How to Misuse Analytics

Catherine Tucker at MIT Sloan and Anja Lambrecht of London Business School have a great HBR piece on how to make big mistakes with analytics.  Here are what they see as the biggest challenges:

1. Size isn't everything.  Usually managers have to merge data sets from different sources that were designed for different purposes.  If the data sets cannot be cross-referenced in a meaningful way, their usefulness can be limited.  
2. Our ability to analyze structured data is well ahead of our ability to analyze unstructured data.  The authors observe that firms have had more success with unstructured data when analyzed in conjunction with structured data.  
3. Data processing skills are more critical to reaching meaningful conclusions than bigger data sets.  Companies need to invest in both.
4. Correlation is not causation.  Any well-trained PhD in economics has had this drummed into their head all the way through graduate school.  But now lots of employees are looking at computer-generated results, see a correlation and think they have something.  Field experiments on relatively small amounts of data are likely to lead to greater insight.  

Big data's biggest user -- finance

At least that's what WP says today.  Traders track tweets for signs of optimism or lack thereof to anticipate where the stock market is moving.  Analysts now gauge sales performance from Amazon comments; why wait for the monthly or quarterly sales figures from the government or corporate reports?

The article makes another important point that anyone who has tried to do economic or financial forecasting has known for some time: your analysis is only as good as the data and big data does not necessarily mean better data.  The stock market always has been subject to herd behavior, but at least the herd had to buy or sell to move the market before.  Now hedge funds are making the same bets in an attempt to guess which way the herd might move.  Investor beware. 

NC State MBA and Master of Accounting students are starting to get training in how to harness big data, but we are focusing on marketing, technology commercialization and risk management.  These are areas where data-driven decision making has a history of allowing managers to make more informed judgments about how to create value. 

Big data, little talent

That's the abridged headline this online WSJ piece.  Big data seems to be on its way to becoming the next big thing in management.  Companies are swimming in data and do not have the expertise to either manage the data sets themselves or to draw logical inferences from it.

What the industry needs is a new type of person: the data scientist.  According to Pat Gelsinger, president and chief operating officer of EMC Corp., the giant U.S. data company, this isn't an unprecedented problem. "IBM started a generation of Cobol programmers," he said, referring to one of the first dominant programming languages. "Thirty years ago we didn't have computer-science departments; now every quality school on the planet has a CS department. Now nobody has a data-science department; in 30 years every school on the planet will have one."

Hilary Mason, chief scientist for the URL shortening service bit.ly, says a data scientist must have three key skills. "They can take a data set and model it mathematically and understand the math required to build those models; they can actually do that, which means they have the engineering skills…and finally they are someone who can find insights and tell stories from their data. That means asking the right questions, and that is usually the hardest piece."

NC State is already staking out a leadership position in big data.  The MS in Analytics has been a huge success.  The Center for Innovation Management Studies in the Poole College of Management is doing big data research in conjunction with NC State's computer science department.  Stay tuned.

There's a great future in analytics

McKinsey Global Institute has released a new study "Big Data: The Next Frontier for Innovation, Competition, and Productivity" that claims the surge of data we are generating from the internet and electronic forms of commerce will open a huge wave of innovation.  (For a good summary, see this NYT account; a link to the study itself can be found here.)  McKinsey defines "big data" as "data sets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze."  The study argues that increased transparency, opportunities for experimentation, enhanced ability to optimize products and services for sub-populations, development of artificial intelligence, and new business models will lead to enhanced productivity and increased consumer surplus. 

The only hitch, the study argues, is a potential shortage of talent.  By 2018 McKinsey anticipates a shortage of 140k to 190k workers in mathematically intensive occupations on top of a shortage of 1.5 million managers and analysts "who can ask the right questions and consume the results of the analysis of big data effectively."  My advice to Jenkins MBAs: give the stat course MBA 504 your best shot. 

Aside: The title of today's post is adapted from the famous "plastics" scene in "The Graduate."