Digital transformation, or DX, is the process of integrating digital technology into every aspect of a business, with the goal of bringing new opportunities for innovation and growth. It means looking at processes, products and people with new eyes, imagining how technology can change how work is done, the tools used, and the employee and customer experience. While the focus of DX is technology, to be successful, companies need to focus on people as well. One of the key roles companies need to have in order to reach their DX goals is a data scientist.
What is a Data Scientist?
A data scientist is a person who reviews collected data, interpreting it and extracting insights that can be used to improve business outcomes. They look for patterns and anomalies, make predictions and recommendations, and create forecasts. Without data scientists, all that data being collected is just noise.
There are some key skills and abilities data scientists must have. Arguably the most important skill is communication. Being able to explain data analytics to a non-technical business audience is key. In addition, they must have business knowledge so they can make actionable and valuable recommendations. And of course, they must be experts in leveraging data sources and using statistical models.
Data Scientist Shortage
With so many companies taking on DX initiatives, data scientists are in high demand. It is estimated that there will be more than 4,000 DS job openings in 2019. Unfortunately, there are not enough trained individuals to fill these roles. So how can companies get the expert advice they need?
To fill the gaps in data scientist ranks, companies are recruiting people with some of the key skills and training them to meet their DX needs. This includes looking at things like internships to promote on-the-job learning and looking at existing employees across departments who have skills like software engineering, communications or domain expertise. Many companies are creating teams for peer-to-peer learning, creating a larger number of internal data scientist-type roles customized for their company needs.
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