Data-Ed Online: Let's Talk Metadata: Strategies and Successes

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Welcome!

Date: September 11, 2012

Time: 2:00 PM ET

Presented by: Dr. Peter Aiken

18/14/2012

Let’s Talk Metadata:

Strategies and Successes

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Meet Your Presenter: Dr. Peter Aiken

4

• Internationally recognized thought-leader in the data management field with more than 30 years of experience

• Recipient of the 2010 International Stevens Award

• Founding Director of Data Blueprint (http://datablueprint.com)

• Associate Professor of Information Systems at Virginia Commonwealth University (http://vcu.edu)

• President of DAMA International (http://dama.org)

• DoD Computer Scientist, Reverse Engineering Program Manager/Office of the Chief Information Officer

• Visiting Scientist, Software Engineering Institute/Carnegie Mellon University

• 7 books and dozens of articles• Experienced w/ 500+ data management practices in 20 countries

#dataed

8/14/2012

DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION

Let’s Talk Metadata:

Strategies and Successes

Let’s Talk Metadata: Strategies and Successes

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Abstract: Metadata Practices

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This presentation describes how data management can be enhanced using meta-processing. Commonly described as metadata management, properly implemented metadata practices incorporate data structures into more abstract processing. By using data about the data to enhance its value, its understandability, its ease of use, and many other options – organizations have developed sophisticated ways to enhance their data management and especially their data quality engineering efforts.

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Outline

1. Data Management Overview

2. What is metadata and why is it important?

3. Types of metadata

4. Metadata for unstructured data

5. Strategy and implementation

6. Guiding Principles

7. Take Aways, References and Q&A

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Tweeting now: #dataed

8/14/2012

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The DAMA Guide to the Data Management Body of Knowledge

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Data Management Functions

Published by DAMA International•The professional association for Data Managers (40 chapters worldwide)

DMBoK organized around •Primary data management functions focused around data delivery to the organization

•Organized around several environmental elements

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The DAMA Guide to the Data Management Body of Knowledge

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Environmental Elements

Amazon:

http://www.amazon.com/DAMA-Guide-Management-Knowledge-DAMA-DMBOK/dp/0977140083

Or enter the terms "dama dm bok" at the Amazon search engine

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Data Management

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Data Management

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Manage data coherently.

Manage data coherently.

Share data across boundaries.

Share data across boundaries.

Assign responsibilities for data.

Assign responsibilities for data. Engineer data delivery

systems.

Engineer data delivery systems.

Maintain data availability.

Maintain data availability.

Data Program Coordination

Organizational Data Integration

Data Stewardship

Data Development

Data Support Operations

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Data Management

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Metadata Management

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from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International


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Metadata or metadata

• In the history of language, whenever two words are pasted together to form a combined concept initially, a hyphen links them.

• With the passage of time, the hyphen is lost. The argument can be made that that time has passed.

• There is a copyright on the term "metadata," but it has not been enforced.

• So, term is "metadata"

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DefinitionsMetadata is …

•… everywhere in every data management activity and integral to all IT systems and applications.

•… to data what data is to real life. Data reflects real life transactions, events, objects, relationships, etc. Metadata reflects data transactions, events, objects, relations, etc.

•… the data that describe the structure and workings of an organization’s use of information, and which describe the systems it uses to manage that information. [quote from David Hay's new book, page 4]

•Data describing various facets of a data asset, for the purpose of improving its usability throughout its life cycle [Gartner 2010]

•Metadata unlocks the value of data, and therefore requires management attention [Gartner 2010]

Metadata Management is …

•… the set of processes that ensure proper creation, storage, integration, and control to support associated use of metadata

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Analogy: Card catalog in a library • Card catalog identifies what books

are stored in the library and where they are located in the building

• Users can search for books by subject area, author, or title

• Catalog shows author, subject tags, publication date and revision history of each book

• Card catalog information helps determine which books will meet the reader’s needs

• Without this catalog resource, finding books in the library would be difficult, time consuming and frustrating

• Readers may search many incorrect books before finding the right book if a catalog does not exist

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Definition, cont’d• Metadata is the card catalog in a

managed data environment

• Abstractly, Metadata is the descriptive tags or context on the data (the content) in a managed data environment

• Metadata shows business and technical users where to find information in data repositories

• Metadata provides details on where the data came from, how it got there, any transformations, and its level of quality

• Metadata provides assistance with what the data really means and how to interpret it

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Defining Metadata

Metadata is any combination of any circle and the data in the center of the spark!

