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The Five Big Data Mistakes No One Should Ever Make

You can use big data to gain valuable insights that help you improve and grow your business. However, having big data doesn’t automatically turn it into a success story. If you don’t consider the common pitfalls, it could become an expensive and time-consuming effort that does not bring you the advantages you had hoped for. That is why we have collected these five mistakes that you should avoid:

1. Don’t blindly assume that your data is clean and accurate
When you start collecting your data, you should never trust that it is perfect straight away. There are many examples that show how inaccurate data can hurt your business. You may remember the time that Bank of America sent one of their customers a credit card offer including a swear word.

If you have not had the time to check your data and do your due diligence — do not use it! You should always scrub any data you collect and look for potential issues before you act on the wrong data and damage your company’s reputation.

2. Only collect the data you need
There are plenty of companies that are collecting loads of data: a so-called data swamp. It is great that they can find and store that much information, but often they have no idea how to make sense of it.

If you want to start collecting big data, first decide what you need that data to do. Are you looking to improve your customer intelligence? Then focus on expanding your knowledge on those customers — and make sure you inform them on how their data is being used! You should always find a problem that big data can solve. This saves you the time and effort of having to go through large amounts of information for which you have no use.

3. Make sure your programs are bug free
It is often an expensive decision to start using big data to grow or improve your business. That decision will likely pay itself back if you use the data correctly, but before you can use it correctly you need the right tools.

Whether you use your own program of trust another party to create a program for you — always make sure that it is largely bug free. People make mistakes and some small errors can occur, but the overall program should run smoothly and help you solve problems rather than create new ones.

4. Don’t reinvent the wheel
You are probably not the first company to start collecting a specific kind of data. The Internet is full of case studies and best practices that can help you get started.

If you want to create your own data collection system and analytics tools, you are most likely wasting a lot of time and resources. Do your research first and see if there are big data solutions that have already been developed and that can help you solve your problems.

5. Confirmation bias
Data analytics is of great value to help you solve your business problems. It can even let you discover new opportunities. But if you look at your data with the answer you need already in mind, you might suffer from confirmation bias.

Confirmation bias occurs when a data scientist uses limited data to prove the thing that they already thought. It means you ignore the data that doesn’t align with your earlier ideas. This can become a real problem. If you only use the data to tell you what you want to hear rather than letting the data speak for itself, you are at risk of misusing the data. This way you won’t really extract its value. has been working with big data for almost ten years. We know these pitfalls, and how to avoid them. If you are interested in this knowledge or our data, please contact us via