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Our current generation is going thru an interesting transition from an analog decision making to a data driven intelligence. A shift from data to information that can be easily interpreted by machines and is actionable. Where do we start  start the journey of augmented intelligence. By embracing SAP S4HANA as your digital core, you have already made your first step.

This is the right moment to start laying foundation for to start laying foundations on the data front to ensure consistency of data to support the transition. You start by asking these questions on data governance

  • I really need to think about data governance right now?

  • When is a good time to start the governance conversation?

  • How do we setup data governance?

  • Do I need a tool to support data governance?

Often people believe that they don't have to think about data governance during the implementation stage and assume they can worry about it "later". Fast forward two years, and more frequently than not, the same set of complaints roll in:

  • My reports are inconsistent! My master data is not coded correctly.

  • I don't know why I need to fill this field, the original BPO is no longer working with the organization.

  • I do not understand what the value in this field means.

  • We need to start a data cleanup project.

  • This acquisition messed up everything...

  • Why do we have so many duplicates? How can we consolidate?

This is the point when companies typically embark on massive data cleanup projects, only to realize that they should have thought about data governance earlier.


What is Data Governance?

Data governance is a cross-functional framework that governs the process of data creation, maintenance and the purging of data. Many people incorrectly believe that no extra effort is needed to put data governance in place if they already have rules for data maintenance in place. However, just having a rule set for data maintenance is not sufficient. In order to have an effective data governance framework you need

  • Procedures and tools for master data maintenance

  • Documented processes, including workflows and approval processes

  • A documented rule set for master data creation for various scenarios and exceptions

  • Ownership of data to a field or field group level

  • Security to ensure that the right person is doing the job

  • Processes to ensure the completeness of data

  • Processes to ensure consistency in the creation of data

  • In complex multi-system landscapes, established the master system that owns creation

  • Continuous improvement  of processes

  • Periodic audits to ensure adherence to the rule set and to also look for opportunities for improvement


Prerequisites for Data Governance

There is never a better time down the road to start working on data governance. The best time to start setting this up is during the initial project implementation, as this ensures you have a fully functional governance framework from day one of your go-live. Before you jump into details, there are some prerequisites that can ensure a sustainable data governance setup:

  • Build a business case for data governance and your get upper management team’s support

  • Determine the cost of bad data for your organization, and see if this justifies an investment in master data governance tools that can automate the processes

  • Identify a leader who can lead the governance organization and can set up an effective framework

  • Identify the data governance model that best suites your organization

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