Business Intelligence Is Transforming Into Intelligent Businesses

By Eric Lundquist  |  Posted 2013-05-07

Business Intelligence Is Transforming Into Intelligent Businesses

Business intelligence is passé. Now it's the intelligent business, and this shift is more than a simple name flip.

The shift underscores the altering of business intelligence from making better use of the data inside your company to using those vast pools of data outside your firm (big data if you will) to capitalize on customer sentiment, buying patterns and economic indicators.

This flip from data-driven decisions beginning inside your company and pushed to the outside world to outside data happening in real time being the driver of your company's inside operations means big changes for the traditional business intelligence and business analysis vendors. And, of course, those changes present opportunity for the intelligence upstarts.

First, let's talk about the NFL draft. What does the pro football draft have to do with business intelligence? Recently The New York Times published an interactive guide to the draft that allowed users to explore the relationship between an early pick and star performance and found early picks were not a particularly good indicator.

In an explanation about the guide, the developers explained, "Our goal was to show an odd reality: Even though N.F.L. teams do tend to pick the 'best' players early in the draft, there's a tremendous amount of chance involved. The best 10 eventual N.F.L. performers will not be the first 10 players drafted—or even close." Picking the best players is often an internal analysis performed by NFL teams. Too bad that picking doesn't always pay off.

Discovering that "odd reality" is the goal of the intelligent business. The opportunity to use the outside world to find a new market, a new price level or a new product that was simply impossible to achieve via traditional inside-the-company product development or seat-of-the-pants wild guesses by company founders.

While Steve Jobs was not a fan of traditional market research to discern new products, he had developed that capability to discern the "odd reality" before big data discovery made the process available to us mere mortals.

This shift toward the intelligent business was much in evidence recently on a visit to San Francisco, where I attended both the Amazon Web Services Summit and the SAS Global Forum Executive Conference.

SAS Institute and its co-founder, James Goodnight, largely invented software-based business analytics starting in 1976. While the years of rapid growth seem to be behind it, the company grew from $2.75 billion to $2.87 billion in 2012.

The company poured 25 percent of its revenue back into research and development. The company is privately held, which mutes possible concerns regarding so much revenue going into R&D for relatively minor growth.  The company has a commanding share in business analytics, a loyal customer base and a good track record of customer success stories.

Business Intelligence Is Transforming Into Intelligent Businesses

You would be hard-pressed to find a Fortune 500 company that does not have a SAS operation. You are best off not thinking of SAS as a product but more of a universe of services based around business intelligence and statistical analysis.

The company has been working diligently to embrace the shift to faster analysis using solid state data storage drives and moving the analytics processing closer to where the data is stored. At the recent SAS conference the company laid out some new products aimed at bringing SAS users the benefits of outside, big data analysis without having to leave the SAS environment. The best case for SAS is to make big data from outside sources simply one more feed into the SAS infrastructure.

As eWEEK Editor of Features and Analysis Chris Preimesberger highlighted in an article on the new SAS offerings, "SAS, the world's largest independent business intelligence provider, updated its frontline analytics engine April 29 with a new version that can be deployed in several ways: in public or private clouds, hosted by SAS or via on-premise software.”

Those new SAS offerings hold appeal to current SAS users, but it is less clear that the company can pull in new users from companies interested in pursuing the big data analytical promise. SAS faces the same issues as other long-established technology vendors such as Dell and Oracle, which are striving to realign their products and services in a technology economy. This requires open standards, nonproprietary software, transparent pricing and customers that want to pay for the service on-demand rather than the product.

In big data analysis the objective is often to ask a simple query over a very large, real-time data set. A recent article in Nature investigated if Google Trends could predict financial market activity. The answer was yes. As the article stated, "Google Trends data did not only reflect the current state of the stock markets, but may have also been able to anticipate certain future trends. Our findings are consistent with the intriguing proposal that notable drops in the financial market are preceded by periods of investor concern.

"In such periods, investors may search for more information about the market, before eventually deciding to buy or sell. Our results suggest that following this logic during the period 2004 to 2011 Google Trends search query volumes for certain terms could have been used in the construction of profitable trading strategies." Whether it is financial trends or NFL prospects, the analysis asks simple questions of big data sets. These data sets reside outside the confines of the companies, and their multi-terabyte sizes make them impossible or at least too time-consuming to move.

Business Intelligence Is Transforming Into Intelligent Businesses

I covered this topic of simple queries over large data sets at a recent GigaOm conference, "Creating simple queries over huge data sets can provide insights much more quickly and with more accuracy than trying to create sophisticated algorithms narrowed toward smaller samples. The scene-completion process for Google's Street View (which removes offensive or embarrassing images and "fills in" the scene) went from using a complicated formula over about 150,000 photos to a simple formula, but with more than 1 million photos with vastly superior results, said (Jack Norris, vice president of marketing at MapR Technologies). The same process could apply to financial services, customer sentiment, weather forecasting or anywhere big data sets could be combined with a simple query process." This is a very different approach from traditional business analytics.

If you take big data analysis as a distinct category, SAS is in the middle of the pack. In an analysis of big data revenue for the Wikibon consulting site, analyst Jeff Kelly puts SAS' big data revenue at $187 million, just $1 million ahead of startup Splunk and far behind IBM.

While it is very difficult to break out big data (a somewhat amorphous term), the business intelligence world is changing. For example, the community developed and free statistical language R is the basis for new SAS competitors including Revolution Analytics. SAS has announced support for R, but that is a long way from basing your products on a new model.

The Amazon Web Services conference was being held a short walk from the SAS event. Amazon has its own open source demons to contend with (OpenStack is a clear competitor to AWS), but the transparency of AWS pricing, the continued price cuts paired with new features and the ability to ramp up or down infrastructure services as needed do point to the new way to acquire enterprise computing services.

SAS seems intent on providing its software as a cloud service, but whether it will be willing to adopt the high volume, low price economics of an Amazon is a different case altogether. Amazon's retail operations are powered by some of the most sophisticated business intelligence operations in the world. Those customer-facing business intelligence capabilities brought to the market place at an Amazon-like pricing model may indeed be the future of the intelligent business model for the likes of SAS and its competitors.

Eric Lundquist is a technology analyst at Ziff Brothers Investments, a private investment firm. Lundquist, who was editor-in-chief at eWEEK (previously PC WEEK) from 1996-2008 authored this article for eWEEK to share his thoughts on technology, products and services. No investment advice is offered in this blog. All duties are disclaimed. Lundquist works separately for a private investment firm which may at any time invest in companies whose products are discussed in this article and no disclosure of securities transactions will be made.

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