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5 Massive ‘Big Data’ Myths Most People Believe – But Shouldn’t

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There’s so much hype around the subject of Big Data that it’s inevitable it will sometimes be over-sold. Don’t get me wrong – revolutionary advances are being made every day by organizations and businesses learning to combine the vast amount of data at their fingertips with cutting-edge analytics and data science. But it isn’t a magic cure-all for your (or the world’s) problems and mistakes and miss-steps happen all the time, often at great cost.

Here are some of the “facts” about Big Data which should be taken with a pinch of salt. Like all myths they may have been based on truths at some point but, though often believed, they don’t necessarily stand up to scrutiny.

  1. Everybody is doing it

With anything new and exciting there is often an impulse not to miss out – and Big Data has proven to be no different. Despite all the words written about it, though, research still shows that the number of companies effectively putting true Big Data technology to work is small. For the majority, it remains an ambition – something which everyone knows they ought to be doing but haven’t quite got right yet.

The danger here is rushing in due to a fear of being left behind. While fear can sometimes be a great motivator, it can also cause us to do things in a rushed or sloppy manner. Spending time building a strategy and assessing the impact of moving to a data-driven business model may delay your entry and possibly let other, more hasty competitors steal a momentary lead – but it’s an essential part of the process and shouldn’t be rushed due to a (false) belief that you’re being left behind.

  1. It’s all about size

Size – volume – is merely one of the defining characteristics of Big Data. Other things such as variety or velocity of the data are just as important. Data is coming in faster than ever – and the more quickly you can process it, the more up-to-date and relevant it is likely to be. Data is also available in increasingly diverse forms – a greater variety of data means you have more ways of looking at a challenge – and are more likely to find an innovative solution. I advise my clients to look beyond the size of their data and take into account the huge benefits faster and more diverse data can bring. In fact, too much data – particularly if it is unverified, old or from a limited number of sources, can be a very dangerous thing, making simple solutions appear complicated as well as incurring wasted expenditure on capture, storage and compliance.

  1. It will tell you what will happen next

When it comes to predicting the future, data doesn’t actually tell us anything that is certain – and anyone who tells you it does, is trying to sell you something.

Big Data-driven prediction is about extrapolating what is most likely to happen in the future, based on what you know has happened in the past. If you are analyzing real-time data, it can take into account what is happening right now, as well. But any predictions it gives you will be based on a probability, and there is always a margin for error. The more data you have, and the more relevant that data is, the more accurate your probability forecasts will be, but reality often has a way of throwing curve-balls – look at how inaccurate political forecasting has turned out to be during recent elections, in spite of the sophisticated statistical analytics which has been used.

  1. Big Data needs a big budget

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