How to Maximise the Return on Your Data Investment

In our age of big data and artificial intelligence, data scientists and analysts are like gold dust. Their ability to derive sharp and coherent insights from seemingly endless silos of data makes them invaluable to businesses. Good analysts can help companies to react faster to market changes, to better understand their ideal customer, and to grow at an unprecedented rate. 

And all that is just scratching the surface.

But few companies are raking in new business and firing up revenue off the backs of their analysts. In fact, many are seeing very little benefit at all. So why aren’t more companies cashing in on the immense talents of their analysts?

Why are some companies falling behind while the others reap the enormous benefits of big data?

Too much preparation

Analysis projects usually start at the same point: collecting and sorting all the data. Businesses today could conceivably pull data from hundreds of different sources, each monitoring thousands of unique data points. The obvious barrier to using such vast data silos is the fact that most companies have a largely manual data preparation process; with each new data connection comes a marked increase in labour.

Right here, in the confounding world of data preparation, is where data scientists spend up to 80% of their time. Staggeringly, three quarters of data scientists are also quoted here as finding data preparation ‘the least enjoyable part of the job’. This raises two major issues.

1. Not only are analysts spending just one fifth of their time actually analysing, they’ve just spent the last few days begrudging their work. With the inevitable drop in morale and enthusiasm, it’s unreasonable to expect top-tier performance. 

2. Cost. Top data scientists don’t come cheap, and if they’re stuck doing grunt work all day, you’ll see a poor return. Not only that, but their small window for analysis means fewer business-driving insights and, consequently, hampered revenue and growth. 

The key here is to help your analysts balance the load between data preparation and analysis.

Using the wrong tools

Remember that to anyone with a hammer, everything is a nail. Without providing the tools and processes to help your analysts perform, you can’t expect optimal results. While every team within every company is unique, there’s a universal approach for figuring out exactly what your analysts need: asking them!

Opening up clear and transparent communication with your team goes a long way. Discuss the expectations from both sides and the resources required to meet them. It doesn’t matter if there’s not enough budget for this software platform or that VA; what you will achieve is aligning your expectations with the actual capabilities of your team. This is also a great time to discuss the 80:20 problem we highlighted before. 

As long as there’s a net gain for the business, there’s no better recipe for success than building a great team and arming them with the resources they need.

Redundant data

It’s not just the amount of information businesses are processing these days - it’s the constant rate at which it’s changed and updated. Many companies use real-time data as part of their analysis. They want to know what’s happening now and what it means?

In any dynamic and fast-paced marketplace, real-time data analysis is crucial to gain any advantage over the competition. If you’re relying on data that’s even a few days old, your analysts will reach conclusions which are at best slightly out of date; at worst, they’re completely redundant.

We’ve already mentioned that in order to get the best out of your analysts, you need to help manage their time and tools more effectively; we also need to ensure they have the most relevant and current data possible. 

We examine all three of these points - plus a great deal more - in our recent white paper. The paper not only describes seven major barriers to getting the most out of your analysts, but also advice on how to overcome each of them in turn. We also look at solutions for taking things a step further: building artificial intelligence and machine learning tools into your process so you can leapfrog the competition.