Today, emerging trends in business analytics are more than just challenges and failures. Failures and shortcomings are looked upon as areas for improvement as the industry continues booming. Businesses have learnt constructive lessons over years. The phenomenon has outgrown into an industry. There are indications of immense growth for an industry that is dominated by a business need for knowledge discovery.
Analytic firms have shifted their focus from serving everyone, to serving a particular industry. The idea is very simple: Learn about one specific industry and optimize the analytics offering. As the industry tries to incorporate task-specific knowledge in their analytics offering, the focus is on industry verticals. It helps industry gain domain knowledge that only businesses in a particular industry possessed. It also allows sharing of best practices and integrating cross-industry experience. Domain knowledge has helped realizing the potential of analytics not as an afterthought reaction, but as a preemptive action. The industry has plunged into optimization of inter and intra-business functions.
To allow business users analyze data and quickly gain insight, the industry has moved from reliance on data mining experts to business users. Industry gurus describe it as descriptive analytics, whereas some: predictive analytics. Regardless, of your selection of terminology, the actuality is, business users require models that closely resemble market reality. The need is to adopt comprehensible models that are relatively easy to understand. Employment of visualization to represent evidences on critical attributes has become a need for business users, and industry has reacted to address it.
‘You cannot analyze what you do not collect'. Collection of rich data has become critical. It has resulted in better data collection efforts, and emphasis is rendered to generation and storage of data. The industry has realized that they need to design data analysis into their systems. There is a great potential in overlaying multiple sources and employing a sophisticated IT infrastructure to make timely analysis feasible.