I was once told that the words “data analytics” make business executives go deaf. They are barraged daily with articles, blogs, consultants, salesman, and colleagues talking to them about the “power of data” and how everyone is going to lose their job to Sofia the robot.

It is a challenge to distil the signals from the noise about what data analytics really means for a business. What is the key information they need to make better decisions about data, and what is just garbage about the latest blockchain companies?

Often businesses get this horribly wrong and can spend millions trying to sort out their data, with some flourishing but most floundering. Acquiring highly skilled (or not) individuals to fulfil roles that are not needed, buying inappropriate software platforms or impeding workflows with analytics is not uncommon. These mistakes are often time consuming, costly, and cause many to shy away from data analytics.

You can’t blame them for getting it wrong, either. People are constantly being asked to make important investment decisions on data analytics teams, technologies and processes that they don’t fully understand, and frankly they shouldn’t have to. From our experience, people just want information to make better decisions and improve the way they work. This focus is often lost with data analytics, but, there are some things all businesses can do to improve the value they get from data analytics.

  1. Grasp the basics

Businesses often want insights, but they do not know what to do if they had them. If you know what decisions you need to make, then supporting those decisions with data analytics becomes straight forward. This step is vital in cutting through the noise.

  1. Know where you want to go

Once you have become aware of the decisions you need to make, then understanding what types of information you need to inform those decisions is key. Reams of charts on a complicated dashboard may look cool, but how do they actually inform the business? The key is being able to simplify the masses amounts of data into key insights.

  1. Be clear on what you already have, and what else you might need

Often businesses have access to information but do not utilise it when making decisions. Sometimes they already have the data they need, but not always in the right form. Being clear on what information you need to make better decisions will give you focus on the exact requirements for any data analytics project. Keep in mind, data and information are different things, and understanding the format of the information required is important. Often this is the biggest challenge of data analytics – how to convert the results of advanced statistical models into real value for a business.

  1. Understand what “leveraging insights” actually means

It may seem obvious, but knowing HOW the insights will affect your decision making is key. What will you do if the insights contradict your initial hypothesis? Are you prepared to use insights that were unexpected, or which steer you away from the current course?

Of course, confirming what you already know has immense value too. It shows you are on the right track and making good decisions. However, not all insights will show you that everything is going well and there will often be some changes. Faced with challenging insights, businesses often question the research rather than questioning their existing practices.

  1. Traverse the gap  

At this stage you have worked out the basis of any data analytics project plan, now it’s time to execute. Getting the right people to do the work is vital, regardless of whether you are looking at external suppliers or internal teams. Providing teams with a detailed brief of exactly what you want to know, what you must work with, and how that will move the dial on your business, is key to cost-effective data analytics.

With this information you can cut out a lot of the wastage. Instead of getting large teams claiming to solve all your problems with data analytics, work with a small nimble team that can solve specific problems that are most likely to move the dial.

How this hits the ground

We have seen a huge variance in the timeliness, success, adoption and cost of data analytics across several businesses. It is too easy to get caught in an analytics death spiral and end up with a bunch of fancy algorithms that don’t add value to your business. Businesses that avoid this bite off little chunks of a larger data analytics strategy, rather than getting caught up in all of the hype.