The future is data interpretation

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Over the past few years, I have had the privilege to mentor undergraduates at my former university in Lancaster, and look forward to doing so again this year. The mentoring scheme offers students an opportunity to speak to – and learn from – analytics professionals across a range of industries. For the mentors, it’s a great way to get a better understanding of what’s important to young people who are looking to enter the analytic arena of market research.

I’m often asked how I see people-based jobs within analytics and data provision developing in the future. At its heart, this question challenges us to consider the role that technology is playing – and will play – on analytic-based activities currently undertaken by humans.

At a recent meeting, I faced these technological advances first hand. A partner organisation demonstrated a platform designed to analyse customer-purchase data. What I saw was a pre-programmed computer demonstrating multiple data retrievals and repeating the task at speed. Compared with other programmes I see every day, this demonstration hit home because it was performing the job I had done in the early part of my career – a role that was once seen as complex and that had helped identify me as a specialist.

The programme clearly needed some help to get up and running, but it was a stark reminder of the positive progress in our analytic space and how roles evolve.

The requirements of data analysts are advancing, and the base capabilities will continue to rise as new approaches to analytics develop. Look at our industry job descriptions; data scientists are all the rage, but – just like in the world of science – new techniques will come along to challenge existing practices and roles will evolve.

This example of seeing my analytic work superseded by technology tees up a broader challenge for analytics across our industry. Automation of analytical processes leading to faster data is a good thing, but – in isolation – it only goes so far. Data is only as valuable as the insights you can gain from it. If you can’t interpret the data, does the speed at which you get it really matter as much?

Being able to find, and then tell, a compelling story from the data output is critical for it to be useful. Most of us want our data in a format that makes finding what is important simple to access. The pace of complex analytic delivery is accelerating, but keeping pace with the right visualisation is another challenge when it comes to people’s roles and technology use.

Existing charting tools that have underpinned presentations for the past 20 years are challenged as an acceptable format, but the one thing they gave was a consistency and common language in visualisation. Without access to visualisation tools that translate data into a usable format, the benefit of improved analytics can be lost.

Across our industry, turning data into an easily understood output is increasingly automated – which is to be welcomed. New visualisation tools are emerging, but we need to know what to use where, and when, to ensure we deliver impactful insight. 

Getting to recommendations from the data that can drive a business – particularly in the boardroom – will mean that people who understand how to interpret data, and how to extract important insights, will still have a valuable role to play.

The industry’s task is to secure the value of analytical research in the future. My advice to this year’s undergraduates will be to grab the opportunity to enter a market research industry in which the sophistication of analytics is fast-moving and evolving. The roles will be exciting, and having the mindset to continually evolve, to deliver the changes, will be essential to ensure the analytic role remains a central pillar in advancing market research.

James Oates is UK analytics director at Nielsen