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Adapted from Brad Melton

Data

WhereWhy

What How

Who

When

Data

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Who: Author

What: Title

Where: Shelf Location

When: Publication Date

Manage a large amount of data (the Library) with a small amount of metadata (Card Catalog)

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Data

WhereWhy

What How

Who

When

DataData

DataLibrary Book

Library Metadata ExampleLibraries can operate efficiently through careful use of metadata (Card Catalog)

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Data

WhereWhy

How

Who

When

Messages

What

Outlook Example

"Outlook" metadata is used to navigate and manage email

Imagine how managing e-mail (already non-trivial) would change if Outlook did not make use of metadata

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Who: "To" & "From"

What: "Subject"

How: "Priority"

Where: "USERID/Inbox", "USERID/Personal Folders"

Why: "Body"

When: "Sent" & "Received”

•Find the important stuff/weed out junk •Organize for future access/outlook rules

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Outlook Example, cont’d

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Metadata practices connect data sources and uses in an organized and efficient manner

• What is the structure of metadata practices?– Storage: repository, glossary, models, lineage - currently multiple technologies are used– Engineering: identifying/harvesting/normalizing/administer evolving metadata structures– Delivery: supply/access/portal/definition/lookup search identify/ensure required

metadata supplies to meet business needs– Governance: ensure proper/creation/storage/integration/control to support effective use

• When executed, engineering and delivery implement governance

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Sources

Metadata Governance

Metadata Engineering

Metadata Delivery Uses

Metadata Practices

MetadataStorage

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Organized Knowledge 'Data'

Improved Quality Data

Data Organization Practices

Metadata Practices will be inextricably intertwined with Data Quality and Master Data and Knowledge Management, (among other EIM Functions)

Operational Data

Data Quality Engineering

Master Data Management

Practices

Suspected/Identified

Data Quality

Problems

Routine Data Scans

Master Data Catalogs

Routine Data Scans

KnowledgeManagement

Practices

ExtractionSources

Data that might benefit from Master Management

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Metadata History 1990-2008The history of Metadata management tools and products seems to be a metaphor for the lack of a methodological approach to enterprise information management:

• Lack of standards and proprietary nature of most managed Metadata solutions cause many organizations to avoid focusing on metadata

• This limits organizations’ ability to develop a true enterprise information management environment

• Increased attention given to information and its importance to an organization’s operations and decision-making will drive Metadata management products and solutions to become more standardized

• More recognition to the need for a methodological approach to managing information and metadata

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Metadata History: The 1990s

• Business managers began to recognize the value of Metadata repositories

• Newer tools expanded the scope• Potential benefits identified during this period

include:– Providing semantic layer between company’s system

and business users– Reducing training costs– Making strategic information more valuable as aid in

decision making– Creating actionable information– Limiting incorrect decisions

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Metadata History: Mid-to late 1990s• Metadata becomes more relevant to corporations who were

struggling to understand their information resources caused by: – Y2K deadline

– Emerging data warehousing initiatives

– Growing focus around the World Wide Web

• Beginning of efforts to try to standardize Metadata definition and exchange between applications in the enterprise

• Examples of standardization:– 1995: CASE Definition Interchange Facility (CDIF)

– 1995: Dublin Core Metadata Elements

– 1994 – 1999: First parts of ISO 11179 standard for Specification and Standardization of Data Elements were published

– 1998: Common Warehouse Metadata Model (CWM)

– 1995: Metadata Coalitions’ (MDC) Open Information Model

– 2000: Both standards merged into CSM. Many Metadata repositories began promising adoption of CWM standard

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Metadata History: 21st Century• Update of existing Metadata repositories for deployment on the web

• Introduction of products to support CWM

• Vendors begin focusing on Metadata as an additional product offering

• Few organizations purchase or develop Metadata repositories

• Effective enterprise-wide Managed Metadata Environments are rare due to:

– Scarcity of people with real world skills

– Difficulty of the effort

– Less than stellar success of some of the initial efforts at some companies

– Stagnation of the tool market after the initial burst of interest in late 90s

– Still less than universal understanding of the business benefits

– Too heavy emphasis on legacy applications and technical metadata

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Polling Question #1

What have been the driving factors in focusing on metadata within the last decade?

a. Recent entry of smaller vendors into the market

b. Challenges related to addressing regulatory requirements

c. Declination to the existing Metadata standards

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Metadata History: Current Decade• Focus on need for and importance of metadata

• Focus on how to incorporate Metadata beyond traditional structured sources and include unstructured sources

• Driving factors:– Recent entry of larger vendors into the market

– Challenges related to addressing regulatory requirements, e.g. Sarbanes-Oxley, and privacy requirements with unsophisticated tools

– Emergence of enterprise-wide initiatives, e.g. information governance, compliance, enterprise architecture, automated software reuse

– Improvements to the existing Metadata standards, e.g. RFP release of new OMG standard Information Management Metamodel (IMM), which will replace CWM

– Recognition at the highest levels that information is an asset that must be actively and effectively managed

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Types of Metadata: Process Metadata• Process Metadata is...

– Data that defines and describes the characteristics of other system elements, e.g. processes, business rules, programs, jobs, tools, etc.

• Examples of Process metadata:– Data stores and data involved– Government/regulatory bodies– Organization owners and stakeholders– Process dependencies and decomposition– Process feedback loop and documentation– Process name

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Business Process Metadata

Who: Created the documentation?

What: Are the important dependencies among the processes?

How: Do the business processes interact with each other?

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Data

WhereWhy

What How

Who

When

Data

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Types of Metadata: Business Metadata

• Business Metadata describe to the end user what data are available, what they mean and how to retrieve them.

• Included are:– Business names and definitions of subject and

concept areas, entities, attributes– Attribute data types and other attribute properties– Range descriptions, calculations, algorithms and

business rules– Valid domain values and their definitions

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Types of Metadata: Technical & Operational Metadata

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• Technical and operational metadata provides developers and technical users with information about their systems

• Technical metadata includes…– Physical database table and column names, column properties, other

properties, other database object properties and database storage

• Operational metadata is targeted at IT operations users’ needs, including…– Information about data movement, source and target systems, batch

programs, job frequency, schedule anomalies, recovery and backup information, archive rules and usage

• Examples of Technical & Operational metadata:– Audit controls and balancing information

– Data archiving and retention rules

– Encoding/reference table conversions

– History of extracts and results

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Types of Metadata: Data Stewardship

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• Data stewardship Metadata is about...– Data stewards, stewardship processes, and responsibility

assignments

• Data stewards…– Assure that data and Metadata are accurate, with high quality

across the enterprise. – Establish and monitor data sharing.

• Examples of Data stewardship metadata:– Business drivers/goals– Data CRUD rules– Data definitions – business and technical– Data owners– Data sharing rules and agreements/contracts– Data stewards, roles and responsibilities

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Types of Metadata: Provenance

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• Provenance: – the history of ownership of a valued object or

work of art or literature" [Merriam Webster]– For each datum, this is the description of:

• Its source (system or person or department),

• Any derivation used, and

• The date it was created.

– Examples of Data Provenance:• The programs or processes by which it was created

• Its owner

• The steward responsible for its quality

• Other roles and responsibilities

• Rules for sharing it.

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Metadata Subject Areas

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Subject Areas Components

1) Business Analytics Data definitions, reports, users, usage, performance

2) Business Architecture Roles and organizations, goals and objectives

3) Business DefinitionsBusiness terms and explanations for a particular concept, fact, or other item found in an organization

4) Business Rules Standard calculations and derivation methods

5) Data GovernancePolicies, standards, procedures, programs, roles, organizations, stewardship assignments

6) Data IntegrationSources, targets, transformations, lineage, ETL workflows, EAI, EII, migration/conversion

7) Data Quality Defects, metrics, ratings

8) Document Content Management

Unstructured data, documents, taxonomies, ontologies, name sets, legal discovery, search engine indexes

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Metadata Subject Areas, cont’d

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Subject Areas Components

9) Information Technology Infrastructure

Platforms, networks, configurations, licenses

10) Conceptual data modelsEntities, attributes, relationships and rules, business names and definitions.

