269SAP Business Suite, 270SAP HANA, 270synchronizing, 285table definition, 282XML model, 280

SAP Process Integration (PI), 246SAP Process Orchestration, 64, 65, 66, 74,

246, 343SAP Rapid Data Migration, 186SAP Rapid Deployment solutions, 183SAP Replication Server, 92, 114, 133

Integration with SAP Data Services, 136Integration with SAP PowerDesigner, 136

SAP River, 81SAP Smart Business, 476SAP solutions for information lifecycle man-

agement, 513overview, 513

SAP StreamWork, 159SAP Travel Receipt Management, 157SAPUI5, 84Scaling, 96Scanned invoice, 169Schema, 338Scope, 278Scorecard, 139


Index

603

Scripting, 304Semantic disambiguation, 390Sentiment, 32Sentiment analysis, 131Service-level agreement, 72, 414

normal, reverse, 72Similarity scoring, 372Single Instruction, Multiple Data (SIMD), 81Single-object maintenance

UI, 489Slowly changing dimensions, 333SN_META

file, 551Snapshot, 524, 545Social media, 41, 569SPRO, 476Sqoop, 101SRM, 62SRS, 528Staging, 472

area, 473Standardization, 345

rules, 387Standards, 227Step type, 506, 507Storage, 168, 172Storage and retention service, 528Storage system

ILM-aware, 523Structured data, 32, 33, 213Subordinate record, 374Supplier, 28Sybase, 318Sybase IQ, 127Synchronizing, 285Synonym, 412

assign to common term, 277System consolidation, 186System decommissioning, 43, 169, 170, 172,

518, 539archive transactional data, 543configure retention warehouse system, 535convert data, 537data analysis, 534data transfer, 551data transfer and conversion, 548define audit areas, 537detailed example, 542

System decommissioning (Cont.)enable system for SAP ILM, 535extract data, 543non-SAP systems, 539preliminary steps, 534report on legacy data, 537reporting, 555set up audit areas and rules, 548transfer and convert files, 551transfer archive administration data, 552transfer data, 537

System Decommissioning CockpitAdministrator, 538Line of Business, 538

System landscape harmonization, 516System of record, 216

T

Task, 301move to production, 304start with web service, 305template, 302

Taxaudit, 166reporting, 173

Technical requirement, 425Tenant, 292

set up, 299Term

hierarchies, 412related, 412

Textanalytics, 388data, 32, 394, 395mining, 36

Text data processing, 121, 130, 131, 132, 181, 307, 389, 390, 393, 394, 399, 400dictionary, 393entity, 392entity types, 399extraction, 392rule, 393transform configuration, 396use cases, 388

Time reference, 550


604

Index

TOAxtables, 524

TransactionILM, 545ILM_DESTRUCTION, 559ILM_TRANS_ADMIN_ONLY, 552IRM_CUST, 550IRMPOL, 526, 548SARA, 543TAANA, 535

Transactional application, 218Transactional data, 213, 543Transform, 318, 397, 399

address cleanse, 363, 365, 367case, 331data cleanse, 233, 353, 360, 367, 387entity extraction, 389, 393, 394, 396geocoder, 367, 368global address cleanse, 354history preserving, 334key generation, 335Map_Operation, 332match, 372, 373, 380, 385merge, 331query, 331Row_Generation, 332SQL, 331table comparison, 334transform configuration, 397user defined, 332validation, 332, 437

Transformationcomplex, 330

U

UIbuilding blocks (UIBBs), 489configuration, 494

Unified business language, 115Uniqueness profiling, 423Universal data cleanse, 241Universe, 271UNSPSC, 144Unstructured content, 212, 213, 571

Unstructured data, 105, 109, 168, 212, 401lifecycle, 221retention management, 531text, 389turn into structured data, 110

Unstructured information, 33, 563User interface, 571

V

Validation, 471, 478rule, 509, 510transform, 364

Validation rule, 139, 142, 405, 406, 420, 425, 427, 432, 433, 441, 443, 446, 448, 450, 452, 454add, 445associate with data source, 447create in rule editor, 429test, 430

Value mapping, 481, 482

W

Web content management, 155WebDAV

ILM-enhanced interface, 523Weight scoring, 374Weighting, 446, 448What-if analysis, 460Work center

archiving, 520reporting, 520

Work order, 163Workflow, 316, 469

distribute data maintenance, 471Hadoop, 104rules-based, 496

Workspaces, 161, 162, 163, 254, 256, 257, 315, 581binder workspace, 577business workspace, 577case workspace, 577

Write programlog, 544


Index

605

X

XMLdata archiving service, 528export/import master data, 483schema, 338, 342

XML DAS, 528XML Schema Definition (XSD), 280

Y

YARN, 99

Z

ZooKeeper, 104


First-hand knowledge.

Brague, Dichmann, Keller, Kuppe, On

Enterprise Information Management with SAP605 Pages, 2014, $69.95/€69.95 ISBN 978-1-4932-1045-9

www.sap-press.com/3666

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Corrie Brague is the director of Data Quality Product Management for SAP, where she defines software solutions that help businesses assess, improve, and monitor their data quality.

Markus Kuppe is vice president and chief solution architect for SAP Master Data Governance. He led various programs across the SAP Business Suite in topics such as analytics, user experience, or architecture. He is a frequent author and speaker at business events.

David Dichmann is director of product management for SAP’s enterprise architecture and modeling tool, Power- Designer.

Phillip On is an industry veteran for Enterprise Information Management with more than 13 years of experience on this topic working for SAP, Business Objects, and Oracle.

George Keller has more than 20 years of experience in the field of information management, having worked within engineering, business applications, and product management organizations. He has also served as a professional delivery project manager for a number of Fortune 100 clients.

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