Balanced Scorecard & More

DataWarehouses - What happens in Reality?

Learning Data Warehouses 101 brought to light quite a few concepts that were fuzzy in my brain, but as a good read always does, it raised more questions.

Before I pour my questions and thoughts, leme write about DW as I have understood with the help of an example:

Consider the Airline industry - well I read and heard a lot about this, so that's the example I'm going for. Say, the Senior Management of Southwest wants to know how our business is performing and expects me to present a full picture. Each of these big shots want to know about a particular aspect - obviously, they don't tell me what it is they want to see - they probably don't even know !

I begin digging and try to figure out the entire process.

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Multiple Data sources – As one would expect, there are a lot of sources of data – e.g. the Customer related data, Supply related data from Vendors, data about my own employees which is internal to the Company, Inventory and so on.  These are termed as the Operational Source systems

Anyone who knows DW has made or seen this diagram and with that assumption, I am also not going to delve into why we need a ODS or what is a Data mart.

So coming back to my airline example, I now begin to build my report trying to incorporate what the Senior management would really like to see.

1.       How has the overall revenue increased from previous Quarter to current Quarter and how do the forecasts look like? – This is easy enough to present because I know I need to get this number from my Warehouse section.

Big Data Symposium

You hear and use a multitude of Social Media – ranging from Twitter, Facebook, Pinterest, Vine, LinkedIn, Google+, I could fill more than a couple of lines with these, but you get the picture – obviously, this has led to the generation of massive data. More recently, the term “Big Data” has be thrown around and used as just another term. This comic – I felt was more real than funny!

Last year, one of my Professors raised a simple question- can you quantify and tell me how much data is being generated? Some 50 odd students sitting in the class, murmured numbers, because we all knew that there was a ‘huge’ amount of data – but how much was it? Petabytes? Is that even a correct term?!

The term ‘big data’ is being used in a variety of industries – Health, Government, Social media, Commercial & Retail, Sports, etc. Now is time when people are wondering – okay so I have this huge pile of data – garbage & useful – how do I put to use. This is where investing in Big data comes into play. Millions & Billions of dollars is being invested to dig through data and get some meaning out of it – i.e. Analytics.

Much like everyone else, the University of Arizona is also edging towards educating students about Big Data and as a result – a Big Data Symposium was held on the 10^th October, 2013.

To be honest, I went in thinking lets learn something and not criticize it for not taking my breath away. It was quite exciting to walk into a room full of people who were immersed in the world of data, I even expected a few of the details to be beyond my comprehension. 

We had some great speakers:

1.    Brian Gentile, Chairman & CEO, Jaspersoft

2.    Tim Hood, Global V.P., Strategic Technologies, Chief Solution Architect, Retail Industry,
SAP AG

3.    David Cowart, Director Strategic Solutions, Mandiant

4.    Michele Polz, Head of Patient Insights, Sanofi and Mikki Nasch, Co-Founder, AchieveMint

5.    Zaheer Benjamin, VP of Business and Basketball Analytics, Phoenix Suns

6.    Brenda Dietrich, IBM Fellow and V.P. Strategy & CTO for Business Analytics

7.    Darren Stoll GVP, Interactive Marketing - Operations and Analytics, macys.com

8.    Kerem Tomak Vice President, Marketing Analytics, macys.com

9.    Sudha Ram Professor of MIS, Director, INSITE Center for Business Intelligence and Analytics

The foremost important concept that one should remember when it comes to Big Data is the 4 V’s.

Volume – As expected, this refers to vast amount of data being generated via various platforms

Velocity – The speed at which data is being generated. “98,000 tweets, 695,000 Facebook status updates, and 11 million instant messages are sent through the Internet every 60 seconds”

Variety – Different forms of Data – consider for example the internet consumption, probably one of the most important factors contributing to the variety of data, in close proximity is the size of data being generated in Healthcare.

Veracity – In my opinion, this is the most important factor contributing to the efforts & skills needed in Big Data. Why? With the speed that data is being generated, it could soon become outdated. Take for example a superstore such as Walmart during Christmas time. Say customers are actively posting about Christmas lights being out of stock. The news spreads fast through Social Media. Next thing you know, customers aren’t coming to Walmart instead going to its Competitor. Is it of any use if Walmart stocks up on the lights after Christmas? Walmart just lost a whole lot of $$$$$, not to mention many unsatisfied customers.

That’s just Big Data explained in simple words!