11) Logical Data ModelsFiles, tables, columns, views, business definitions, indexes, usage, performance, change management

12) Process ModelsFunctions, activities, roles, inputs/outputs, workflow, timing, stores

13) Systems Portfolio and IT Governance

Databases, applications, projects, and programs, integration roadmap, change management

14) Service-oriented Architecture (SOA) information:

Components, services, messages, master data

15) System Design and Development

Requirements, designs and test plans, impact

16) Systems ManagementData security, licenses, configuration, reliability, service levels

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Benefits of Metadata

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1) Increase the value of strategic information (e.g. data warehousing, CRM, SCM, etc.) by providing context for the data, thus aiding analysts in making more effective decisions.

2) Reduce training costs and lower the impact of staff turnover through thorough documentation of data context, history, and origin.

3) Reduce data-oriented research time by assisting business analysts in finding the information they need in a timely manner.

4) Improve communication by bridging the gap between business users and IT professionals, leveraging work done by other teams and increasing confidence in IT system data.

5) Increased speed of system development’s time-to-market by reducing system development life-cycle time.

6) Reduce risk of project failure through better impact analysis at various levels during change management.

7) Identify and reduce redundant data and processes, thereby reducing rework and use of redundant, out-of-data, or incorrect data.

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Metadata for Unstructured Data

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• Unstructured data = any data that is not in a database or data file,

including documents or other media data

• Metadata describes both structured and unstructured data• Metadata for unstructured data exists in many formats, responding

to a variety of different requirements• Examples of Metadata repositories describing unstructured data:

– Content management applications– University websites– Company intranet sites– Data archives– Electronic journals collections– Community resource lists

• Common method for classifying Metadata in unstructured sources is to describe them as descriptive metadata, structural metadata, or administrative metadata

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Metadata for Unstructured Data: Examples

Examples of descriptive

metadata:• Catalog information• Thesauri keyword terms

Examples of structural

metadata• Dublin Core• Field structures• Format (audio/visual, booklet)• Thesauri keyword labels• XML schemas

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Examples of

administrative metadata• Source(s)• Integration/update schedule• Access rights• Page relationships (e.g. site

navigational design)

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Sources of MetadataPrimary Sources:

• Virtually anything named in an organization

Secondary sources:

• Other Metadata repositories, accessed using bridge software

• CASE tools, ETL tools

Many data management tools create and use repositories for their own use.

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Specific ExampleFour metadata sources:

1.Existing reference models (i.e., ADRM)

2.Conceptual model created two years ago

3.Existing systems (to be reverse engineered)

4.Enterprise data model

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ADRM

}

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Metadata Strategy • Metadata Strategy is…

– … a statement of direction in Metadata management by the enterprise

– … a statement of intend that acts as a reference framework for the development teams

– …driven by business objectives and prioritized by the business value they bring to the organization

• Build a Metadata strategy from a set of defined components

• Primary focus of Metadata strategy: gain an understanding of and consensus on the organization’s key business drivers, issues, and information requirements for the enterprise Metadata program

• Need to understand how well the current environment meets these requirements now and in the future

• Metadata strategy objectives define the organization’s future enterprise Metadata architecture and recommend logical progression of phased implementation steps

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Metadata Strategy Implementation Phases

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Metadata Management

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Goals and Principles

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1. Provide organizational understanding of terms and usage

2. Integrate Metadata from diverse sources

3. Provide easy, integrated access to metadata

4. Ensure Metadata quality and security

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Activities1) Understand Metadata requirements

2) Define the Metadata architecture

3) Develop and maintain Metadata standards

4) Implement a managed Metadata environment

5) Create and maintain metadata

6) Integrate metadata

7) Management Metadata repositories

8) Distribute and deliver metadata

9) Query, report and analyze metadata

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Activities: Metadata Standards Types

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• Two major types exist:

1) Industry or consensus standards

2) International standards

• High level framework shows how standards are related and how they rely on each other for context and usage:

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Common Warehouse Metadata (CWM):• Specifies the interchange of Metadata among data warehousing, BI, KM,

and portal technologies.

• Based on UML and depends on it to represent object-oriented data constructs.

The CWM Metamodel

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Activities: Noteworthy Metadata Standards Types

Warehouse Process Warehouse Operation

Transformation OLAP Data Mining

Information Visualization

Business Nomenclature

Object Model Relational Record Multidimensional XML

Business Information Data Types Expression

Keys and Indexes

Type Mapping

Software Deployment

Object Model

Management

Analysis

Resource

Foundation

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Information Management Metamodel (IMM)

• Object Management Group Project to replace CWM• Concerned with:

– Business Modeling• Entity/relationship metamodel

– Technology modeling• Relational Databases• XML• LDAP

– Model Management• Traceability

– Compatibility with related models• Semantics of business vocabulary and business rules• Ontology Definition Metamodel

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The Information Management Metamodel...

