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Business analytics is a term that may have become popular in the last few decades, but has existed in business for a much longer time. Analytics started when people started measuring and paying attention to minor details of their businesses; output, time, manpower, costs and profits are examples of elementary analytics. It was the early 1800’s when mechanical engineer Frederick Winslow Taylor developed the concept of business management as a formal subject. After which measuring, analyzing and improving was a path that most businesses took quite naturally.
A brief history of business analytics
In 1908, Henry Ford measured the time taken to manufacture each part of the Ford Model T, and used this data to optimize the assembly line. In the 1970’s, software engineers came into the limelight and started applying programming concepts to build Decision Support Systems for businesses that would help analysts use machines to make predictive decisions. In the 1980’s, data warehousing was becoming increasingly relevant, and all the stored data was being analyzed and used to build intelligent reports. Between 1995 and 2005, Google had introduced Google Analytics, a digital analytics tool that was accessible to anyone, and Gartner a few years earlier introduced the Enterprise Resource Planning (ERP) suite, which was a collection of resource storage, management and analytical tools. Business Intelligence (BI) and Business Analytics (BA) was now trending.
Defining business analytics and it’s explosive evolution
Business Analytics is a discipline of research that proposes solutions to business problems through a fact-based analysis of business needs. Analytically derived solutions help organizations make process improvements, organizational optimizations and improve policy development.
We have seen that analytics and business decision making have gone hand in hand since the industrial revolution. To help structure the business analytics evolution, here are two broad eras of analytics:
The Era before Big Data: For a major part of the digital revolution up until the mid 2000’s, we were analyzing structured data. Structured data is data that can easily be represented on a relational database and can be used or modified using simple, readily-available algorithms.
Managers were able to make decisions with data from sales, customer feedback and other processes that were recorded, mapped and used to produce intuitive actionable reports.
Steadily data analysts and scientists saw the advantages of collecting such data and that brought on the need for data warehousing. As storage technology improved and also became cheaper, companies did not hesitate to build large scale storage models to store all business information. There was however a flipside to all this data that was being collected and used to make decisions. Data scientists were using most of their time to collect data and ensure consistency. This process was slow, could take weeks and sometimes never led to any new insights (also led to frustration). There was a clear need for data collection and reporting systems to improve and be automated to a huge extent.