Why decision science is increasingly crucial to modern research

May 5th marks International Market Research Day. While it may not be up there with major anniversaries in terms of top-of-mind awareness, it is nonetheless a date in the calendar worth noting in an era where data increasingly rules all our lives.

Globally, approximately US$76 billion was spent on market research last year (2021), double of what it was in 2008. This is still only about 10% of what is spent on advertising and a fraction of overall marketing spend. Yet, research is crucial to sustaining and advancing both. The days when educated guesses were enough to launch a product or campaign are well and truly over.

Modern market research has evolved greatly over the last decade and is now a strand of what is referred to as data science. MR tools are both more sophisticated and more robust but, as in previous eras, analysis and understanding typically take precedence over real-life business needs.
However, with so much data now pouring into the hands of decision makers, the need for a new type of researcher has become more pressing. Researchers who not only have the expertise to analyse data but can apply it to the problem at hand.

This means finding data specialists who are more adaptable and possess wider skillsets beyond statistics, psychology, or behavioural economics. They not only have to be able to adopt a more holistic perspective of the challenges facing their organisation or business but should also be able to apply narrative and visualisation techniques that help key stakeholders to make faster, more informed decisions—so essential in a digitalised world.

This change has seen the emergence of decision science in recent years. Decision science is a new but increasingly important development that is helping to bring research closer to the centre of day-to-day management. It is a discipline that requires its practitioners to possess both business acumen and acute analytical capabilities.

Increasingly, decision science is being applied not only in business, but in education, health, the military as well as wider public policy. A variety of data (not just primary research) is typically used by data scientists to frame the business question at hand.

Crucially, the decision scientist’s expertise is relied on to select the data that is most relevant and based on its ability to address the problem at hand. Indeed, one of the great challenges facing
organisations today is the sheer volume of data and the often-conflicting signals it can send. This requires a unique ability to sort the wheat from the chaff and pinpoint what really matters.

In today’s business environment, a market researcher not only has to align their analysis with the business/sales data, but also compete with the insights being gathered via numerous new touchpoints and platforms employed by organisations to listen to and engage with audiences. Some of these can provide robust information while others can be easily manipulated or are overly amplified to the extent that a minority perspective can sound louder than an elephant’s stampede.

This is where decision science expertise can prove valuable. Where possible, decision scientists look at the entire information and data ecosystem and seek to make sense of it. How does one behaviour interact with another? Why does this data point appear to conflict with another? Which insights should be afforded more credibility?

Furthermore, a good decision scientist needs to be able to translate data meaningfully, so it offers maximum clarity and is easily processed by all stakeholders.

Advances made by data science in terms of new analytical tools, artificial intelligence (AI), etc. mean that data quality has improved greatly and can go be more expansive and drill far deeper than it could even a decade ago. You no longer need the resources of Google to better understand what is going on with your customers or your competition. Even small businesses now commonly employ smart data analytics to track their progress.

But the thrust of modern business and global commerce is relentless. Disruption is no longer a ‘black swan’ occurrence that impacts a sector maybe once a decade. In some industries, disruption is ever-present. Decisions need to be made quickly to not only move things forward but sometimes to simply stay in the game.

For this reason, data scientists must work hand in hand with decision science specialists. The former to ensure data quality and accuracy; the latter to translate, process, and narrate meaning to the information.

By nature, researchers are often cerebral, thoughtful creatures and prefer to avoid the limelight. But with data now so vital to decision making, a new breed of researcher is emerging with a very different lens on the world and a desire to make a difference in real time. Decision science is taking researchers from their traditional back of house status and giving them a seat at the big table where they play a part in the major decisions.

As data continues to proliferate and become increasingly complex, decision science will be ever more present in organisations and emerge as a key discipline in the world moving ahead.

David Black is founder & CEO of Blackbox Research