Moving from Data Governance 1.0 to 2.0

FEA02x - edited feature imageJack was right in the movie A Few Good Men – It is not easy to understand that a practice that we have been practicing for decades needs to face the truth.  It is evident that the classic approach formulated as a result of the 2008 crisis is in a crisis, so much that people are trying to re-name it.

Several practitioners like Bob Seiner and myself have tried to distinguish our approach (“Non-invasive”, “Federated”, “Business-driven”, etc.).  For many business, Data Governance 1.0 has become an item to avoid as it is too theoretical and does not bring tangible value.

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What went wrong with Data Governance 1.0 and what is Data Governance 2.0?  To be fair, the components of Data Governance continued to be used in innovation in areas such as Data Science, Analytics, etc. But is there a dissonance between the “Enterprise/Establishment” view and the budding minority?

Yet, everyone agrees that Data Governance components (e.g. Stewardship, Lineage, Data Quality) are needed to solve and go beyond a business unit narrow view, as well as unlock the Strategic value from taking of Data as an organizational asset.   Organizations must embrace Data Governance 2.0 which focuses on operationalization and solving Business Problems.

Along with that, the classic concept of the Data Governance Council must change to ensure that the view of practitioners is brought in for sustainability.   Data Governance components must move from a theoretical best practice to solving problems for the business in order to bring Data producers and Data consumers into the Eco-System as partners.   You can’t connect Metadata if you don’t have access to it.   Frameworks are important but Data Governance 2.0 is about understanding the business problem and using components of Data Governance to support the solution and not apply a one-size-fits-all approach, which gives the perception of theoretical.


What are your thoughts about Data Governance transitioning from 1.0 to 2.0? Feel free to leave your comments below. Thank you for being a part of the community.


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About Peter Kapur

Peter Kapur is a Data Governance, Metadata and Data Stewardship industry visionary. As a Data leader and speaker in the industry, Peter drives the industry towards continuous innovation and evolution of data governance as an enabler of business initiatives for operational efficiency, regulatory compliance and revenue optimization. Peter has more than 25 years in Data leadership positions in AIG Enterprise Data Management, DTCC Data Solutions, Credit Suisse Private Wealth Data, Goldman Sachs Asset Management Data and Deutsche Bank’s Common Data/Common Services.

  • Charles Harbour

    You to demonstrate value from the business side. While IT can easily see the value of cleaning data at the source, that takes time away from business people, who don’t see the impact of dirty data. Sometimes, this is easily quantifiable (Data not available at 8am for the sales team because the IT staff is still working on the batch file from last night – what was the loss of sales today) – other times, not so much.

    My experience is that it doesn’t have to be quantifiable, but it must be described in a way that pulls the business management and end users into the conversation – how does this benefit the company? How does this benefit my team? What could I be doing with my time if I wasn’t fixing/hunting/cleaning/validating data? Help draw out those conversations and work together to come up with shared solutions.

    If you don’t have that buy-in, you will be part of that 90% failure rate. Grow your own perspective – learn to be a better storyteller. Strive to see the business perspective – sit with their users, feel their pain directly. Then come up with an approach that meets both of your needs. Focus on the big stuff, but do it in an incremental, continuously improving approach.

    Does anybody have any success stories they’d like to share that highlight this approach? How have you been successful, so that all of us can get better at this?


  • yowsa

    In general if you can connect your inputs to someone else’s outputs, you can make the case that they need to take more care because its causing you pain.

    Part of the problem with modern enterprise governance is this just isn’t done. But I think in many cases there’s so many steps its difficult to explain the connection – modern data architectures intentionally decouple inputs and outputs into so many layers the connections appear tenuous.

    We need to start seeing the DAGs connecting sources to reports. Compartmentalizing data into layers has created complacency.

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