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• Based on Core model.• Used to translate from one model to another.

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Primary Deliverables• Metadata repositories

• Quality metadata

• Metadata analysis

• Data lineage

• Change impact analysis

• Metadata control procedures

• Metadata models and architecture

• Metadata management operational analysis

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Roles and Responsibilities

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Suppliers:– Data Stewards– Data Architects– Data Modelers– Database Administrators– Other Data Professionals– Data Brokers– Government and Industry

Regulators

Participants:– Metadata Specialists– Data Integration Architects– Data Stewards– Data Architects and Modelers– Database Administrators– Other DM Professionals– Other IT Professionals– DM Executives– Business Users

Consumers:• Data Stewards• Data Professionals• Other IT Professionals• Knowledge Workers• Managers and Executives• Customers and Collaborators• Business Users

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Technology• Metadata repositories• Data modeling tools• Database management systems• Data integration tools• Business intelligence tools• System management tools• Object modeling tools• Process modeling tools• Report generating tools• Data quality tools• Data development and administration tools• Reference and mater data management tools

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Guiding Principles1) Establish and maintain a Metadata strategy and

appropriate policies, especially clear goals and objectives for Metadata management and usage

2) Secure sustained commitment, funding, and vocal support from senior management concerning Metadata management for the enterprise

3) Take an enterprise perspective to ensure future extensibility, but implement through iterative and incremental delivery

4) Develop a Metadata strategy before evaluating, purchasing, and installing Metadata management products

5) Create or adopt Metadata standards to ensure interoperability of Metadata across the enterprise

6) Ensure effective Metadata acquisition for internal and external metadata

7) Maximize user access since a solution that is not accessed or is under-accessed will not show business value

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Guiding Principles, cont’d

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8) Establish and maintain enterprise-wide business involvement in data stewardship, assigning accountability for metadata

9) Define and monitor procedures and processes to ensure correct policy implementation

10) Include a focus on roles, staffing, standards, procedures, training, and metrics

11) Provide dedicated Metadata experts to the project and beyond

12) Certify Metadata quality

8) Understand and communicate the necessity of Metadata and the purpose of each type of metadata; socialization of the value of Metadata will encourage business usage

9) Measure content and usage

10) Leverage XML, messaging and web services

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Using metadata descriptions of Bluetooth devices

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Attributes/FieldsKeyboard VCUMotorola S9Peter's i4Trackpad CGLTrackpad IDRTrackpad VCU

Data ColumnCGL TrackpadIDR TrackpadMotorola S9Peter's i4VCU KeyboardVCU Trackpad

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Example: iTunes Metadata

• Example: – iTunes Metadata

• Insert a recently purchased CD

• iTunes can:– Count the number

of tracks (25)

– Determine the length of each track

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• When connected to the Internet iTunes connects to the Gracenote(.com) Media Database and retrieves:– CD Name– Artist– Track Names– Genre– Artwork

• Sure would be a pain to type in all this information

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• To organize iTunes – I create a

"New Smart Playlist" for Artist's containing "Miles Davis"

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• Notice I didn't get the desired results

• I already had another Miles Davis recording in iTunes

• Must fine-tune the request to get the desired results– Album contains "The

complete birth of the cool"

• Now I can move the playlist "Miles Davis" to a folder

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• The same: – Interface– Processing– Data Structures

• are applied to – Podcasts

– Movies

– Books

– .pdf files

• Economies of scale are enormous

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Summary

6904/07/231/26/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!


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References & Recommended Reading

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References, cont’d

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7204/07/23



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7304/07/23



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Questions?

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Upcoming Events

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October Webinar:Engineering Solutions to Data Quality Challenges

October 9, 2012 @ 2:00 PM – 3:30 PM ET

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November Webinar:Get the Most Out of Your Tools:

Data Management Technologies

November 13, 2012 @ 2:00 PM – 3:30 PM ET

(11:00 AM-12:30 PM PT)

Sign up here:•www.datablueprint.com/webinar-schedule •www.Dataversity.net

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Category:	Technology
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