Brad Pitt has taken business intelligence mainstream with his starring role in a movie where analytics save the day. Pitt’s movie, Moneyball, released by Sony Pictures in 2011, is helping to yank BI out of the tech-centric corners of an enterprise into a far broader, more glamorous role. In the film, analytics are the key to helping an underfunded, small-market group of castoffs could go toe-to-toe with the richest, most powerful competitors imaginable.

Moneyball is not another silver screen fable. It’s a real-life business parable. Pitt plays Billy Beane, the Oakland A’s general manager who upended a century-long tradition of judging player potential by shifting away from the informal “gut” wisdom of baseball scouts and field managers. Instead, Beane pushed for data-driven, analytical methods to evaluate and choose good ballplayers, helping take an otherwise uncompetitive team into the playoffs. The success of analytics in Oakland has radically altered baseball. Now every team applies BI principles to drafting and trading players.

Today, given the big data era we live in, using analytics as a virtual cast member who saves the day is an easy sell to risk-averse Hollywood. That’s because almost everyone suddenly has become awash in structured data and is wondering how best to use it. As a result, well beyond the geeky confines of IT departments, analytics is emerging as a chic solution to data deluge.

Show Me the Data

Intuitively, most people understand that since before they were given a Social Security number, massive amounts of data have been and are being collected about their lives. And given these huge volumes, the outcries about privacy and data security breaches, and the rise of identity theft, people are reconsidering the increasing role of data in their lives. They are even signing up for software and services to help manage and protect their information online.

The ubiquity of data even prompted the United Nations to launch World Statistics Day in 2010. The UN’s director of its Statistics Division, Paul Cheung said, “[T]his unprecedented event has achieved fully its objective of building support and better understanding for official statistics among the general public and the policy-makers worldwide.” More than 100 nations participated.

While it’s safe to say that modern individuals are becoming hyperaware of data in their lives and that almost everyone is hypersensitive about their private data and wants to protect it, there is also a growing subset of individuals who actually want to interrogate the information around us. National Security Agency analysts and brand marketers, come to mind. But that subset is growing fast and its typical user profile is changing from a pure technical professional to a broader business professional.

This change is not surprising in light of the things many of us do. More and more of us are comfortable working with structured data as it has become increasingly pertinent in our lives. These days we’re less indifferent to data than we once were. In fact, we are enamored by it.

Take the Data and Run

On Facebook they count your friends. On Twitter it’s your followers or your tweets. Reddit tallies your karma points. Numbers are everywhere in our digital lives, and they’re beginning to intrigue us at a personal level.

Consider Twitter. Numerous services, such as Twitterholic and TwitterGrader, offer free analytics so “tweeps” can assess how they compare to other users of the microblogging service. With nearly 170 million blogs worldwide (according to BlogPulse in late 2011), every blogger can get loads of data on page views, referring websites, search terms and numerous other metrics. You can even find out how effective a Google AdSense campaign is right from your smartphone or iPhone. The appetite for BI among our growing class of digital mavens is endless.

Even people who eschew social media can benefit from analytics. Anyone who uses GIS apps for, say, navigation on their mobile devices encounter a variety of analytics with quantitative information about qualitative decisions such as choosing a “best” driving route based on fewest miles driven, fastest time between destinations, or the most scenic drive. Online services like BillShrink give consumers free analytical tools to assess their buying options for everything from wireless carriers to which credit card is right for them. Analytics are everywhere.

Small businesses have flocked to free services, such as URLtrends.com and Google Analytics, to get BI insight into their online operations. And many upgrade later to paid services or subscribe to the countless other cloud-based BI tools with their credit card. It’s as easy as pie.

It’s becoming commonplace for people today to use analytics in their daily lives. They understand its value. They want more. And they seem to be able to get BI anywhere they want it.

Almost.

The Color of Data

Inside the enterprise is the one place many analytics-hungry and ready users are struggling to get the BI tools they need to get their jobs done better. To some, these millions of workers eager to apply analytics to their jobs are part of the looming Big Data problem for IT. According to a McKinsey Global Institute study, by 2018 in the United States there will be jobs for more than 4 million “data savvy managers,” which is to say, business analysts without much SQL or DBA experience. At the same time, the consultancy predicts there will be around 400,000 technical positions of “more deep analytical talent” in the U.S. to support the millions of managers who crave analytical answers to their business problems. However, the report goes on to say there will be a shortfall of 150,000 of these experts, the geeks.

But even if there were no projected staffing shortfall, the vast chasm between the number of people inside business who would be more productive with BI versus the available technical talent to support their aspirations is daunting.

There are, of course, three distinct approaches to the problem:

  • Invest in the training of a new army of SQL query writers and DBAs. 
  • Spend on educating motivated end users to acquire the technical skills to build efficient and worthwhile queries while minimizing the hogging of shared BI resources. 
  • Use technology to alleviate the problem.

As with virtually every other dilemma in technology, the most likely long-term answer will come from technology itself. In this case, we need software investments that bridge the knowledge gaps between users and the information they want to investigate. While hiring more BI workers and adding broader training programs for users is worthwhile, even noble, it’s in the industry’s lap now to deliver more intuitive tools that can handle the BI needs of these millions of data-savvy managers.
Based on the evolution of technology in other areas, it’s a virtual certainty that better, faster, easier analytical tools are the way forward. Look at presentation software. You used to have to go to the graphics department to get presentation slides built.  It was the same with desktop publishing. Only graphic artists could create a designed document suitable for printing. Now, anyone can create virtually any type of document. And look at the spreadsheet itself. It started life as a specialty tool for the finance department. Now it is ubiquitous and some say the most widely used business intelligence tool.

So why, in our data-driven world, would it be any other way than the emergence and domination of faster, easier analytical tools? Anyone without specialized training will be able to use software to help them create meaningful business analytics. The good news is it’s already happening.

BI may play the starring role Hollywood again, but its true stardom will happen when it reaches people of all levels and types at virtually every enterprise. There is still a wider, eager audience hungry for sophisticated, yet easy-to-use tools. It will be exciting to watch. I’ll bring the popcorn.

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