Data Strategy with SAP - Data Governance

[pbaumann]

Currently I see many topics in my projects, where the right Data Governance would do a good job. So in this blog today, there are no fancy graphics or a to clear structure as I mainly write down things just going through my head at the moment. Happy to share them with you and possibly get into discussion about.

 

It is always helpful to start with a definition:

Data governance is the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption and control of data and analytics.

Source: Gartner, 2023

While I would say Data Governance is not just about data and analytics, it becomes typically pretty relevant in this area. If you have a data strategy in place and there is no word about Data Governance, you finally have NO data strategy.

As most tech people (I assume you are more tech than business here) are more interested in tools than processes and organization, let's start with the tool side.

 

In Data Governance we find typically these solutions:

• Data Quality Management - e. g. SAP Information Steward


• Master Data Management - e. g. SAP Master Data Governance


• Metadata Management - e. g. Data Catalog in SAP Datasphere or SAP Data Intelligence but also technical solutions like SAP PowerDesigner


• Business Glossary Management - e. g. often also connected to Data Catalog solutions but for SAP context bluetelligence Enterprise Glossary is also a good example


• Data Catalog Management - see Metadata Management and Business Glossary Management


• Data Lineage Management - e. g. Manta but can also be found in the context of Data Catalog solutions, strongly connected with Data Lineage is Impact Analysis

 

Now, let's come to the motivation. A lot of companies are aware, that Data Governance is important. Typically they faced some situations where they learned that one or the other solution categories listed here, make sense because of security breaches, bad decisions based on poor data quality, long development processed for data and analytics projects due to missing transparency of data, long discussions about specific meaning of an KPI of why we have so much different KPI values with the same name. And at the latest if you hear yourself askting "Who is responsible for … requirements, implementation, data assets, data protection, data quality, KPI definitions" often - Data Governance is there for you!

 

Some further reasons for Data Governance are:

• If a regulatory compliance is very important, you should already have a good Data Governance in place.


• Decentralization and more and more autonomuous functional areas and business units additionaly drive the need for a federated governance.


• You want to foster Self-Service BI, Data Literacy and Data Democratization in general - lead soon to chaos, high costs and bad decisions without a good Data Governance strategy and strong principles how to handle data within your company.

 

Maybe these challenges make clear, Data Governance makes sense and, done right, saves a lot of money or even helps earning more money e. g. via a better customer satisfaction. A data-driven company runs on the right Data Governance and is unthinkable without. To find the right balance and way is the art to master as there is not the one way.

 

So far, we see we should care about Data Governance. But why are many companies do not? Indeed there are several reasons, why it can be a little bit complicated:

• There is possibly no initial value - Who pays for a project (while Data Governance shouldn't be just a project), if no direct value can be seen? Most company just start after something happens with a high impact on business.


• It is driven by IT only or tool-oriented ("let's buy xyz for data quality" or "a data catalog for being data driven") - Business involvement is key and many IT-driven initiatives die very fast, if you can not find together.


• It is driven by business only - Business department often starts from their perspective, but often miss a holistic view and are not aware, what is possible to do with technology today.


• You want it all and you want it now - Boiling the ocean kills many initiatives. You are maybe motivated doing the right thing, but doing to much and to fast, so that your organization can not follow and typically it gets to expensive.

 

Some recommendations from my experience:

• Think big, start small - Identify important data domains for your business (often customer or product) and start here. Learn during the way.


• See the change necessary and take the people with you - You will need new roles, new processes, the right tools and the right collaboration model between IT and business.


• Identify the right use cases and the value behind - Start with the most valuable first and build lighthouses.


• Clarify the mandat - If you have to convince all the people, you will need forever to establish a Data Governance. To rule them all on the other side, is typically also not the way and leads to low acceptance and resistance.

 

Many things can be said about Data Governance. It is worth to care about if you want to create value from your data. To do the right things the right way is  important. 

What is your experience with Data Governance? Do you care about?