About Data Science

COVID-19 is the most serious health crisis of our lifetime and has disrupted economies all over the world. As the economies begin to reopen, there is no greater need than to reimagine sustainable business models that can survive additional disruptions.

Companies and employees need to innovate at a serious pace to stay relevant in the marketplace. Many of these challenges are tactical operational challenges and while AI isn’t going to answer all challenges, automation and intelligent decision support would be used at any point to improve speed and safety of execution.

Analytics has seen significant adoption and business sponsorship in the past decade. Data Scientists are being hired to build analytics models—largely machine learning and, increasingly, deep learning—capable of turning vast amounts of data into insights.

Business leaders continue to be invested in the concepts, but are not without concerns around cost and results. The need for integration of analytics solutions in business can’t be overstated as a way to drive end value.

Success with analytics requires data scientists and an upstream team of data engineers, data architects and downstream teams of software developers working in agile scrums to implement their frameworks and solutions.

As a result of this dynamic, an analytics initiative can easily take on its own life and become a significant cost center. It is therefore critical to ensure that analytics initiatives consistently show business value and business outcomes are attributed to these specific initiatives.

This is where a need for a more operational role emerges to help shepherd these initiatives to implementation -- the analytics translator and interpreter.