Avoiding the Data Governance Gap

Since 1999, David Loshin, president of Knowledge Integrity, has developed technical and management methodologies for instituting data quality, master data management, data standards, and data governance in many different industries, including financial services, banking, insurance, health care, manufacturing, pharmaceuticals, and government agencies. Please note that the views of our guest bloggers do not necessarily reflect the views of Kalido.

There has been concern among senior managers at many organizations that source data do not fully meet the requirements of enterprise business applications, or worse, that there few or no processes have been put in place to even connect the two. In turn, this has been a significant driver to undertake data governance initiatives. Data governance programs typically begin with the formation of a data governance council. While this seems to be the logical starting point, efficacy, participation, and management support can wane in the absence of well-defined policies and processes. I refer to this as a “data governance gap” – the delta between intention and action – and it occurs when the prerequisites for data governance policies, practices, and procedures are not established prior to the creation of the council.

The data governance gap can be avoided if the data governance council’s first order of business is understanding and using best practices for managing data policies and operationalizing data governance processes. Collaboratively defining data policies in the context of current state business process, data, system and organization models, and aligning them with business objectives is essential to implementation success. This process can be accelerated by leveraging existing investments in data models, process models, data quality rules and related software through an open framework. If necessary, external advisors can be engaged to help draft an initial set of data policies.

Operationalizing data governance processes involves orchestrating comprehensive data governance processes such as data policy creation, change management, communication, implementation, issue tracking, and remediation. Data governance program performance can be measured and improved by tracking and reporting key operational metrics relating both to the data and those held responsible and accountable for those data.

Lastly, repeatable processes and executional rigor can ensure broad compliance with defined data policies that are correlated to business information requirements.

For more of my perspective on this topic, download the Knowledge Integrity white paper “Operationalizing Data Governance Through Data Policy Management” and watch my video podcast.

David Loshin, president of Knowledge Integrity, Inc, is a recognized thought leader and expert consultant in the areas of data quality, master data management, and business intelligence. David is a prolific author regarding BI best practices, via the expert channel at www.b-eye-network.com and numerous books and papers on BI and data quality. Please note that the views of our guest bloggers do not necessarily reflect the views of Kalido.
4 replies
  1. Ellie K-W
    Ellie K-W says:

    The data governance function is not a revenue generator (excluding organizations whose business IS data governance, of course). All will concur that well-defined data policies including accuracy standards and metrics to monitor compliance with such standards is a worthwhile endeavor. Unfortunately, the benefits of data governance are difficult to quantify. So when push comes to shove, resources and priority will be granted to nearly every other area, be it marketing, operations, finance or facilities rather than data governance. Until something goes wrong.

    Data governance CAN and WILL be effective, however, if there is consistent high-level management support which is communicated and re-emphasized to all, particularly in the beginning.

    Thank you for posting about this more organizational behavior aspect to effective data governance, as it is sometimes equally important as methodology!

  2. Winston Chen
    Winston Chen says:

    Hi Ellie,
    In order to get data governance programs funded, we need to take a stab at quantifying its benefits. This is a useful exercise, not only for funding, but also for building the discipline to align data policies with business objectives.
    Most data related initiatives generally start with data, and then identify business processes that benefit from the data. Say, starting with customer data, and then find the benefits in reducing cost of sales. What if we flip it around? Let’s start with the objective of reducing cost of sales, and the identify what data, and what policies for the data, can reduce cost of sales.
    Once we can associate data policies with business objectives, it’ll become much easier to quantify benefits. How many less hours the sales organization can save by reduce customer data duplication by 2%? The math gets easier and more supportable.
    I wrote a blog on this topic: Building a Business Case for Data Governance, http://blog.kalido.com/building-a-business-case-for-data-governance/

    • Ellie K-W
      Ellie K-W says:

      Winston, thank you so much for responding to my bit-of-rant. I am so very pleased to know that data quality professionals have been putting some thought into quantifying the benefits of Data Governance.

      I was a manager of Data Governance until mid-2007, and it sounds as though it isn’t such a new concept anymore, that issues such as these are being addressed by many individuals. I look forward to reading the case study from Kalido that you included.

      Again, I really do appreciate your comments and pointer toward more a detailed information source for me!

Trackbacks & Pingbacks

  1. […] my previous blog, I discussed the “Data Governance Gap” and how to avoid it. I highlighted the importance of understanding and using best practices for […]

Leave a Reply

Want to join the discussion?
Feel free to contribute!

Leave a Reply