While big data is undoubtedly high on the list of invaluable tools a business needs today – and has been for some time – many companies are still struggling to use it. In fact, according to Square Root’s Data Chasers research, while 92% of companies wholeheartedly believe that big data will revolutionise their success, only 40% are actually taking advantage of it as it was designed to be.

There are obstacles that companies have been stumbling over for years, seemingly unable to overcome – but progress can be made with the right insights and perspectives. Here are the top four roadblocks businesses are facing with big data, and how they could finally beat them.

It’s too little, too late to make the change

Big data isn’t some shiny new accessory to speed up and improve your productivity. On the contrary, you can instead think of it as the foundation and structural beams of your company’s infrastructure – something that’s clearly not easy to replace on short notice. While younger businesses are better able to embrace the change, with new startups figuring it into their original construction, many businesses who’ve been in the industry for years are struggling to remake themselves as big data compatible.

It’s a steep uphill climb, but the key for established businesses trying to make the switch is to take meaningful but measured steps. You didn’t build Rome in a day and you’re not going to rebuild it in that time; instead, evaluate what parts of your business could benefit the most from Big Data, and what practices could make real changes in your productivity and interactions with your audience now. Apply those small but crucial changes and slowly work your way backwards. It won’t happen all at once, but it will give you valuable results where it counts.

The experts are in short supply – or aren’t the right kinds

Your current data experts aren’t to blame; they are skilled in their chosen profession and you hired them for a reason. However, the issue lies in the data world moving out from under them. New practices, tools, and developments in big data have made previously invaluable skills irrelevant, and calls for a new crop of data experts fluent in the modern lingo and tactics. The obvious answer is to hire on these professionals, right?

However, the universities are having trouble keeping up. Students are graduating as quickly as they can and older professionals are taking new courses to bring themselves up to date, but the issue remains. Businesses who want to leverage big data to their full benefit will have to accept that the right experts come at a high price and competition is tough, but it’s necessary.

They’re not sure what they need big data for

Unfortunately, many companies are approaching Big Data with the mindset of “if they have one, I want one too!” It’s undoubtedly a tool every company needs, but for different reasons, and if you acquire big data without knowing the problems you want solved or the insights you’re looking for, it’ll be useless.

While it’s tempting to build up big data as quickly as you can, it’s more important to put on the breaks and have your company take a long look at what actually needs accomplished, from developing converged systems to ironing out operational hiccups. If there are gaps in your information, then this is a place big data can help as well. Once you have a solid look at your goals, you’ll know how to refine the tool to work for you.

They take too much too fast

Think of big data like a massive haystack; the data you benefit from is also hay, but a specific kind of hay. Using big data properly is asking for the right type of hay, from the right haystack, and extracting it with the right tool. Unfortunately, many businesses fail to realise this and believe that all the hay is valuable – and the more haystacks, the merrier. In other words, companies often use too many data sources, too many data collection methods, and put in too many data requests, giving them plenty of results but none that are precise or actionable. This leads to confusion and false starts that hinder rather than help.

Instead, companies need to refine the way they use big data – and not get too excited. It’s about the right answers, not all the answers.