Data governance is nothing without data quality


When you spend long enough writing and working in any industry, you inevitably see trends emerge and reach varying levels of maturity. Data governance is one such trend, as you can see from the following Google Trends chart:DG Growth

When I started writing about data quality best practices back in 2008, it's fair to say that data governance was barely mentioned in corporate circles. My first introduction to data governance probably came from the Data Governance Institute created by Gwen Thomas. Then over time – slowly, as the chart above demonstrates – we witnessed the rise of data governance as a discipline. Now, of course, it is far more than a discipline. It is an entire industry with a whole technology, events, training and career ecosystem emerging.

It would be easy to think that data governance has somehow superseded or overtaken data quality in importance. So, how does data quality fare in the eyes of Google Search?:

DQ Trend

To some, it would appear that data quality as a discipline is on the slide. Some might say it's had its day and is being usurped by data governance, perhaps. But we need to overlay the charts to show a more accurate view of search behavior:

DQ Trend

Now we get a clearer picture.

Yes, data governance is growing in search behavior – but the search traffic is still below that of data quality.

Will data governance overtake data quality? Yes, possibly. But another thing to consider is that as more organizations begin to implement data governance, what is the first obstacle they will have to overcome? A constant need for robust data quality management.

I see data governance and data quality management in simple terms. I've used the HR analogy in the past. But I think a military analogy works well regarding the link between data governance and data quality.

At the top of the hierarchy you have the generals and chief of staff – your directors and executives. Down at the bottom of the hierarchy are your frontline troops – the privates, sergeants and corporals that are far more likely to see action than the brigadier generals. When you apply data governance and data quality to this analogy, it becomes easier to see how the two disciplines operate.

At the top of the military hierarchy are decisions on strategy, frameworks, planning, mission and vision of the whole structure. These commands then flow down through the structure into clear objectives for action.

The same applies to data governance and data quality.

Data governance leaders set the mission and objectives for data across the organization (which is why they increasingly report into the newly created CDO roles). They decide what policies need to be enforced, what equipment should be standardized and a whole rack of other strategic initiatives.

Data quality is where the "rubber hits the road." It's where the policies of data governance are delivered to every corner of the organization by the privates, specialists and sergeants within the hierarchy.

Too often, companies blur the lines between data governance and data quality. I often see Data Governance Manager positions advertised that include a whole list of data processing, profiling and cleansing duties. This focus on doing data tasks is a mistake, because it breaks the hierarchy and devalues both disciplines – especially data governance.

Make sure your company has a clear command structure for data governance and data quality.

Create a clear mission statement for each discipline and ensure that everyone, from your foot soldiers to the brigadier generals, have a crystal clear understanding of what part they need to play.

What do you think? Has your company figured out the right command structure for data quality and data governance?

Welcome your views below.

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About Author

Dylan Jones

Founder, Data Quality Pro and Data Migration Pro

Dylan Jones is the founder of Data Quality Pro and Data Migration Pro, popular online communities that provide a range of practical resources and support to their respective professions. Dylan has an extensive information management background and is a prolific publisher of expert articles and tutorials on all manner of data related initiatives.

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  2. Tejasvi Addagada on

    A different perspective that got me thinking. Some Organisations consider Data Quality as a dimension of Governance while some consider it as a dimension of Data Management. Data Governance is an enforcing function which defines policy, frameworks and org structure while ensuring that data management is fund-able and sustainable. It is really the choice of the org based on the current needs and the scale of operations - regulatory or growth drivers, that define the functions. I like to delineate "Monitoring, Evaluating and Directing" as functions of Governance while Planning, Building and Running and part Evaluation of Data Quality, Metadata, security services etc as functions of Data Management. Each needs to complement one another while I would not try to raise one above other if we are viewing it from a sustainable perspective.

    • Thanks Tejasvi, appreciate the great comment.

      I agree completely with your assertion that "each needs to complement one another" and I am definitely not trying to raise one above the other.

      My impetus for the article came from several sources...

      1) I keep seeing organisations spending months (+ sometimes years) implementing incredibly elaborate Data Governance frameworks and policies but with little to show for it from the business user perspective. So the post was really calling out these organisations to do something sooner, rather than later, and data quality management is something they can implement tactically in the short term and then strategically (i.e. prevention) as they mature.

      2) Hiring companies keep confusing data quality and data governance so I tried to make that split a little clearer but I think your description nails it better to be honest :-)

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