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Enterprise data analytics and the governance federated collaboration oxymoron

Editor's note: Peter Kapur will speak on the topic "Governance 2.0: Best Practices to Operationaliza Your Data Strategy," at the MDM & Data Governance Summit, hosted by Information Management, July 10-12, 2019 in Chicago.

The famous criminal Willie Sutton was once asked why he robbed banks, and his response was simple: Because that's where the money is.

Data analytics, in different forms, is an important driver for companies to monetize their investments that sit across various data silos. For analytics to be successful, you need data along with some critical components which include business drivers, data context, data quality, data process transformation and a data culture.

Data governance is a critical component to build collaboration across lines of business and corporate and to leverage an organization’s existing data assets and human capital. But enterprise governance has too often been approached as a response to regulators since the financial crisis and become an end in itself versus a means to an end. That means perpetuating what I term “the right of men to have babies,” which to me is analogous to an enterprise determining how data should be governed without a goal of critical business drivers or support of existing data producers and consumers.

In that situation, data analytics happens in a silo without involving SMEs and producers who can be critical partners.

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The solution calls for a business-driven governance focused on data analytics and a federated collaboration, to ensure that you are bringing the data producers and consumers into the analytics data universe as partners to share in the journey and success.

This approach:

  1. Blends the right mix of data strategy and execution for business initiatives (“data stratecution”) for operationalization of data analytics to just enough data governance, metadata and architecture aligned with privacy and data risk.
  2. Focused on delivering business value in partnership with data sponsors, producers and consumers. It does away with a monolithic top-down approach by taking advantage of existing human capital, governance and tools in an organization.
  3. Creates a trusted partnership across data producers and consumers with a goal to create a holistic eco-system from existing silos, in a win-win relationship.
  4. Leverages a “building blocks” approach to create enterprise assets by supporting initiatives at lines of business with transparency and alignment to enterprise - focusing on meeting the needs of the enterprise as well as members in discovering organizational data assets for cross-functional use
  5. Supports data transparency with data producers and consumers into a jointly owned active data catalog
  6. Defines “implementable” best practices and standards for managing critical data assets.
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