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ASUG and SAPPHIREThis guest blog post was written by Tina Rosario and Maria Villar. This was based on their ASUG presentation.

Lessons from SAP

Launch of CDM

Customer Data Management (CDM) came into sharp focus at SAP as the data management topic was launched as a board sponsored program in 2008.  To support the CDM program a cross line of business leadership team was established and a multi-year business case and plan was developed.  CDM’s overall purpose was to address highest priority customer master data issues.  Improvement opportunities existed from both short and long term perspectives.   The team set about to fix the immediate issues such as getting the “basics” in place.  In addition, long term solutions included the design and build of an operational customer data management solution.   The successful program spanned a year with staff of 6 full time resources supported by an extended team of subject matter experts from the business and IT.

CDM program scope and focus

Very quickly the CDM team realized that to be successful scope definition and control was required as well as a sharp focus on the most important customer master data issues.  It was apparent that customer data had not received attention over the years. Without governance and quality management many data challenges existed.  The CDM team explored the issues in detail, from an outside in perspective, and found that 20% of the master data elements were delivering the most value to SAP.  That input was used to narrow the scope and evolved into a guiding principle to “get those right”.  To address the longer term root cause information governance issues, master data accountability and standard definitions were included in scope.  

CDM approach overview

Given the CDM scope and the broad nature of the requirements, the program methodology reflected a phased rollout of global data standards which were tested via a pilot and then rolled out to a broader audience. 

Country pilots of global data standards and ownership model (for the most critical elements of customer master data) was successful in delivering business benefits, particularly in the sales and marketing areas.  Data quality reporting and cleansing services leveraged SAP’s EIM suite and clearly demonstrated that the global CDM approach would successfully operate within a regional model.  

The pilots also served to validate the design of the future state data organization and transition plan which reflected a phased rollout to other areas and lines of business within SAP.  For example, the CDM program leadership evolved to form a Global Data Council, consisting of the newly appointed data leads within each line of business many of whom were directly involved in the pilot launch.  

Part of the operational plan also included ongoing data cleansing and resolving the priority customer master data issues.  That scope ran parallel to the establishment of the data management organization which was being established.  The tactical data quality cleansing enabled the program to stay on track and deliver business benefits in the shorter term while building the foundation for longer term data management solutions. 

The successful pilots and data quality improvements enabled the longer term CDM vision to move forward.  Upon program closure in Q1 2010, the team executed a smooth 4 month transition to the operational  “run” state.

SAP's current CDM organization 

Fast forward to today as the transition is complete with Maria now leading  SAP’s  operational global data management team.  Maria wears “two hats”, leading both the global data management across the lines of business as well as detailed management within the global field organization.  She is responsible for driving both the global master data strategy and the sales line of business data strategy.   There’s also a close collaboration with the business process owners and a continuation of the data standards and governance established by the CDM program.

Cross LOB governance model

The global data management organization extends to all lines of business (LOB) which have identified data leads who are tasked with driving tactical and strategic data programs.  The LOBs are also responsible for delivering business process engineering and ensuring information governance and accountability is enforced throughout their organization. 

To support the cross-LOB collaboration a Global Data Council was established as a vehicle for the business leads, along with IT and others, to work together to address common data issues and foster cross-LOB alignment.  For example, the Council handles strategic functions such as defining information governance requirements, as well as having direct responsibility for business rollout/adoption.  The council also prioritizes and manages the IT portfolio for the data tool and technology changes , providing one business voice to IT for master customer data related IT projects 

The Council is supported by a cross-LOB Executive Data Steering Committee which provides oversight and decision support.  Executive engagement is a critical success factor for the master data management approach as their attention to the topic results in budget and resource allocation to the data programs.  

Regional data centerswill support process execution

One additional operational component established this year is the development of a Regional Data Management Center model.  Each region now has a team of skilled data resources who focus on delivering improvements to core master data such as enriching customer records, creating master data and updating account assignments.  The regional centers currently support marketing and sales data requirements but will soon expand to support other LOBs such as Finance and Customer Support.

