In previous years, employing a chief data scientist was viewed as quite a luxury. Today, the role has become a necessity, especially as more businesses continue to accelerate their digital transformation plans in challenging conditions.
The importance of data science and analytics has surged during the impacts of the pandemic as businesses recognise how data is critical for survival and continued progress. With data now influencing major decisions at the highest level, the need for senior leaders in data science has become more important.
Today, it is clear that leaders need to work beyond identifying and measuring analytics. Organisations require chief data scientists, capable of connecting executives and data science teams, an individual capable of defining strategy and executing data-focused plans. As a result, investment into chief data scientists roles has increased as they primarily focus on managing the data systems, creating a clear strategy and improving the overall quality of data used.
A chief data scientist is regarded as having a deeper understanding of how new technology systems such as AI and Machine Learning can enhance data management. This has become more important as ML has continued to influence data quality and navigating big data concepts into real-world ML implementation. The chief data scientist is responsible for the navigation of this process, focusing on data as the primary driver for new initiatives.
Businesses are actively engaging with their customers in new and innovative ways, generating new business models and exploring more efficient ways to launch their products. These processes all require complex data plans and need the support of an experienced and skilled data leader.
When it comes to making important decisions, senior executives are becoming more reliant on the chief data scientist. A study by the IDC discovered that nearly 60% of chief data scientists report directly to the CEO. The position has progressed significantly over the last few years in terms of value and responsibility.
For this year, the priorities of chief data scientists will be focused on discovering ways machine learning can be applied to manage the challenges related to the pandemic and economic recession. One area is identifying churn rate, understand when customers are likely to leave. This type of information requires expertise as well as different levels of technical and data science knowledge.
This year, chief data scientists will need to expand on their existing influence across their business during a critical stage in the economy. Pressure will be higher for them to discover solutions and so challenges will focus on ensuring data science teams are focused and working with the right data sets. At the same time, chief data scientists will be empowered to utilise corporate data assets to make important decisions for their business.