Big Data and Music Industry

The economist recently reported that the music industry has started using some concepts of data analysis to better understand their customers. A couple of my favorite lines include:

Every time a track is uploaded to or played on YouTube, every time it is sold by iTunes, streamed on Spotify, shared on a pirate network, liked on Facebook or tweeted about, it gives off a digital signal. A cottage industry has sprung up to process these signals and feed the results to the record companies.

“We’re no longer just a wholesaler of music,” says Paul Smernicki of Universal. As their traditional business declines, the music companies are moving into live music and merchandise. To succeed in those markets, they will need to become experts in fan behaviour, understanding not just how and why people buy music but how they weave it into their lives. In that, data will be crucial.

Descriptive, Predictive, & Prescriptive analytics

The Institute for Operations Research and Management Science (INFORMS) has recently gone through a process of rethinking what "Business Intelligence" or "Business Analytics" means.  They have settled on what seems to be a couple of frame works (here is the first, here is the second).  But, first their definition of analytics:

Analytics facilitates realization of business objectives through reporting of data to analyze trends, creating predictive models for forecasting and optimizing business processes for enhanced performance.

Next, they identified three main categories of analytics:

1. Descriptive - the use of data to find out what happened in the past (I would add:  what is happening now)
    - data modeling, trend reporting, regression analysis
2. Predictive - the use of date to find out what could happen in the future
     - data mining, predictive modeling
3. Prescriptive - the use of data to prescribe the best course of action for the future
     - optimization, simulation
Here is a short clip from IBM (hat tip The Vantage Point) discussing the three categories:



The second framework similarly identifies three different uses or users of business intelligence as described in their venn diagram:

I think neither framework is bad, but I worry that there seems to be some level of hierarchy among the three elements, that one is vastly superior to the others.  

Here is another interesting post on the subject.

Vegas provides more than just bad odds....

Economic Downturn hits wedding business in Vegas?

 Fewer than 92,000 couples married in or around Sin City in 2010. The last time the city married fewer people, it was 1993.

 ...in 2004, when 128,250 couples tied the knot. Fewer people said "I do" in each subsequent year.

 Is it due to poor economy or decline in marriages?  How would you determine a sense of causality or at least determine the effect of poor economy vs shift in social norms?