Data democracy elevates the data scientist
Data scientists develop soft skills to drive organizational change.
Data scientists are in demand. In its 2017 U.S. Emerging Jobs Report, LinkedIn cited that "data scientist roles have grown over 650 percent since 2012" and "hundreds of companies are hiring for these roles" in a variety of industries. The candidate pool is getting deeper as "machine learning engineer, data scientist, and big data engineers rank among the top emerging jobs."
But as more departments and roles are expected to work with data, organizations are seeing an overall increase in data literacy and the emergence of more citizen data scientists. Gartner defines a citizen data scientists as "a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics." These people aren’t replacing data scientists, but they are becoming key partners in developing and testing hypotheses.
This is shifting the definition of data science, and blurring the lines between those with traditional data expertise and business domain knowledge. Sonic Prabhudesai, Manager of Statistical Analysis at Charles Schwab shared how "More business workers understand how to work with data, while data scientists are becoming more familiar with the inner workings of the business."
Today, data scientists are expected to have advanced statistical and machine learning knowledge, but they are also expected to have a strategic mind for the business, including a deep knowledge of their industry. "Data science is more than just number crunching: it is the application of various skills to solve particular problems in an industry," explains Dr. N. R. Srinivasa Raghavan, Chief Global Data Scientist at Infosys. "Data Scientists need to have a thorough understanding of the domains in which their insights will be applied."
The outputs of algorithms and models are only effective if they help solve the right problem in the right context. This means working hand in hand with stakeholders to identify and refine the problem statement and hypothesis at the beginning of the process and keeping them involved throughout the workflow. And at the end of the workflow, it means knowing how to communicate the results to business partners in a way that is relevant and actionable.
"Statistical modeling and machine learning are now becoming table stakes in order to become a data scientist," shares Sonic. "The differentiator is how well those working in the field can communicate their findings in a simple, but actionable way." Instead of handing over results, data scientists will have a core role in how those results are applied to the business.
With self-service analytics tools, both data scientists and advanced users can explore and get a better understanding of their data. This sparks insights that can direct the rest of the analysis and ultimately, impact the business.