Get data science out of IT departments and into boardrooms
Where do your data scientists sit? Perhaps they occupy a typically gloomy, computer-filled basement, or maybe they have a glassy building all to themselves. Either way, you’ll not always see business decision-makers walking the same corridors. After all, analytics is best left to the experts, isn’t it?
Yet, back in the boardroom, the statistics and insights that the managers have ordered from the data science department make little sense. How can these be turned into profit? The truth is that where data scientists sit in your organisation is vital – strategically, as well as physically. When they’re separated from decision-makers, data analytics won’t line up very easily with business goals.
I recently had the chance to speak with SAS Collaborators and about optimising the role of the data scientist. A catalyst for positive change, the influence of data professionals should be felt throughout the entire organisation.
One analytics strategy for all to participate in
Data is nothing without strategy. While many business decision-makers stress the importance of data, pointed out that often this appears to be a case of “data FOMO”. These business leaders have a fear of missing out on the potential insights their data has to offer… but in reality, they have no idea what insights they’re looking for.
For all analytics use cases there needs to be a clear understanding first of exactly what the business problem is that you’re trying to tackle, and how analytics can help. Only then can the relevant data be used and the right models created, refined and refreshed that will lead to key business insights to help resolve the problem.
Analytics is for all, as we become more data-driven and analytical in our thinking and our work. But, it’s not up to each department to ‘shake the data hard enough and get results’. Companies must strategically , turning the data into insights which are valuable to all who could benefit from them. This means starting with the right high-quality datasets, selected in collaboration with decision-makers. Then, model design and deployment should prioritise the achievement of the agreed business goals. Finally, the findings should be presented comprehensibly, using visual analytics or simple interfaces tailored for those who need to understand and then act upon them.
Speed is of the essence in analytics, and yet so many projects become a “poisoned chalice”, as stated: passed down through teams and taking years to complete. This can be sped up through collaboration at every stage. Take the furlough scheme. Desperately needed to preserve jobs and businesses through COVID-19, the efficient collaboration of hundreds of policy-makers, data scientists and employers helped within three to four weeks, saving countless jobs.
We are all data scientists
As data becomes more and more pivotal in business, it shouldn’t be left up to the tech experts to advocate its value. Asking data scientists to take on the role of a business analyst is only half the solution; the other route to truly data-driven success is to nurture the evolution of new analyst roles outside of the data science department.
- . While working in their own field – say, supply chain, or customer service – citizen data scientists might create models that leverage analytics specifically for their own department. They can rely on the strength of their contextual knowledge and wider understanding of business priorities to tailor the analytics process to produce insights that their team can act on immediately.
- Data translators. With a breadth of knowledge spanning technical data science and top-level business management, a data translator can turn insights into a more “business-friendly framework”, as Jen noted. As intermediaries between data scientists and decision-makers, they guarantee a process that has impact at scale in the organisation. At the same time, data translators are by default teaching and nurturing each group to think more collaboratively.
- Data literate employees across the business. A sensitivity for the raw data generated from customers or operations isn’t just for business analysts. It has the potential to revolutionise business thinking in all departments and among all staff members. Making this is arguably one of the greatest drivers of reliable growth that businesses can commit to today.
What place does data science have in the future?
Companies aiming to be competitive in the modern day must have a greater focus on data, analytics and insights than ever. But the role of the data scientist is beginning to change.
Ultimately, coding could become a smaller part of what data scientists do, as models become more interface-based. This means data scientists will soon be working to fit machine scale data into existing models, and interpreting the results. They will become responsible for the wider process of managing the insights, and they’ll be constantly reviewing what they mean for the end-user.
In the boardroom, data scientists are invaluable. As Neil pointed out, their knowledge of reference cases can provide the evidence to convince the C-suite of key decisions, informed by data insights. But data science can also have a huge impact on public perception. Organisational ethics is fast becoming a non-negotiable for customers. Sensitivity for how data science capacity can be leveraged worldwide helps organisations to use , to make a difference on issues from deforestation to reducing injustice.
Data-driven businesses are leading the way in the business landscape, and data science is at the helm. When data scientists are stuck in the engine room, their influence on navigation is wasted. By encouraging collaboration, skill-sharing and a broadening of roles among data scientists and others, organisations can be confident of their bearings as they chart a way towards their goals.
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