Data science, as Deepinder Dhingra recently blogged, “is essentially an intersection of math and technology skills.” Individuals with these skills have been labeled data scientists and organizations are competing to hire them.
“But what organizations need,” Dhingra explained, “are individuals who, in addition to math and technology, can bring in the right business perspective. These individuals must have the ability to artfully blend left-brained and right-brained thinking to solve complex business problems. They should possess the requisite analytical skills to understand, translate and generate insights that can then be consumed effectively.” Dhingra refers to them as decision scientists who complete the data-driven decision making process started by data scientists.
Data scientists, as Dhingra explained, analyze data and then “translate and communicate insights to the senior management and key decision makers using information dashboards and visualization tools. But the efforts can become ineffectual if business users, for whom the insights have been generated, do not show equal fervor in consuming it.” This is where decision scientists are needed. “Consumption of analytics,” Dhingra explained, “is a recurrent and overarching process that includes the creation and communication of insights, its implementation and measurement, aligning incentives to endorse a data-driven decision making culture, and lastly the development of cognitive repairs to let facts rule the process.”
While data science is vital to data-driven decision making, Dhingra argued it is only half of the equation that decision science is needed to complete. “To fully harness the business benefits that data can offer,” Dhingra concluded, “organizations will need a complete ecosystem comprising the right integrated processes, technology and people with the right mindset and skills.”
An appreciation of scientific principles needs to pervade all aspects of data-driven decision making. Which is why, especially when dealing with big data analytics, organizations need both data science and decision science.
