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SAP’s 4 keys to data governance | #MITCDOIQ

SAP’s 4 keys to data governance | #MITCDOIQ

drowning in big data tree underwaterFor some people, data governance is still a little unclear in terms of what it is and how it’s applied to the enterprise. In an interview with Dave Vellante and Jeff Kelly at this year’s MIT CDOIQ Symposium in Cambridge, MA, Tina Rosario, VP of Global Operations for SAP America, offered a brief explanation of data governance by sharing how the company manages its program through keeping things simple and working closely with data analysts.

Focusing key data and Axing the data speak


Rosario said that SAP America centers everything it does on data governance around four key capabilities:

  1. Having good organization and practices around data governance, meaning rules, standards and policies.
  2. Looking at the right engineer processes for simplifying how data is created, updated and maintained.
  3. Looking at data from an ongoing maintenance point of view and determining what the right operations and tools are to automate the maintenance of data.
  4. Having good technical and business-driven IT solutions.


With this in mind, SAP America drills down the ‘data speak’ into simple business language. This means figuring out the critical bits of information needed to run the business process and the currency of that information. Rosario said this is where the company finds out where it’s going to govern. She added that data governance is ultimately about learning what to do in order to better enable business processes to run more efficiently and how to get the data to businesses faster and with the right level of content.

Governance and Analytics


Kelly asked Rosario if there’s any tension between data governance and data analytics. She responded by saying it’s actually the opposite, and the two work very closely together.

“I think it’s our job in terms of governance and management to make sure that the data is at the right level of quality and is at the right level of standards, so the analytics people don’t have to spend time normalizing, rationalizing,” said Rosario. This makes the data easily accessible for analysts.

She described the relationship between the two as symbiotic. For example, before running a report, analytics would ask governance for access to a certain level of data and then help to ensure that it’s from the right source, at the right level of quality and also available. On the flip side, governance needs help to drive data analytics, using tools like SAP’s Information Steward to analyze the current level of data quality.

Data governance is the learning and practice of improving business processes. SAP America has been successful in doing this by following four core capabilities, keeping things simple for businesses and working closely with analytics.

See Rosario’s entire segment below:

photo credit: gideon_wright via photopin cc

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