Business Intelligence andBusiness Analytics

Business Intelligence and Business Analytics both look the same but have different standing in terms of Decision Making.

Business Intelligence has been there for decades with respect to tools and technologies. Business Analytics has also been there but mostly tagged with manual effort by Functional, Business or Financial Consultants.

Howard Dresner coined the modern definition of the term “business intelligence” in 1989, at least in the sense it is typically used in industry today (“end-user access to and analysis of structured content, i.e., data”).

As there weren't a lot of tools available in the market for Analytics, so many companies were making fortunes for this piece of manual effort. Now the question comes, when Business Intelligence tools were there, how come Business Analytics was manual and why Intelligence and Analytics cannot be done using the same tools? For this, one must first understand the differences in how Intelligence and Analytics processing works.

This is confusing right, yes, it is 😊?

Business Intelligence is done on historical data and Business Analytics is done using that generated intelligence i.e., to predict or forecast the potential KPI(s) for the future.

Previously organizations used to extract data and give to the Analytics companies which then used to run Data Science models manually or via their own in-house products. Now, with the introduction of Big Data, with the introduction of no NoSQL databases, with the introduction of tools and technologies which can host and as well process structured, semi or unstructured datasets now customers themselves has become self-sufficient to build in-house data science, machine learning, deep learning etc. models.

This new era has brought Business Analytics right to the front-end where now Business Intelligence is an input for it.

Now, Business Users can plan their Use Cases vs ROI vs TTM within their own organizations, saving millions of dollars. At the same time, customers should not ignore the importance of Data Strategy, else they will face Data Swamp which will impact 180 degrees on their ROI.