When businesses hear the phrase “big data”, they often assume it’s a IT problem, best left to the techies. They couldn’t be more wrong.
We’ve already entered the age of front-desk IT, where the IT department is actively driving the business, rather than providing solutions, support and systems driven by business needs and outcomes. The advent of big data – which is basically the use of diverse data to solve business problems – is meaningful for the entire business.
Big data is not in itself, the solution or answer to anything. At its heart, big data is a problem solving tool. In order for your business to benefit from big data, you need to have a good idea of the sort of problems you’re trying to solve, and the kind of data you’re going to need to solve it. The IT department does not necessarily have an in-depth understanding of the business problems that are facing the marketing, forecasting or finance departments. It is only the heads or key staff within these departments that can articulate what is most challenging for them.
Marketing is one department that can benefit immensely from big data solutions. The data available to marketers through social media interactions, customer purchase patterns, call-centre interactions and market-wide consumer data means that marketers can start to make decisions based on data analysis where previously there’d be a good deal of guesswork.
Because marketers are working across a large number of consumers with a good margin of error, they don’t need their data to be 99 per cent accurate or complete like, say, an insurance company’s actuaries would. As long as they can get a general picture, they’re able to make a decision. Since marketing decisions rely heavily on the speed with which they can be made, velocity is by far the most important of the “four Vs” (veracity, volume, variety and velocity) of big data for a marketing solution.
Imagine, for instance, a big data solution utilising a wide variety of data that allows a bank to predict, with 95 per cent accuracy, which customers will switch to a competitor if they are not offered an interest rate cut. The solution must use a wide enough variety of data to be able to meet accuracy standards, but it must be able to advise a customers service representative of the correct course of action in real time, while the customer in question is on the phone.
This is wildly different to the requirements of, says, a finance department. In their forecasting, an 80 per cent complete or accurate data-set is woefully inadequate. It could lead to errors in the tens of millions of dollars, and place the company at severe risk if their forecasts are based on faulty data. A big data solution for this department needs all the accurate data it can get. In this situation, data veracity and volume are far more important than variety or velocity.
Simply handing over responsibility for big data to the IT department, and asking them to obtain, capture, store and analyse data without giving any thought to what the data is, and what it can do, is a very fast way to spend a lot of money with no tangible return. If you ask IT to “do big data”, your solutions will be ill-matched to the needs of one, some or all of the departments who could benefit from it, and your will be at a competitive disadvantage to those companies who’ve taken the time to get it right.