Optimize Data Management in All Phases of Your SAP Implementation
- by Gerry Miller, Retired Principal
- Darwin Deano, Senior Manager
- Lindsey Berckman, Consultant, Deloitte Consulting LLP
- June 4, 2010
Poor data management can lead to many problems, from incorrect data, to a lack of integration, to bad analysis. Learn ways an effective data management strategy can help you in your efforts to optimize your system’s performance while increasing your compliance and reducing cost.
Data management is a critical component of running a highly effective business and must be considered in all phases of any major SAP implementation or transformation. Effectively employed, it should encompass all aspects of data creation, including establishing application master data, ascertaining data ownership, defining and enforcing data management policies, data cleansing and conversions, data governance, and the eventual storage of data that is no longer needed for daily business transactions.
It is becoming increasingly apparent to us that effective data management is critical to day-to-day business operations and a key component of a successful operating model. In reality, data affects every single business activity including product and service quality, profitability, business partner satisfaction, financial compliance, validity of business analytics, and end-user experience. Data management preparations should be paramount in all phases of an implementation life cycle and, therefore, are deserving of a closer look.
The Data Management Imperative
In recent years, the amount of data that many organizations need to manage has grown exponentially, especially with globalization efforts and an increase in mergers and acquisitions across business units. Regulatory trends, particularly in the areas of financial compliance and privacy protection of personally identifiable information, have increased the stakes for managing data effectively. In addition, the trend in packaged enterprise applications (e.g., SAP ERP) is for configuration and analytical responsibilities increasingly to shift to business process owners. The result is a dynamic business application, a software system that is:
- Representative of a business process
- Built for constant change
- Adaptable to business context
- Information rich
This is a positive development in terms of enabling business innovation, but it makes the management and control of data more complex. As a result, businesses need to handle many issues when dealing with the management of data. Typical issues to address include:
- No consolidated view of master data is available across the enterprise
- Inconsistent and duplicate master data exists across multiple applications
- Master data is distributed throughout the enterprise using custom interfaces or manual procedures
- The cost of master data ownership is unknown or unacceptably high
- Redundant data management processes increase the cost of regulatory compliance
- Each application has its own local data governance procedures
- Lack of data archiving causes production instances to grow, decreasing their performance and increasing IT maintenance and infrastructure costs
- The time and cost associated with performing backup, recovery, replication, and upgrades is extremely high
- Decisions are based on incorrect data
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