Big Data Quality: Certify or Govern?

We've been having an intersting conversation with clients and internally about the baggage associated with Data Governance.  As much as we (the data people) try, the business thinks it is a necessary, but the commitment, participation, and application of it is considered a burden worth avoiding.  They wonder, "Is this really helping me?"  Even CIOs roll their eyes and have to be chased down when the data governance topic comes up.  They can't even sell it to the business.  

So, the question came up - Do we need to rebrand this? Or worse, do you abandon data governance?

Well, I don't know that I'm convinced that Data Governance needs a new name or brand.  And, with regulatory and security risks it can't be abandoned.  However, what organizaitons need is a framework that is business oriented, not data oriented.  Today, Data Governance is still stuck in the data, even with strong business participation.

Big data is the catalyst.  If you thought your data was challenging before, chaos and messiness takes on a whole other meaning with big data.  Scale now forces us to rethink what we govern, how we govern, and yes, if we govern.  This is to both better manage and govern process-wise, but it also drives us to ask the questions we didn't ask before. Questions about meeting expectations for data over meeting expectations to fit data into systems.

What this means...orient data governance toward data certification.

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Why Every Data Architect Should Be An Analyst First

“Context” is the new buzz-word for data.  Jeffery Hammond talks about it in Systems Of Automation Will Enrich Customer Engagement, Robert Scoble and Shel Israel talk about it in their book “Age of Context”, and you can’t ignore it when it comes to a discussion for Cognitive Computing and the Internet of Things.  We’ve live in a world where data was rationalized, structured, and put into standardized single definition models.  The world was logical.  Today, we live in a world where the digital revolution has introduced context, the semantic language of data, and it has disrupted how we manage data. 

Big data technologies were created not because of volume and cost.  They were created to manage the multi-faceted model that data takes on when you have to link it to how regular consumers and business people see the world.  Performance and cost are only factors that had to be considered to scale in order to support the objective.  Search, recommendations, personalized web experiences, and next best action could not be possible in a structured single definition environment.  Why we know this is that the sculpted purpose built environments that supporting business applications collapsed when analytics to discover causation in relationships and correlations at scale was applied.

That is the tipping point for data architects.

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