Application Consolidation for Enterprise Applications (ERP/CRM)Rick L Miller,

WW Program Manager, IBM Information Management

Enterprise Applications Support Core Business Processes

• Highly interdependent applications

• Support critical business processes like order to cash

• Require complex deployments, data migrations and upgrades

• Inherently risky and prone to failure

SAP Component (ERP)

SAP Component (SCM)

SAP Component (BI/BW)

SAPXI/PI

ESR

SAPEnterprise

Portal

SAPMDM

The ProblemContinually growing islands of information coupled with lack of governance processes increase the challenges of consolidating applications

AIIM & Accenture Surveys, 2007

47% of users don’t have confidence

in their information

42% of managers use wrong information

at least once a week

59% of managers

missinformation

they should have used

Uncertainty Creates Risk

What are the right data sources?

What is the state of the legacy data?

Are we simply moving our data quality issues?

What legacy data is missing for the new environment?

How can we build accurate project specifications to limit risk?

How do we keep data from becoming the critical path?

Source: The Gartner Group – Corporate Integration Projects

•Data sufficient for legacy systems, but not sufficient for SAP

•Lack of documentation of existing systems

•Poor quality of data in source systems

•Multi-brand complexity

•Errors detected late in project lifecycle leading to re-work and delays

Typical Risk to Data Migration Projects

83% of migration initiatives either overrun their budgets or fail outright

Data integration activities will consist of 15 – 30% of total project spendData integration activities will consist of 15 – 30% of total project spend

Between 20 and 40% of your implementation expenses should be allocated to data migration1

1Direct from SAP’s Legacy System Migration Workbench (LSMW) manual

Data integration activities will consist of 15 – 30% of total project spendData integration activities will consist of 15 – 30% of total project spend

Data Integration/Migration Activities

30%Understanding Source Data

40%Cleaning, Standardising

Harmonizing, Management

30%Conversion, Loading,

Interfaces, Connectivity

DeliverDiscover Prepare

Largely manual effort on small percentage of data. Some manual coding can

review all data .

This effort is the most unpredictable. The work can vary greatly depending on condition of data, however it is always the largest piece of work in the data

initiative.Largely manual effort on 100% of data.

This can mean dozens of persons cleaning source systems manually to

correct and augment data and manually aligning records to MRD. Some manual

coding can reduce the manual effort.

Coding transformations and loads. Traditionally this effort

is plagued with problems related to data quality and it

can easily be pulled by necessity into the Cleaning,

Standardising and Harmonising area causing

timing and budget problems.

Data integration activities will consist of 15 – 30% of total project spendData integration activities will consist of 15 – 30% of total project spend

Data Integration/Migration Activities

30%Understanding Source Data

40%Cleaning, Standardising

Harmonizing, Management

30%Conversion, Loading,

Interfaces, Connectivity

DeliverDiscover Prepare

Largely manual effort on small percentage of data. Some manual

coding can review all data .

This effort is the most unpredictable. The work can vary greatly depending on condition of data, however it is always the largest piece of work in the data initiative.

Largely manual effort on 100% of data. This can mean dozens of persons cleaning source systems manually to correct and augment data and manually aligning records to MRD. Some manual coding can reduce the manual

effort.

Coding transformations and loads. Traditionally this effort is plagued with problems related to data quality and it

can easily be pulled by necessity into the Cleaning, Standardising and Harmonising

area causing timing and budget problems.

Reality is 70% of the activity required to be successful with the data migration occurs

here

Data integration activities will consist of 15 – 30% of total project spendData integration activities will consist of 15 – 30% of total project spend

Data Integration/Migration Activities

30%Understanding Source Data

40%Cleaning, Standardising

Harmonizing, Management

30%Conversion, Loading,

Interfaces, Connectivity

DeliverDiscover Prepare

Largely manual effort on small percentage of data. Some manual

coding can review all data .

This effort is the most unpredictable. The work can vary greatly depending on condition of data, however it is always the largest piece of work in the data initiative.

Largely manual effort on 100% of data. This can mean dozens of persons cleaning source systems manually to correct and augment data and manually aligning records to MRD. Some manual coding can reduce the manual

effort.

Coding transformations and loads. Traditionally this effort is plagued with problems related to data quality and it

can easily be pulled by necessity into the Cleaning, Standardising and Harmonising

area causing timing and budget problems.

Estimates are often basedon this

Data integration activities will consist of 15 – 30% of total project spendData integration activities will consist of 15 – 30% of total project spend

Data Integration/Migration Activities

30%Understanding Source Data

40%Cleaning, Standardising

Harmonizing, Management

30%Conversion, Loading,

Interfaces, Connectivity

DeliverDiscover Prepare

.75% Business 50% Business 25% Business

75% IT50% IT25% IT

There is a more significant involvement by the business users in the up front activities. Not understanding the roles and responsibilities required can negatively impact the project.