These centers not only provide data services but they are also engaged in driving quality process improvements via best practices and tools.  The DMCs have the master data knowledge and are best positioned to best deliver best practice solutions.  For example, business partner creation processes can be developed and tested by the global data team and once proven can then be enabled via the regional data operations team.   The Global data management team, under Maria, provides the DMCs with common global tools, processes, resources.  They have established global KPIs by which to measure the DMC’s effectiveness.  

2011 areas of improvement

SAP’s global data quality program is measured on both specific KPIs such as reducing duplicates but also process improvements such as reduced data creation time.   KPIs are tracked with data quality reports which use EIM tools such as SAP BusinessObjects Explorer and Dashboard Design (formerly Xcelsius).  These online reports measure both regional and global data improvements and are tailored for the specific audience (e.g. executives vs. data managers) with drill down capabilities. 

Process improvements contribute to overall data management business case in a significant way, particularly in areas that impact the bottom line such as days sales outstanding.  These metrics are tracked by the global data team and are reporting to the executives through dashboards and scorecards. 

Tracking of these improvements is formalized into data quality readouts to the Executive Data Steering committee.  We are now also tracking specific line of business contributions via a holistic scorecard that monitors each line of business’ support of the data portfolio and helps provide a complete picture of all contributions to master data quality.  

Data management technology vision 

Technology and tools are critical enablers of SAP’s data strategy and will allow us to more quickly scale and address additional master data requirements.  The vision or roadmap centers on a partnership with IT to deliver solutions that align with best in class technology capabilities.  For example, the roadmap reflects the use of tools to centralized workflow and business rules for easier management and control.  Data models and management tools will help us diagnose and automate changes as needed for more active governance.   Analytics and dashboard tools are required for transparency to all of the processes.  Thus, there’s a significant amount of funding allocated to a multiyear portfolio of IT projects to deliver Information management tools required by the business and prioritized by the Global Data Council.   We use SAP technology from the EIM product portfolio, as well as the SAP business suite.  SAP uses  SAP !! We  partner with the product develop team to provide our feedback  on future and current products .

EIM success factors

While information governance programs are tailored to a company’s Information Maturity,  company pain points  and culture,  there are many common success factors.  First, the company has to be ready for a top’s down enterprise governance approach where all functions participate, collaborate and play a part.  Business sponsorship is also key- the higher in the organization the better as roadblocks will be encountered that will need to be resolved . Funding is another  EIM success factors.  Perhaps even more important is having the right visibility on the required data capabilities along with business sponsorship and readiness for successful change management.   Data tool delivery goes hand in hand with having an organization in place to best leverage the automation with the right people and skills.

What does great data management look like?

Through the journey from focused program to creating a data management business capability , we have defined a few key practices for “great” data management at SAP.  It’s not just about creating roles and building work teams but data management must become an integral part of the organization with cross business decision making forums who will actively engage in driving accountability. The organization must ensure  the data programs enable business goals and don’t exist just for the sake of creating “good” data.  Great data management is embedded as a core part of the business and IT process so that whenever data is created, changed or updated and information governance is clearly defined and easy to follow.  This requires engagement from business leaders and IT leaders with simple language and clear accountability. Data quality is not just an organization practice but extends to trusted sources of data as those sources should adhere to the same standards.  These lessons learned have helped shape SAP’s data management organization; our priorities reflect a desire to achieve high levels of data confidence across the company.

For more details, attend a live webcast from Maria and Tina, hosted by ASUG. You can register for this webcast if you are not an ASUG member, but need to register via this link.

Related blogs on SAP’s Data Governance program:

Information Governance Maturity Models: Quick and Easy

Using the Program Management Office to help your initiative

One SAP for Data Quality: Building an Operational Data Management Organization

Information Governance Tips + Tricks from a Practitioner

Creating a dataculture in your company: What you should know about Information Governance you learned in pre-school

Walking the Walk: SAP and Information Governance

Information Steward 4.2 in Practice: How SAP’s Data Management Organization Uses Information Steward

SAP’s Internal Information Governance Program: Business Value Metrics Framework

Avoiding data corruption during mergers and acquisitions: A story from SAP’s Data Governance Organization

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