The Top Big Data Mistakes to Avoid

Big Data is a huge concept in the modern technological sector. With so many ways to gather information from your users and products, it can feel like your company is overwhelmed with data.

However, Big Data analysis can also help you to determine the path that your company takes. Without it, you may fall behind your competition. As a result, you need to know what mistakes you should avoid when you adopt Big Data.

Mistake #1 – Relying on Your Instincts

Your instincts may guide some of your business decisions, but you can’t rely on them when it comes to Big Data.

In fact, many people make the mistake of trying to limit the data sets that they look at, primarily with the goal of matching the data to their instincts. This leads to a phenomenon called “confirmation bias”, in which you actively look for data that supports your views, rather than accepting what the data actually says.

Mistake #2 – Collecting all of the Data

Having said that, it’s still not a great idea to collect every scrap of data you can find without any plan for how you’ll analyse it. This is what leads to that overwhelmed feeling that many companies experience when working with Big Data.

Before you start trying to leverage Big Data to your advantage, create a set of questions that you want to use the data to answer. This roadmap will guide you when it comes to what data your organisation needs to collect.

Mistake #3 – Take Action on What You Find

Another common Big Data mistake is sitting on the information you find, rather than taking action on it. Confirmation bias often plays a role here too. If an organisation finds that the data tells them something that doesn’t mesh with what they thought it would, they may be slow to take action.

However, you must remember that your data changes constantly, often in line with the trends in your market. Collecting and analysing the data is important, but it doesn’t mean anything if your organisation doesn’t react to the data it gathers.

Mistake #4 – Not Checking the Quality of the Data

Not all of the data that your organisation collects will be of a high enough quality to use in determining future policy. In fact, low quality data can actively damage your organisation.

Many people don’t realise this when trying to analyse Big Data. Instead, they think that every piece of data they collect is relevant. You need big enough data sets to draw accurate conclusions, but if that data comes from unreliable sources you may end up doing more damage than good.

Mistake #5 – Not Using the Cloud

This may seem like a simple mistake, but many people still don’t understand the infrastructural demands that using Big Data creates. Instead of moving to a cloud-based system, they try to keep everything in house.

The cost of maintaining so much infrastructure should show how much of a mistake this is. If you’re trying to use Big Data without adopting cloud-based technology, you’re organisation will lose thousands of pounds.

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