Data Quality Issues/Risks

Poor quality data from source systems

Duplicate data

Irrelevant data

Embedded business rules

Data sufficient for legacy systems not sufficient for ERP

Data gaps require augmentation strategies

Error detection and reprocessing is iterative and time consuming

Critical data issues often are not found until late in Realization and Final Prep

Projects delayed due to data quality problems

Inability to load data

Data attributes insufficient to execute cross functional business processes

Multiple change requests required to deal with data issues

Decisions to “go with what we have” and clean up the mess later

Data migration issues lead to Inaccurate reporting

Customer satisfaction suffers

Poor system adoption

Incorrect Data Loaded Correctly into SAPCustomer Master Data

Duplicate data

Irrelevant data

Lack of addressvalidation or verification

Vendor

Customer

G/L G/L Account

Material

SAP Master Data in

Business Flow

SalesOrder

MRP

PlanningMPS

PlannedOrder

PurchaseRequisition

ProductionOrder

Delivery BillingCustomerPayment

PurchaseOrder

InvoiceReceipt

VendorPayment

GoodsIssue

GoodsReceipt

GoodsReceipt

GoodsIssue

ProductCosting

ProfitabilityAnalysis

Raw Finished

SAP Integration Requires High Quality Data

Project Lifecycle

Traditional Approach

InfoSphere Approach

70

50

30

10

90

110

Benefits of This Approach

► Reduce risk – surface and address issues early

► Maximize value from Subject Matter Expert resources

► Lower cost than traditional approach

Analysis Mapping SystemTest

Load PostCutover

Relative $cost

of fixing error/issue

RelativeEffort

Best Solution Is To Fix Problems Early Find problems early, reduce pain (risk, $, time) later

Application Consolidation with InfoSphereBenefits vs. Hand-coding and services-only solutions

Discover

Implement and Monitor

Deliver

Prepare50 – 90% Savings 50 – 90%

Savings 40 - 60% Savings

Benefits45 to 80% of total integration cost savings in the initial project

Ensures trust for timely Go-Live and Production Deployments

>75% ongoing project savings for new implementations/waves

Ensures lasting data quality

73 – 90%Savings

Enabling Trusted Information

Analyze Source Systems Before Blueprint• Eliminates surprises• Provides an objective basis for planning• Minimizes re-work by increasing accuracy of specifications• Identifies data strengths and weaknesses• Quantifies data quality impacts pre-transformation

1 Discover

Employ Ongoing Data Quality Process• Monitor critical business rules• Trend data quality over time to identify

problem areas• Assess Data Quality periodically

5 SupportApply to Blueprint Design Workshops• Provides enterprise standardization• Identify matched records across or within sources• Survive the best possible record sets for target• Analyze free form text and split domains• Identify embedded business logic• Apply data correction and augmentation

2Design

Maintain Data Quality Through Controls• Apply end user controls for standardization,

validation and augmentation• Apply standardization, validation and augmentation

in master data synchronization strategy• Apply standardization, validation, augmentation to

operational interfaces

4 GovernanceApply Results to Realization of DataIntegration• Provides transformation logic (mapping tables)• Identifies faulty logic & aids acceptance testing• Captured business rules for reuse• Apply data correction and validation in Batch

and Real Time

3Delivery

SAP Provisioning Environment

DATA HARMONIZATION

LEGACY SOURCES

DATA ALIGNMENT

STAGING AREA

PROVISIONINGAREA

ALIGNMENTAREA

GAP REPORTS

DATA EXTRACTION

IDA, DS IA, QS

Cognos

FT, DS

QS, DS

BG, MWB

Substantial acceleration for SAP migrations & consolidations

• CMC needed a single view of its customers to understand their business across all divisions.

• CMC was unable to rapidly and dynamically analyze their global manufacturing capacities. As a result, they could not make optimal decisions on where to fulfill demand or take advantage of manufacturing techniques such as Global Available-to-Promise (ATP).

• CMC needed to adequately leverage global spend across all divisions with their suppliers.

Challenge

CMC has invested in SAP software to help them create common business processes and reduce the total cost of ownership of their information technology assets.

CMC purchased IBM Information Server to rationalize the data that will be loaded into SAP and create a common view of data globally.

InfoSphere Solution

Benefits

Merged divisional silos of data into a single view of customer.

CMC will use IBM Information Server to rationalize the data being migrated from legacy systems into SAP.

Eliminated manual processes, allowing CMC to utilize IBM Information Server coupled with the IBM data integration methodology to accelerate and standardize the migration process

Completed first 2 migrations on-schedule. Aggressive 3 month rapid migration cycle through 2010

Commercial Metals CompanyGlobal Consolidation of 200+ locations into a single SAP Instance

ERP Data Integration Realities

Moving to ERP will not make data quality issues go away, it will expose them

ERP will allow data to be loaded that is either not relevant or will load, but is not clean

As data is loaded into ERP extensive validations are performed (for example a full Customer Master object has over 400 validations)

ETL tools alone do not provide support for all necessary data integration activities. This is more than a mapping activity.

Legacy data must be recast into the ERP load formats and only have a fraction of required attributes to run ERP business processes

Project milestones are often delayed due to ERP validation errors. This represents a lot of extra development time and project risk

Loading data correctly into ERP is not the big issue, loading correct data correctly into ERP is the real issue

Accurate Assessment & Design of Business Processes

Accelerated Data Migration

Ensuring on-time Go-Live’s

Repeatable & reusable infrastructure foron-going legacy system interfaces

Enforced Data Quality & Governance

Establishing Enterprise Master Data integration

InfoSphere Benefits in an SAP Enterprise

Enhancing the bottom line by simplifying & reducing implementation cycles and ensuring sustained value

Thank you!

For more information please visit:

http://www.ibm.com/software/data/infosphere/