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

Posted on February 19, 2013 by Paras Doshi • Posted in Data Visualization, Power View • Tagged Data mining, Data visualization, Database, DataSet, Google, IBM, Trafigura, Vitol • 2 Comments

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

48	Koch Industries	Conglomerate	110.00	60000.00
49	EXOR	Investment	109.15	40.00
50	Cardinal Health	Pharmaceuticals	107.55	40000.00
51	CVS Caremark	Retail	107.10	202000.00
52	IBM	Computer services	106.92	433362.00

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

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

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!

Playing w/ the Occupational employment Statistics Data-Set: (parasdoshi.com)

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

Posted on January 28, 2013 by Paras Doshi • Posted in Big Data • Tagged Big Data, Business, Data Warehousing, Databases, EMC Corporation, IBM, Machine Generated Data • Leave a comment

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.

Thanks:

http://www.dbms2.com/2010/04/08/machine-generated-data-example/

http://en.wikipedia.org/wiki/Machine-generated_data

http://tdwi.org/articles/2012/10/23/machine-generated-big-data.aspx

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

Posted on December 21, 2012 by Paras Doshi • Posted in All about Data!, Big Data, video • Tagged Artificial intelligence, Big Data, BigData, Data analysis, Deep Blue (chess computer), IBM, Jeopardy, Machine learning, Natural language processing, Neural network, Watson • 3 Comments

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: