Covid 19 has forced many companies to rapidly accelerate their data and digital transformations. And with this rapid modernisation of systems and processes, the importance of having a robust Data Governance framework has also grown exponentially.
Which specific Data Governance tasks vary from company to company depending on their size, their nature, and the level of data maturity they already possess. So, data ownership remains a critical challenge for some, whereas it is a priority to improve data quality for others. For others, it will be a need for both data ownership and data quality, plus numerous other key data governance activities. The typical ‘one size fits all’ model can no longer offer the dynamic and agile approaches that digital-savvy companies require.
While the need for an adaptive data governance approach is one of the enablers for successful business transformation, demonstrating the value of investing in the necessary people, processes, and technology continues to be elusive. This is partly driven by a strong perception amongst those who control and make budgetary decisions that data governance is a cost rather than a benefit. Having such perceptions can and will stifle the ability of an organisation to fully and successfully implement data governance.
So along with implementing the data governance activities themselves, your data governance plan must show how value is being derived. The current buzz is around how you monetise your data, and showing value from your data governance activities is just one element of this monetisation challenge. According to the Gartner (www.gartner.com) CDO 2021 survey, while 38.7% of CDO’s are measured on data monetisation, only 27.2% report that their D&A teams are producing tangible business value to the organisation.
We would advise caution in undertaking this monetisation activity (itself being perceived to be a cost to the organisation). So, as in previous blogs, we have spoken about the implementation of Data Governance being something that organisations can do without significant up-front investment (The recent Oakland Group Lighthouse report – Data Governance by Stealth – explains how many of the benefits of Data Governance can be achieved through no or low-cost approaches) Using this same idea of doing things by “stealth” should also equally be applied to how you monetise your Data Governance.
Let’s take Data Quality as an example. How do we show that fixing a specific data quality issue makes good business sense? Rather than pick a complex data quality issue, start by focusing on an issue that almost everyone understands well. A great one is to show the cost of having an incorrect customer date of birth.
So if you know that within your call centre, 1 in every 20 dates of birth are being misreported or are incorrect, and you know the amount of new business you are generating in any given day, say 60 new customers per call operator, this means three incorrect dates of birth are being generated on average per operator every single day! Scale that up by the number of call operators, say 10 in this example, then over a typical month, then some 900 incorrect dates of birth are being generated or nearly 11,000 of your customers in a year. That is a big data quality issue that needs fixing before you even start calculating the cost to fix it.
Armed with these sorts of statistics, you can estimate the cost of correction – probably the cost of an analyst. So using an hourly or daily rate, you can start to cost out how much your data quality issue is costing the organisation.
The costs of your analysts having to correct simple things like dates of birth manually can be costing your business £££s. However, this could be money well spent if your analysts can identify systematic patterns and trends in the data quality issue. So fixing these issues may incur an initial cost, but it will derive an immediate benefit if the problem is permanently fixed. Of course, in the call centre example, a key task will be training and educating employees to be more diligent, which will have a cost in terms of training materials and training time.
The way you monetise the value of the activity will vary by business. But you will quickly be able to show the financial impact of not doing data governance correctly over a given month or a year. Focusing on these easy-to-understand issues and simple arithmetic will quickly make your stakeholders sit up and take note. In fact, from our experience, sharing such insight with your Finance Team will very quickly result in them giving you more precise values to use in your calculations.
Another easy example is people moving house. We know from research that some 4 to 5% of the population move home each year. So if you do nothing to your data to check that the customer details are accurate, your data quality is eroding at least 5% per year. This is, of course, assuming all your data about those who have not moved was complete, accurate, and fit for purpose to start with! Again, this latter point is relatively easy to validate against recognized databases such as the Postal Address File (PAF) to give you a figure of how accurate your customer address data might be. Then using this figure, you can work out the indicative cost of content being sent to wrong addresses and the costs involved when customers ring up to explain a delivery or letter has gone astray. The numbers can be mind-blowing but easy to compute.
These few examples highlight it is then easy to start showing the cost of not fixing data quality for the most basic customer details. Suppose you capture a record of your data quality issues on a register. In that case, having an indicative cost for everyone will start to reveal the true cost of inaction to those who doubt the value of data governance!
Hopefully, this blog shows that it only takes some simple metrics to monetise the costs to a business of poor data quality. These statistics are compelling in getting people to sit up and think about the overall cost of taking the appropriate action on fixing data quality or, more generally, not implementing proper data governance. As the Oakland Data Governance by Stealth report explains, ((Data Governance by Stealth Lighthouse Paper, September 2021).this monetisation can be done by stealth and under the radar of extensive transformational programmes. But how many of us are telling this data story to our business stakeholders?
Andrew Sharp is the Oakland Data Governance Lead.