Data Governance and Data Quality: Angels and Angles
“Are the terms Data Governance & Data Quality synonymous or synergistic?” and “Does this matter?” were two of several questions explored in a recent “Hashtag” or “Tweet Chat” which I moderated on behalf of Trillium Software. For the uninitiated, as I was until recently, a Hashtag Chat is basically a Twitter conversation open to all, focused on a particular topic, question or theme. In this case, it was a great way for a wide variety of data people to share and exchange ideas and thoughts. An added bonus is that perspectives need to be expressed within the 140 character limitation which Twitter imposes so it encourages brevity and clear thinking, all too often lacking in some data discussions.
Our recent chat was a particularly lively affair, which started by considering the synergies between data governance and data quality, but then branched out to explore further topics such as “What skills are really important to success in data governance and data quality?” and a number of useful ideas emerged. Here are the ones that stood out for me:
First, there was consensus that data governance and data quality are not synonyms, but do have strong synergies. Some thought the core of the relationship between them is that data quality is an end goal of data governance, but not the only goal.
Does all this really matter, or is it just an irrelevant academic discussion akin to medieval theologians debating how many angels could fit onto the head of a pin? (They really did that by the way). The consensus was that it is important to be clear about the core concepts and practices in these disciplines. It’s already hard enough to explain their value to non-specialists and so lack of clarity within our profession can only serve to make that task even harder.
One strong theme that did emerge is that the skills required to be successful in any data governance or data quality initiative are broadly similar with one exception. Data quality is more often seen as an IT specialty so professionals in this area tend to be more technically proficient in specialist data quality and data management tools. Data governance on the other hand is mainly seen as a business function, discharged by business data stewards, so recognizing the value of the data to the enterprise is a key competence in data governance.
Apart from that distinction, however, the skillsets for success are pretty much identical; soft skills are the real differentiator between success and failure. These include the need for a clear understanding of the wider business, what drives it, and how data governance and quality can contribute to it. Also important are enthusiasm and optimism (‘Cheerleading” as one contributor termed it), leadership, diplomacy and influencing skills, an ability to express complex ideas and problems simply, change management, communication skills, and programme and project management. One chat participant also suggested that “An ability to walk on water” is also an advantage. I’d love to do the training course; I’ll float that with my boss.
Seasoned practitioners from our Hashtag Chat also offered these suggestions:
- Start any governance or quality initiative in a small area of the business and grow it from there. You’ll be more likely to demonstrate rapid success and get buy-in for additional projects.
- Ensure you can relate data quality or accountability problems to real business pain points or failures, and be able to show how improvement benefits the business. One contributor had built a model to calculate the costs of undelivered postal mailings and how incremental improvements positively impact his Marketing department’s bottom line.
- You must have active senior manager and business stakeholder support, but ensure you also get the participation of people working on the front line of data issues.
The debate around the semantic relationship between data governance and data quality proved not to be an esoteric exercise, but instead a great launch pad for an informative session with many practical hints and tips. We didn’t agree on how many angels can fit onto the head of a pin, but did learn some new angles about the real world of data governance and data quality.
|Nigel Turner |
VP Information Management Strategy, Trillium Software
Nigel Turner works with Trillium Software clients to start, expand and accelerate their enterprise data quality initiatives. He spent much of his career at British Telecommunications plc (BT) where he led an internal enterprise wide data quality improvement programme. This ten year programme was praised by Gartner, Forrester, Ovum Butler and others both for its approach and proven benefits. Nigel has published several papers on data management and is a regular invited speaker at CRM and Information Management events. He is also a part time lecturer at Cardiff University where he teaches data management.
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