‘Data science’ misses half the equation: an argument for ‘decision science’

Data scientists are a start, but when you add in design thinking and behavioural science you get better consumption of insights and informed decision making, writes Deepinder Dhingra in this guest post.


A lot of focus has been given to data sciences and data scientists, which is essentially an intersection of math and technology skills. But what organisations need are individuals who in addition to math and technology, can bring in the right business perspective.

While having the right data at the right time is very important, what is even more important is the ability to derive meaningful insights from it. It is for this reason that having an understanding of the right analytical technique becomes critical.

Any analytical initiative becomes futile if organisations do not have at their disposal the individuals, with specific skill set, to cull meaningful insights from the ever-growing chunks of data. These individuals must have the ability to artfully blend left-brained and right-brained thinking to solve complex business problems. They should possess the requisite analytical skills to understand, translate and generate insights that can then be consumed effectively. They are what we call the ‘decision scientists.’

An example of bringing in the business perspective: an FMCG company’s promotion strategy could be very intuitive such as advertisements in newspapers, magazines, television, billboards outside shopping malls , airports and other public places; or inducting a celebrity as the brand ambassador for its promotion, and so on. But if the same FMCG company is operating in multiple geographies and demographics, the analysts need to understand how these will have varying impact on the buying decision and consumption of the product or the brand. Hence the right business context will be required if the analysts were to suggest a promotional strategy to the decision makers.

In this regard an interdisciplinary prowess of math, business, and technology makes decision sciences and, hence, decision scientists the need of the hour.

In addition to this, there are certain skills that distinguish decision scientists from all others. For example: a quantitative and intellectual horsepower; the right curiosity quotient; ability to think from first principles; and business synthesis among others. This is pivotal to help marketers in uncovering customer insights – ergo, organisations should intently focus on nurturing such talent pool.

Post acquiring these rare breed of professionals, organisations should endow them with the right:

  • Skill-set and mind-set – while making sure the learning is continuous, deep and fast, and
  • tool-set and data-set – to perform the right analytics and extract meaningful insights.


Balancing the creation, translation and consumption of insights

With the right people, processes and methodologies, analytics tools, techniques and technologies in place, the analytics teams can generate insights for any marketing problem. They can translate and communicate the insights to the senior management and key decision makers using fancy information dashboards and visualisation tools. But the efforts can become ineffectual if business users, for whom the insights have been generated, do not show equal fervour in consuming it.

Consumption of analytics is a recurrent and overarching process that includes the creation and communication of insights; its implementation and measurement; aligning incentives to endorse a data-driven decision making culture; and lastly the development of cognitive repairs to let facts rule the process.



Marketers are constantly facing an influx of rising complexities on account of inherent dynamism of marketplace itself. To fully harness the business benefits that data can offer, organisations will need a complete ecosystem comprising the right integrated processes, technology and people with the right mind-set and skills.

This certainly calls for a fundamental shift in conviction and belief of the marketers to embrace analytics and make it a pivotal part of their operations. But if marketers really wish to institutionalise data-driven decision making, they need to unlearn and relearn the new ropes of decision sciences, and do it quickly.


Deepinder Dhingra is head of products and strategy at Mu Sigma.