I was talking with a leading bank yesterday regarding their current information ‘set up’.
They explained they had the usual disparate ‘MI teams’ which pumped out contradicting data from the same source. It’s the most common theme I encounter across all business sectors.
Though what really struck me was how the business had data on the bottom of the pile in terms of investment. Again, this is not unusual as people want more data, usually at the drop of a hat, from systems that they won’t invest in etc. And they genuinely don’t want it to cost anything – I’ve always assumed that this is due to the data just being there and it takes minimal effort to extract it.
So I asked ‘what’s the number 1 focus of the business today, and for the remainder of the trading year?’. The reply was swift ‘reducing operating costs. simple.’. I laughed. I probably shouldn’t have.
It had been described to me how the multitude of system’s had received minimal investment over the years. There were over 1000 independent systems used in the collaborative MI effort. Through multiple mergers the systems had been ‘made to work together – or at best, fit the process‘. Understanding the data being recorded was an immense effort only mastered by one or two. Then there’s matching the data from one system to another. Then (you see how this might go on!) there is the use of fields (or entities if you will) that have been used for multiple purposes rendering years worth of data simply useless.
But of course. the business expects the data to be real-time, accurate, of the highest quality. But they’re not willing to invest?
When we started talking about the number of resources that had a relation to MI the alarm bells start ringing. The usual story – ‘they only spend 10% of their time on MI‘ suggests the line manager. Ask the employee and you always get a very different picture. Add up the effort and you won’t believe the cost spent across the organisation. But it’s hidden in many places, and so easily disguised. The phrase ‘too many chefs’ springs to mind. Anyone can extract the data, but it takes knowledge to know what that data is, and how it should be used.
So back to the case in point. The focus was to reduce opex. spending. Simple. Sort out governance, ownership and the root cause issues that make people distrust the data and you’ll quickly find that they don’t need the extra MI resources. Before you know it you’re reducing opex by a lot more than you need to invest. And your data provision will be all the better for it.
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