The benefits companies can achieve through insights-driven decisions can range from identifying new sources of revenue to developing new products and services to optimizing operations -- All of which could create a competitive advantage and have a profound effect on the bottom line. It is, therefore, important for businesses to get the most out their analytics workforce.
Data scientist, in particular, is a role many companies are looking to fill. However, this level of skill is a rare commodity and finding such scarce talent has proven to be a challenge. When data scientists aren’t available, companies should establish a team approach to gaining these skills and look to pool people with different talents and experiences together, creating a collaborative and synergistic environment. After all, innovation is rare where one isolated individual with a defined skill set attempts to solve a problem.
Searching for data talent is only one part of the problem as companies must also place effort behind retaining them. For many data scientists, a main requirement to stay at a company isn’t about compensation or climbing the career ladder to be the next CEO, but rather having management that can help them to flourish in their area of expertise and earn respect from their peers. Also, experience has shown that data specialists are more motivated by solving a difficult issue than implementing and maintaining the solutions they design. It is helpful, therefore, to view data scientists as a separate workforce within the organisation, even though there may not be many of them.
Another important factor to keep in mind on data scientists is that they prefer to have a clear career path mapped out in front of them. This will allow their skills to be utilised and keep them at the forefront of solving the most difficult challenges that the business faces. Management should take a pragmatic view and not be afraid to trust their data scientist teams with difficult tasks, effectively mirroring their approach to other employees who are involved in difficult future orientated quantitative tasks. In doing this, however, organisations must also appreciate that some of the results will be unpredictable and that a “test and learn” approach is required, where management should be willing to fail fast but quickly industrialise the successful “proofs of value.”
In addition to businesses, Universities also have an important role to play in this analytics skills need story. Today, many schools across the UK and around the globe are already stepping up and designing and offering courses to address the data scientist deficit.
It will take time for these positive developments in education to filter through into the workforce and for the deficit of data scientists to be filled. In the meantime, companies should be making the most of their current talent pools by building teams of individuals who, together, can contribute the varied capabilities of a data scientist, and by investing in training and career development. What’s more, retaining the data scientists that organisations recruit will be require a clear career path that meets the need for intellectual challenges and provides the appropriate opportunities for peer recognition.
Ray Eitel-Porter, Managing Director, Accenture Analytics – Accenture Digital, United Kingdom & Ireland
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