All roads lead to (or from) data quality

By Rob Frost, Propositions and Solutions Manager, GBG Datacare

In this industry I’ve often quoted my grandfather-in-law who suggested that “oil is the cheapest mechanic”.  Taking this concept, what if we liken data to oil, and the engine to a customer database? The point is that investment in improving and maintaining data quality is well worth doing.

This rings true with heightened sensitivity around the General Data Protection Regulation (GDPR), which comes into force in May 2018, and the risk that businesses could be fined up to 4% of global turnover if they don’t comply. However, maintaining accurate data on an individual has always been part of the Data Protection Act (DPA), so this heightened sensitivity is naive – it should be happening anyway.

Keep Clean and Carry On!

Many companies will state that their data is regularly cleansed against suppressions such as deceased and ‘gone-aways’, etc. Some will add that they regularly validate and verify contact data such as email addresses and telephone numbers. And to some extent this is true. However, this type of data cleansing tends to only gets applied to contactable customers or prospects - those who are likely to receive a communication or those customers earmarked for marketing activity. But this approach is only skin deep and all too often operational data is not included.

The DPA and GDPR make no distinction between customers that are contactable and those that are not. Period!

Business Benefits

If you store data you are not planning to use, why keep it? Cleaning your data can have a positive impact on day-to-day business operations. Freeing up IT system space by removing duplicate and unwanted records will speed up querying. Removing data stored on back-up disks will save your IT department time and money whilst deleting duplicate customer records will provide you an accurate count of customers.

But for many businesses, cleansing data outside of campaign delivery is not seen as a priority. This, in my view, is a mistake and where we return to the engine oil analogy. I’m no mechanic, but oil, what does it do? It lubricates, it protects, it absorbs heat, and it makes the working parts work more effectively!

So, let’s explore lubrication. Followers of Formula 1 may recall a piece of research that studied which working parts made the biggest difference in racing car performance. The research looked at things like the driver, aerodynamics and suspension. The factor that made the biggest difference was the tyres (which is why Michael Schumacher had 60 Bridgestone engineers working with him and in the rest of the paddock about 5!). In this case, and in my opinion, the biggest gains in analytical performance are in improving data quality and data accuracy.  So data cleaning can lubricate analytical performance, and is probably cheaper than those much wanted data scientists.

Next up, protection! Easy when we think about protecting reputational risk and fines through the misuse of data. Activities such as contacting deceased customers or getting someone’s name or age wrong can cause distress to individuals and risk damaging your organisation in several ways.

Data can also protect the customer experience by reducing duplications, so for example, the inbound call centre assistant sees one record of Mr Frost at ‘10 High Street’ on their screen and not five. Data can also pre-populate quotation forms to speed up customer interactions. Marketing investment is wasted if mailings or emails fail to be delivered due to incorrect contact details. What about protecting the integrity of models and segmentations where variables like postcode, age and gender play an important role? Surely investment in data quality is worth considering?

Working Together

And finally, can improving your data quality make different departments work together better? It would stop different departments producing different sets of figures for the same question, which must be a good step forward and avoid those awkward conflicts over whose figures are right or wrong. It would enable system migrations that merge differing datasets from differing platforms to become more streamlined.

Unfortunately, there are organisations that have conflicting customer data about the same customer. Fixing your data will help in the creation and maintenance of your Single Customer View (SCV) and Total Customer View (TCV) – TCV being GBG’s real-time SCV service using unstructured data. From a campaign management perspective, better data quality will reduce the differences between campaign initial counts versus final counts and the need to alter the selection criteria to meet the volume of print ordered.

Cleanse, Use, Repeat

So let us conclude. Is data quality the place we should start, or end up? Perhaps, it is both. A virtuous circle that helps keep the parts of any business continuously moving.

Any questions, comments or requests for help are always welcome. Post them here or contact me at


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