Embracing the new technological order

Both sectors also face broadly similar challenges. On one level, we are having to confront the reality of increasing globalisation and commoditisation of data, while simultaneously managing the impact of huge volumes of messy, noisy data from the internet and social media.

On the other, the widespread availability of cheap technology has lowered barriers to entry, serving to attract new entrants in the form of technology providers and the DIY brigade. The traditionally conservative market research sector has had to scramble to embrace this new order.

No doubt media measurement providers are experiencing the same concerns, judging by the proliferation of trend and management consultancies, major PR consultancies, and analytical providers who, by offering media evaluation in its various forms, appear to pose a serious threat to the status quo.

There is, however, one area where I believe the market research community is further ahead of its media measurement counterparts: investing in, and adopting technology that is fit for purpose.

In a previous market research role, my job was to integrate the various technologies that accompanied nearly 30 acquisitions.  While that does not make me an expert in the needs of the media measurement sector, it does qualify me to draw some parallels between media measurement and traditional market research, particularly where technology is involved.

Some years ago, the market research industry recognised that it needed to solve the problem of moving data (and meta data) between systems. The Utopian ideal of having a single system to manage, collect, process and deliver everything is still elusive, and perhaps even risky, so many companies use different, best in class, software to perform the different stages of the process.

Historically, vendors had made this hard/impossible to do by using proprietary data formats. But, in arriving at this conclusion (an easy way to move data around), the industry also accepted that there would be many competing systems in the market, each offering strengths and weaknesses, and many would be niche-specific. 

Against that backdrop, a non-profit group was formed to tackle this problem. It devised a standard that later became known as ‘Triple-S’. Now, 10 years later, more than 80 companies use the standard, setting a benchmark which organisations such as FIBEP (the world’s largest association for media intelligence and communications insight) could usefully examine, and re-engineer if necessary, to create a protocol that meets its own objectives.

These initiatives, however, demand considerable input and take a long time to come to fruition. That said, there is still some way to go before our industry can claim to have harnessed all the technology now at its disposal.

Take mobiles and tablets, for example.  The harsh reality is that the vast majority of our dashboards do not work well on these devices. We need to adapt and take on board the disciplines of adaptive and responsive design. We must refocus on mobile as a serious platform for delivery; this is the new world of ‘Mobile First’.

Or consider IBM’s Watson Project to understand where ‘Big Business meets Big Data’ is heading. Watson is a remarkable question answering (QA) computing system that IBM has built to apply advanced natural language processing. Watson is expected to go on sale in the next year or two and, according to John Kelly, IBM’s Head of Research Labs, could help decision-makers sift through enormous piles of written material in seconds.

Although Watson’s primary focus is on solving healthcare problems in the third world, I feel sure that this – or similar technology – will have an application in media measurement before very long. After all, making sense of huge piles of media data is what our industry is all about.

But all is not lost. Despite the wealth of technology available to us, ours is still a world where the human touch remains key to our success. This applies not only in the context of interpreting and analysing media data, processes with which machines still struggle, but in delivering that oh-so-important personal customer service component.

Pat Molloy is consulting chief information officer, Salience Insight