Quick note on evolution of Business Intelligence & Microsoft’s vision for BI space:


I attended “Enabling Familiar, Powerful Business Intelligence hosted by PASS BA VC last week & I got to listen to Microsoft where they shared their vision for the BI space, so I thought of posting this quick note about it:

“Corporate BI” has been around for may years. This space has established players like Microsoft, SAP, IBM, Oracle. But in recent times, “Self Service BI” space has been gaining momentum. Players like Qlikview & Tableau that lead the Self Service BI space have been ranked as leaders in the Gartner 2014 magic quadrant. Microsoft has also been making serious advancements in this space since last few years & with their latest offering called “Power BI” they have shown that they putting their bets on Self Service BI space. So, as Microsoft said in the event, they view themselves as the only player that offers a full suite of Corporate BI as well as Self Service BI:

Evolution of BI

you can watch the recorded session here: http://www.youtube.com/watch?v=0yKhxSPlykg

Found something interesting by exploring a “List of companies by revenue” Data Set:


I like exploring data sets to find interesting patterns from them. To that end, I was exploring a data-set: List of companies by revenue and I added a column to calculate Revenue/Employee to explore the dataset:

And I found an outlier!

Here’s the outlier: Exor

Here’s what it’s interesting:

It’s revenue in 2012 is: 109.15 billion USD

And number of employees is just 40!

Just think of Revenue/Employee !

To put things in perspective, Lets Compare that with its neighbor in the data-set:

Rank | Company | Industry | Revenue in USD billion | Employees

48Koch IndustriesConglomerate110.0060000.00
50Cardinal HealthPharmaceuticals107.5540000.00
51CVS CaremarkRetail107.10202000.00
52IBMComputer services106.92433362.00

I got to know about this by quickly creating a data visualization to explore the data-set:

list of companies by revenue

And removing Trafigura, Vitol and Exor, this is what we have:

power view excel 2013 rank revenue employees

Observation: oil and gas industry have relatively higher revenue/employee ration.

That’s about it for this post. Thanks for reading about my data exploration!

Examples of Machine Generated Data from “Big Data” perspective:


I just researched about Machine Generated Data from the context of “Big data”, Here’s the list I compiled:

– Data sent from Satellites

– Temperature sensing devices

– Flood Detection/Sensing devices

– web logs

– location data

– Data collected by Toll sensors (context: Road Toll)

– Phone call records

– Financial

And a Futuristic one:

Imagine sensors on human bodies that continuously “monitor” health. How about if we use them to detect diabetes/cancer/other-diseases in their early phases. Possible? May be!

Interesting Fact:

Machine can generate data “faster” than humans. This characteristics makes it interesting to think about to analyze machine generate data and in some cases, how to analyze them in real-time or near real-time

Ending Note:

Search for Machine Generated Data, you’ll be able to find much more, it’s worth reading about from the context of Big Data.





See what went into building WATSON, an advanced machine learning & natural language processing system powered by Big Data!


Do you know about Jeopardy! quiz show where a computer named Watson was able to beat world champions? No! Go watch it! Yes? Nice! Isn’t it a feat as grand as the one achieved by Deep blue (chess computer); if not less?

I am always interested in how such advanced computers was built. In case of Watson, It’s fascinating how technologies such as Natural language processing, machine learning & artificial intelligence backed by massive compute & storage power was able to beat two human world champions. And as a person interested in analytic’s and Big Data – I would classify this technology under Big Data and Advanced Data Analytics where computer analyzes lots of data to answer a question asked in a natural language. It also uses advanced machine learning algorithms. To that end, If you’re interested in getting an overview of what went into building WATSON, watch this:

If you’re as amazed as I am, considering sharing what amazed you about this technology via comment section: