Inside Data Governance: Part 1 | An Introduction

Data Governance is an important first step in a project

Part 1 | An Introduction to Data Governance and Why You Can’t Ignore It

As part of my role at itelligence, I regularly meet people at various levels in organizations, and always seem to get the same questions with regard to data governance and migration.

  • Do I really need to think about data governance right now?
  • When is a good time to start the governance conversations?
  • How do we setup data governance?
  • Do I need a tool to support 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 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 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 work flows 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, establish 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 governance setup:

  • Build a business case for governance and get upper management 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

Learn More About Data Governance

Want to learn more about how to manage your Master Data? Register today for a replay of our webinar and it.mds demonstration: Managing SAP Master Data in One Simplified Solution. You will gain an insight into how it.mds can make your master data business oriented, provide better governance throughout your business, and deliver greater compliance through business-driven workflows.

For more information on it.mds, please visit the itelligence Addstore.

In part two of this series, we will look at different data governance models and what size and structure of businesses they best fit.

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