Transitioning Data Scientists to Managerial Roles: Building effective data leadership
By Arijit Banerjee
For 80% of companies, investing in data science talent alone does not generate the required return on investment (ROI), unless it is accompanied by the ability to connect data with business outcomes. With organizations increasingly depending on data to etch out competitive advantage, the data scientist’s role is evolving. From being cooped up in office corners crunching numbers, the role is assuming expanded responsibility – one of supporting businesses in making smart decisions to drive growth and expansion.
However, building effective data leadership is a major challenge for organizations today, given the acute shortage of skilled data science talent in the country. According to Analytics India Magazine, there will be a 45% spike in the number of data science job openings in 2019, with the banking and financial services industry creating the maximum demand. This puts the onus on organizations to proactively transition their data scientists to managerial roles. This is easier said than done as data scientists usually have extensive background in areas like mathematics, science and statistics and analytics but not in business or management. So, how can forward-looking businesses help their data scientists successfully transition to managerial roles? The answer lies in implementing focused learning interventions, starting with the following three skills.
Communicating effectively with technical and non-technical teams
Humanizing mathematical results into actionable business insights is the most critical aspect of the role of data scientists as they interact with stakeholders across the organization. Often, business leaders may not understand the import of a particular set of data. As data scientists move up the ladder, a large portion of their work will not only involve analyzing structured and unstructured information to detect patterns and make predictions but also helping business stakeholders understand its implications. This is where data storytelling comes in.
Acknowledged as one of the top skills required in the industry, a study by Stanford University states that storytelling with statistics results in better understanding, with a retention rate of 65%-70%. For data scientists to acquire communication and storytelling skills, training in skills such as data visualization and usage of relevant anecdotes and interactive dashboards that can convert complex information into consumable narratives is a must.
Aligning data science goals with organizational goals
Data analytics assumes relevance and meaning only in the context of a business. For organizations to benefit from the data they have access to, data scientists must be coached on how to connect complex information back to what the business is trying to achieve. This requires an understanding of the company’s unique offerings and how it stacks up in the market vis-à-vis its competitors. With the right understanding of business, data scientists can leverage data to identify and analyze potential business risks, relate expenditure to process improvement, or identify ways to create customer delight.
At the Rising 2019 event, Sohini Mehta, Global Service Delivery Head of Analytics at Wipro said that technical expertise has almost become redundant with so many automated machine learning platforms making most of the jobs easy and simple to perform. But there is a lot of business thinking that cannot be performed by machines. A data scientist, therefore, needs to be trained on cultivating strong flair for viewing information from multiple perspectives and then narrowing it down to the most useful insights for a given business.
Effectively managing inter-personal relationships
While data scientists are capable of churning out relevant insights, the question is: do they have the skills to deal with friction in the form of multiple perspectives from a diverse set of stakeholders? According to LinkedIn’s 2019 Global Talent Trends Report, the top five in-demand soft skills include persuasion and collaboration.
Clearly, data scientists must acquire skills that will enable them to cultivate, manage and nurture interpersonal relationships - given the diverse set of stakeholders they interact with across functions. One way to build this capability is to design programs focusing on building empathy where the data scientists are coached on being mindful of pitching a solution as an alternative recommendation or helping stakeholders understand that a unique viewpoint can also be constructive. The result: reduced friction and improved cohesion within teams.
Organizations that take a strategic approach to equipping their data scientists with requisite hard and soft skills will create successful teams that are able to synchronize their understanding of technology, business, and human assets to drive growth.