Today I attended a nice little event at the British Computer Society. The event was called “Data Surgery” and had sessions with combined presentations and discussions around data management. Among presenters were Julian Schwarzenbach with his beavers and squirrels from the data zoo and Martin “Johari” Doyle of DQ Global discussing data quality.
In the data quality session I attended the good old subject of selling data quality was touched and not surprisingly the fear factor was mentioned as a way to go.
While I agree that fear of failure in the form of bad reputation and financial loss is a working concept I have also seen that data quality initiatives based on fear doesn’t stick too long. Similar thoughts were expressed in the Data Quality Pro post called Taking The ‘Fear’ Factor Out Of Data Quality By Duane Smith. Herein Duane says:
“Selling your data quality initiative based on fear may have a short-term pay back, but I believe it will ultimately fail in the longer term.”
The opposite approach to relying on fear is counting on greed. That means making better profit by improving data quality. It’s a more sustainable way I think but indeed predicting ROI from a data quality initiative is very hard as examined on the blog page called ROI.
So, most often we fear counting on greed and falls back to greeting the fear.
I don;t think this should be an either / or decision.
Data quality initiatives should be built on as broad a business case as possible – so combine fear and greed if they are both drivers.
I do agree that a pure fear approach is probably unsustainable. fear is basically an “insurance” sale – most people like to do the bare minimum for insurance.
The same question can of course be applied to data governance, with similar results.
What I think is driving most data quality initiatives, from my experience, is neither fear or greed, but pain.
The initiatives does not really get high level management attention until business breaks down. Well, or close.
Thanks Gary and Jeppe for adding in. Indeed, it doesn’t hurt if both fear and greed are part of the justification. Pain is a trigger for sure and as always (not only for data quality improvement) finding the painful spots will guide the prioritization.
As for pain as a trigger I have also noticed that making something easier for the influencers / decision makers is a good way of getting data quality improvement and prevention approved.
Thanks for the post. I would like to complement your important observation “I have also noticed that making something easier for the influencers / decision makers is a good way of getting data quality improvement and prevention approved”
This is absolutely spot-on and something that many decision-makers and Executives fail to grasp. Let me bring in an observation from the Lean discipline, specifically a quote from Shigeo Shingo “There are four purposes for improvement: easier, better, faster and cheaper. These four goals appear in the order of priority”. The clarity of thought in sequencing the improvement goals is amazing.
In true lean systems, the primary focus of improvement is centered around “humanization of work”. In other words, it starts with making the work EASIER. Respect for People and Continuous Improvement are mutually beneficial – one leads to the other, and the other is necessary to support the former.
When you can show that your decision-making to invest in MDM is anchored around the concern to make it easier for your data stewards, data analysts, Information Managers etc, who need to work daily with business data, then all questions around buy-in etc are automatically resolved. And this momentum helps to sustain the MDM program as first the front-line data stewards/analysts, and later the higher level executives start to see how easier their work becomes with the introduction of MDM (IT product, and discipline, as well) – this leads to better work processes as they start to find more time to improve their daily work routines by removing unnecessary waste from their jobs (esp. rework of poor quality data, need for multiple inspections & approvals etc), which in turn makes the processes go faster (or flow faster), eventually reducing the cost of the process!
This is the virtuous chain of thought that should drive investments in any business initiative – not just MDM programs. More than fear or greed, this seems to be based on showing “respect for your people” by giving them the opportunity to deliver their best in an enabling environment, removing obstacles for their performance.
I would absolutely agree with the last comment, Data Quality needs to be simple, focussed on the business and not painful to implement. QFire Software has focussed on building a Data Validation product designed for use by business users. No heavy Technical setup and browser based so easily deployable. Pain drives short term behaviour but life change requires an environmental adjustment. http://www.qfiresoftware.com.au
I think I would say ‘opportunity’. This could take many forms and largely depends on the organisation, e.g. non-profit, government, for profit.
Using the Johari Window metaphor, I think we need to try to expand the open area as much as possible. Moving into the ‘blind area’ enables us to uncover things we didn’t know we are missing, i.e. identifying lost opportunities for us. Moving into the ‘hidden area’ means that we are better communicating the value we offer, i.e. identifying lost opportunities for others (customers, partners, etc).
Thanks a lot Shiva, Neil and Adrian for commenting. Good to see a lot of possible drivers for better data quality.