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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/