Limits of Data Quality

Some years ago I was asked by one of our salesmen to attend a meeting with a leading milk distribution company. The friendly milkmen who deliver your daily pinta (or used to).

 With ever increasing competition from the supermarkets the marketing manager was keen to pursue a CRM solution as a means of at the minimum retaining existing customers.

 After the initial discussions concerning what they wanted, what we had to offer in terms of data processing, customer matching capabilities and database technologies, service and support, we got down to the nitty gritty.

 “Can we see a sample of your customer data?” I asked. Whereupon I was handed a pile of papers. These turned out to be photocopies of the milkman’s rounds records. Each milkman you see was responsible for maintaining records of orders and payments in a little black book.

 “You have an electronic version?” I ventured. “Errr No” was the reply. So a major data capture exercise would be required.

 “Lets have a look at the contents of the record book” I said.

 Much hillarity ensued as we browsed through the data and realised that the contents were less of a record of customer data and more of a set of advise to colleagues regarding delivery to specific households with annotations relating to the, er how shall I say this, well here is an example : –

 “Lady at no 5. 2 pints a day. Collect fridays before 8 – she’ll still be in nighty”.

“No. 32. 1 pint. Big licks – wow!”   – I have no idea what this meant.

 Now I like a challenge and I like to think I have found some pretty creative solutions to difficult data issues but there are limits.

 My advice to the marketing manager – contact each delivery address and capture the customer details properly. I would be happy to advise on data formats etc. Also some training relating to the importance of maintaining accurate data would not go amiss.

 Regretably, with no budget available we had to walk away. But this little story does I think provide a very extreme example of the pitfalls of not having appropriate data quality and data governance in place. Whilst the company in question could operate effectively in that the milk was delivered, they could not leverage their data in order to gain any competetive advantage.

 You don’t see many milkmen these days.

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