Organizations, governments, and individuals generate massive collections of data as a byproduct of their everyday activities. In 2011, Bloomberg Businessweek estimated that 97 percent of companies with revenues exceeding $100 million would take advantage of this massive data by using some form of business analytics.
The motivations behind these initiatives were to use intelligent business analytics techniques and technology to improve decision-making and generate a competitive advantage. This need for business analytics is further exacerbated by the evolution and expansion of its very definition, and all that it includes, as technologies such as artificial intelligence, biometrics, the internet of things, communications, and security become more robust.
A 2017 Forbes survey, however, found that only 53 percent of companies have adopted analytics in their business practices. Similarly, based on a survey in 2017, Tech Pro Research concluded out of all of the companies that gather data aggressively, only 39 percent analyze the data for strategic business insights.
A central reason for this lack of adoption is the lack of qualified data scientists. Analytics, a magazine by reputed INFORMS, argues that data scientists either do not know the tools necessary to tackle data or treat business analytics as science projects rather than a business-influencing capability. Perhaps this would also explain the 2012 University of Arizona projections that by the year 2018, the United States would see a shortfall of 140,000 to 190,000 people with deep analytical skills.
Organizations seem to be tackling this problem with a two-pronged approach. First, many organizations have outsourced their analytics operations to major tech companies such as Tableau, Domo, Logi Analytics, and Pentaho. These tech companies pioneered the idea that big data presents an immense business opportunity to generate a competitive advantage and have trained scientists that can leverage business analytics tools to solve business problems.