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Gaps Your Big Data Project Can’t Have

Big data is making huge waves in the world of business and for good reason. It can help you develop insights on internal processes, customer relationships, marketing efforts and much, much more. But if you think that it’s foolproof, you are likely to leave gaps in your strategy that can be misleading at the very least, and legally disastrous at the worst. Here are some gaps you need to make sure you close.


It’s one of the biggest words out there right now, thanks to some high-profile security failures from companies like Facebook as well as breaches into online banking apps within the last year. If you’re collecting and storing vast amounts of data, you have to invest in protecting them. From ethical hacking to help you understand and cover the weak points of your network to contingency plans in the event of a data breach. Make sure you are paying for the professional protection of the data you keep, otherwise you could be legally responsible for what gets out.


Data doesn’t lie, or so some would have us believe. The truth is that data can be misleading, but mostly because of those of us who use it. When you collect data, it’s a good idea to have a qualifying process by which you can better understand it. Ensure that the data you collect is accurate, consistent, relevant, complete, and timely. Outdated or incomplete data can start taking your journey for insights into unreliable territory.


Having the right tools by which to organize all the data points you collect is essential, as well. If you’re working with insights from a bunch of separate sources, it’s easy to miss the fact you’re working with duplicated data points, and much harder to organize in general. You need to look at implementing things like ETL tools. Creating a big central hub that allows you to more easily and efficiently search through the huge range of data points your business could collect.


Make sure you know why you’re collecting data, as well. The best approach is one that is driven by purpose. If you know what questions you’re trying to ask, you can better figure out which kinds of data are most relevant, and how to quickly source and sort them. Without a purpose, you could get stuck searching aimlessly through huge sets of data with no results to show for it.


Again, the problems with interpreting data tend to lie within those who do the interpreting. One of the biggest big data mistakes is letting bias get in the way. Knowing which questions you want to answer is good, but if you search for only the data that gives the answer you’re looking for, you are going to get biased results. Aim for a complete picture by intentionally aiming to collect data that also contradicts preconceived notions or optimal outcomes.

A big data strategy can be hugely beneficial to your business. It can help you develop understandings you might otherwise miss entirely. Just make sure that you’re aware of the potential limitations of your efforts and try to improve on them constantly.