Traditional Business Intelligence vs Modern Data Sciences

I have been a BI professional for a few years now. My exposure, like most of us, have been towards the traditional side of BI where the core job revolves around transforming and loading transactional data into a Data Warehouse, and developing reports on top of it.

Over these years, a few key trends like Social, Mobile, Cloud, Collaborative, Self-Service, In-Memory, etc. have had a significant impact on the BI market. As a result, the horizons of BI have expanded, and a lot more is now in the realms of possibility. These trends have also contributed towards the changing (increasing) expectations of Business users. Fair is to say that the metamorphosis of BI Application triggered by these trends has made the world of BI so much more interesting, challenging and rewarding.

From time to time, my interest has meandered from one trend to the other and each one has looked to have the promise of crossing over from being an academic fad to a practically implementable functionality. Of late, it was the Data Sciences which captured my imagination, and though am a bit late in the game as I realize, I decided to catch up and understand what Data Sciences is and how it stands to impact (transform) Business Intelligence.

As a convenient start, I have put across this table to differentiate between traditional BI and Data Sciences. I thought this comparison will be a worthy starting point since we invariably tend to understand the unknown (like, data sciences) by connecting it with the known (like, Traditional BI).

  Traditional BI Data Science
Volume of Data Handled Typically in GBs Typically in TBs, PBs
Diversity of Data Sources Lacks Diversity; Limited to Structured data that fits in conveniently into rows and columns of a Relational Database Highly Diverse; Combines Structured and Unstructured; Unstructured includes Social Media, Audio Files, Documents, E-Mails, etc.
Role of Statistics and Programming Minimal or no role of statistics; Programming limited to PL/SQL Statistics is a major influence; extensive programming in R, SAS, etc.
Interpretation of Results BI Practitioner’s role is limited to creation of reports. Interpretation of results left to Business Users. Data Scientists interpret results and communicate them to Business Stakeholders
Use of Visualizations Charts/Graphs used extensively but no usage of advanced visualizations Advanced visualizations required to communicate results, primarily because of the complexity of data that is being analysed

Surely the above table is simplistic and incomplete. But it gives me a handle to hold while I move forward on my exploratory journey of Data Sciences.

I will delve deeper on the topic in subsequent posts. Would love to hear your thoughts.

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