Background

Business analytics skills, technologies, and practices have improved at an exponential rate ever since they were first used in major applications in the late 19th century. These capabilities quickly evolved to focus on improving production and its efficiencies, quantities, and cost-effectiveness.

Leading up to the information age, highly segmented workflows produced operational reporting that was stored in siloed informational warehouses. The Business Analyst (BA) then extracted insights from these datastores for domain operational improvements in specific Lines of Businesses (LOBs).

In the early 2000s, data science emerged and provided tremendous capabilities for producing higher quality insights, and businesses started to offer these services internally to the LOBs through a hub-and-spoke data team structure. As the demand for analytics services increased, these specialized central data teams could not scale to satisfy the LOBs. As a result, LOB analysts had to learn new ways to help improve the processing, storage, and extraction of insights.

With the big data ML/DL era producing much larger and complex datasets, and the explosion in complexity of modeling algorithms, businesses started to see a massive gap in capabilities between the BAs and data teams for similar tasks. With today’s low/no code tools and modern, ever-expanding AutoML Cloud services, you can grow your analyst skillset to an extraordinary level and extract insights by performing “data science-like” tasks that previously required specialized knowledge held only by the central data team.