4 Ways To Keep Your Decision Scientists Happy

Data sciences is  the ‘sexiest job’ of the 21st century (at least according to the Harvard Business Review). Data analytics professionals – or data scientists – who are able to extract meaningful insights from ever growing amounts of digital data are hot property.

But the supply of such talent is not able to satisfy the demand, which is intensifying on a daily basis. There is an official shortage of data scientists, says McKinsey, and your company will have to fill this gap.

Decisions matter more than Data

I have worked in the area of decision sciences and Big Data analytics for over a decade, and what companies really need are Decision Scientists and not just Data Scientists.

Data Scientists are certainly able to apply maths and technology to clearly defined business problems. But most problems are constantly changing and poorly defined. For these kinds of problems, mere proficiency in maths and technology is not sufficient. There is a need for a strong business mindset, an ability to interact with multiple business stakeholders, a design thinking approach to clarify ill-defined problems and an understanding of how business decisions are made within the organisation. Decision Scientists are a rare breed, primed for helping the organisation not only with the creation and translation of insights, but also with more effective decision-making. Requiring excellent intellectual business understanding, in addition to technology and math skills, they are difficult to find.

How to delight a decision scientist

If you’ve been lucky enough to find and hire some of these people, you need to make sure you retain this talent and keep them suitably engaged. So, what’s the secret to keeping your Decision Scientists happy? Here’s what works at Mu Sigma.

1. Create a culture of collaboration: Decision Scientists thrive in environments with like-minded people, especially when they are able to team up and bounce ideas off each other. If you leave them to work alone, or in a smaller group setting, you may risk undermining their natural curiosity and leaving them feeling bored. In order to maintain their creative qualities and help them flourish, they need to get involved in brainstorms and collaborations, regularly.

2. Time allocation: Harmonising discovery-driven and problem-driven analytics – Traditionally, analytics teams have always been problem-driven and there is a tendency in organisations to prioritise problem-driven analytics. This is hardly surprising, since there is clearly a stronger business case for investing in an analytics response to a defined problem. More recently, however, organizations have started giving their Decision Scientists the latitude to engage in discovery-driven analytics, allowing them to explore vast amounts of data and identify hidden patterns with a view to gaining new insights. Why is this beneficial? Because new, game-changing insights are more likely to be discovered if you don’t approach them with pre-conceived ideas.

3. Side projects: Side projects—or “Simmer Projects” as they are known—are an interesting concept introduced by Microsoft, which can be effective in keeping Decision Scientists engaged. These projects usually run alongside the core work, but aren’t necessarily expected to contribute to any revenues, in the short run. They may range from coming up with new and innovative product-let ideas to improving current practices that support analytics delivery. They can encompass the entire spectrum of analytics consumption cycle—right from creation and translation, to consumption of insights.

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Some examples of side projects could be exploring unconventional areas where analytics can create a difference, such as “crime”; devising innovative apps that support certain or all types of analytics (Descriptive, Inquisitive, Predictive or Prescriptive) or developing visualization tools or techniques that can enhance analytics consumption. Organizations should aim for continuous innovation by fostering this culture of “Intrapreneurship”

In addition to this, organisations should also encourage their workforce to showcase their creativity in ancillary aspects of work that can enhance productivity and increase operational efficiencies. They should not only come up with ideas, but also work towards implementing them in a sustainable manner. For example: creating a feedback system for the caterers in the cafeteria, building a complaint portal for housekeeping and facility management or building Hadoop clusters using unused / disposed laptops.

While these may initially appear to be minor changes, they are essential building blocks towards making a robust analytical organisation, where individual contribution is duly appreciated and encouraged.

Taking a long-term view on innovation is the key to organisational growth. As opposed to making every employee work on short-term goals, this approach gives them the creative license to experiment and piques their curiosity to learn continuously.

4. Ongoing education: Big data and analytics are constantly evolving, with new technologies and approaches emerging at a rapid pace. Just as software developers need to keep pace with the latest tools, technologies and trends, so do Decision Scientists. Therefore, it is crucial that they are given the opportunity to have continuous learning and development—either from their peers and mentors, or through more formal training programmes or educational conferences.

Conclusion

The predicament of the ever-growing dearth of analytical talent in the industry has emerged as a widespread phenomenon. What is important at this juncture is to identify ways to bridge the gap between demand and supply of analytics talent. It is essential that employers focus on building such talent, while also channeling their efforts towards retaining their Decision Scientists. While these are just a few tried and tested steps that have worked well for us, there is much to explore to set the ball rolling in making, and keeping, your Decision Sciences workforce happy.

Deepinder Dhingra is head of strategy at Big Data specialist Mu Sigma