Here's a sentence to conjure with: “The complexity of investment data and the lack of global data standards has resulted in fund management companies investing in specialist teams and software packages to manage the challenge of populating front office systems with quality data.”
Could the case for data standards be any clearer? If you don't have data standards, you need extra people and extra software in order to get any practical value out of data.
Notice a couple other things about the statement. First, data complexity is added to the lack of data standards as a cause of problems. But, in fact, it you have good data standards, data complexity is not an issue. A good data standard handles whatever inherent complexities exist in the data being standardized. Sure, if you don't have data standards, then you'll be working with data that is inconsistent and difficult to reconcile. This will create additional complexity to the task of exploiting the data. However, it's not genuine, inherent complexity arising from the business. It's noise added to the system by the failure to use standards.
Second, I don't think it's right to say companies “invest” in specialist teams and software packages to overcome the difficulties created by a lack of standards. This is a cost. You don't get anything like a good return for your spend on data wrangling, manual adjustment of data, specialist interpretation of data, re-mapping of data in software packages etc. This isn't investment – it's institutionalized firefighting.
Invest in standards. Get big returns. Consalis
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