When there was no data

Soon after computer 'data' was first conceived, punch cards became an invaluable means for corporations seeking to define and create a new asset class in the form of knowledge capture, retention and exploitation. At that time, most organisations had no previous know-how regarding the best way to manage, handle or process their data to create competitive advantage.

Yet most adapted and discovered optimal ways of handling and processing data, and subsequently transitioning it to other mediums, such as magnetic tapes, disc arrays, etc. The same learning by doing principle also applies to the emerging challenges of Big Data.

So why should Big Data be any different? Whereas punch cards and magnetic tapes presented manually intensive challenges, today we have powerful computers to crunch our data. What ought to be different, however, is that we temper the hype and one-sided arguments of IT vendors and industry consultants with pragmatism and prudency.

Furthermore, the driver should not be the quantity but the quality of data. Decades of experience with data in its various guises has already shown us that it is far better to have more manageable data that is also of high quality than to have mountains of it of dubious quality.

Data illusion: big is bountiful

The financial crisis of the recent times have also served as a reminder that we can slice and dice our data to the nth-degree, but it is the quality of the underlying assumptions that drive those financial (and other) models that ultimately determine the accuracy (or inaccuracy) of our data and any useful information or intelligence that we might glean from it.

Whilst most chief marketing officers begin to glee at the thought of getting their hands on reams of customer data, a more sobering thought should prevail: most of the data that we call Big Data is as susceptible to change as we are as human beings.

So where should the focus and priorities lie for Big Data? The answer would obviously depend on the ultimate purpose of that data. However, one would do well to remember that most companies have already invested millions in harnessing a well-rounded digital view of their customers, and in doing so have partially also contributed to the rise of the Big Data dynamic.

Yet, on the other hand, most organisations are, at best, mediocre at fully exploiting the data they already have to create a win-win for themselves and their customers. So how should they deal with a surge in data volumes and processing demands, never mind complying with the legislative requirements?

Data trap: Real-life scenario

One customer’s recent experience with their utility company (let us call them Company X) was an eye opener in this respect. This long-time (10+ years) customer of Company X received a demand for repayment of a miniscule amount of a few pounds (circa £20) outstanding on their account.

What is interesting is not the demand for reimbursement but the actual approach taken by Company X in contacting the customer. Instead of taking a more intelligent approach of first analysing the information they already had, and then determining a suitable course of action, Company X chased the customer through a debt collection agency.

This happened even though Company X had recently undertaken a massive IT transformation programme, running into tens of millions of pounds. Add to that, all of the customer’s contact, usage and demographic data already existed within the Company X’s systems. Moreover, the customer had an immaculate credit and on-time payment history, with an annual expenditure with Company X of circa £1800, via six different service/product lines.

The above scenario is a poignant and real-life reminder of how companies must learn to walk before they can attempt to run with Big Data. Most importantly, their focus should first and foremost be on responsible assimilation, processing and analysis of data to better serve their customers. Hence, Good, not Big, should be their top-most priority; and good data must support good business processes, and vice versa.

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Read Part 2 of this series of articles »

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