Now Business Intelligence Is Dead
July 18, 2012
I received a “news item” from Information Enterprise Software, an HTML email distributed by InformationWeek Software. The story was labeled “Commentary.” I did not think that “real” journalists engaged in “commentary.” Isn’t there “real” news out there to “cover” or “make.”
Read the article. Navigate to “If BI Is Dead, What’s Next?” The “commentary” is hooked to an azure chip consultant report called “BI Is Dead! Long Live BI” which costs a modest $250. You can buy this document from Constellation Research here. First, let’s look at the summary of the report and then consider the commentary. I want to wrap up with some blunt talk about analytic baloney which is winging through the air.
Here’s the abstract so get your credit card ready:
We [Constellation Research] suggest a dozen best practices needed to move Business Intelligence (BI) software products into the next decade. While five “elephants” occupy the lion’s share of the market, the real innovation in BI appears to be coming from smaller companies. What is missing from BI today is the ability for business analysts to create their own models in an expressive way. Spreadsheet tools exposed this deficiency in BI a long time ago, but their inherent weakness in data quality, governance and collaboration make them a poor candidate to fill this need. BI is well-positioned to add these features, but must first shed its reliance on fixed-schema data warehouses and read-only reporting modes. Instead, it must provide businesspeople with the tools to quickly and fully develop their models for decision-making.
I like the animal metaphors. I must admit I thought more in terms of baloney, but that’s just an addled goose’s reaction to “real” journalism.
The point is that business intelligence (I really dislike the BI acronym) can do a heck of a lot more. So what’s dead? Excel? Nah. Business intelligence? Nah. A clean break with the past which involved SAS, SPSS, and Cognos type systems? Nah.
Information about point and click business intelligence should be delivered in this type of vehicle. A happy quack to the marketing wizard at Oscar Mayer for the image at http://brentbrown98.hubpages.com/hub/12-of-the-Worst-Sports-Logos-Ever
So what?
Answer: Actually not a darned thing. What this report has going for it is a shocking headline. Sigh.
Now to the “commentary.” Look a pay to play report is okay. The report is a joint work of InformationWeek and the Constellation report. Yep, IDC is one of the outfits involved in the study. The “commentary” is pretty much a commercial. Is this “real” journalism? Nah, it is a reaction to a lousy market for consulting studies and an attempt to breathe controversy into a well known practice area.
Here’s the passage I noted:
We all saw the hand wringing in recent years over BI not living up to its promise, with adoption rates below 20% or even 10% of potential users at many enterprises. But that’s “probably the right level” given the limitations of legacy BI tools, says Raden. I couldn’t agree more, and I’ve previously called for better ease of use, ease of deployment, affordability, and ease of administration. What’s largely missing from the BI landscape, says Raden, is the ability for business users to create their own data models. Modeling is a common practice, used to do what-if simulation and scenario planning. Pricing models, for instance, are used to predict sales and profits if X low-margin product is eliminated in hopes of retaining customers with products A, B, and C.
So what we are learning is that business intelligence systems have to become easier to use. I find this type of dumbing down a little disturbing. Nothing can get a person into more business trouble faster than fiddling around with numbers and not understanding what the implications of a decision are. Whether it is the fancy footwork of a Peregrine or just the crazy US government data about unemployment, a failure to be numerically literature can have big consequences.
The “commentary” adds:
It’s no coincidence that IDC’s latest BI and analytics market share stats show that the three fastest-growing vendors in the industry are Tableau Software, QlikTech, and Tibco Spotfire, with reported growth rates of 94%, 43%, and 23% in 2011, respectively. All three blend data visualization, analytics, and high-scale in-memory analysis capabilities. In my view they’re moving toward the kind of flexible and accessible analysis environments that Raden calls for. Their interfaces and approaches are being imitated by larger vendors, though it’s too soon to say whether those efforts will transform the way people interact with BI. IDC forecasts that advanced analytics (the uber category for predictive modeling and machine learning) will grow 10.1% per year through 2016 and content analytics (the parent of natural language processing) will grow 14.5% per year through 2016. Traditional BI query, reporting, and analysis tools, meanwhile, will see still-impressive 9.5% annual growth, according to IDC.
Okay, “Über.” I have some familiarity with the technologies used by Tableau, QlikTech, and Tibco. Heck, I sort of know the guy who developed a big chunk of the Spotfire plumbing. The predictions, the buzzwords, and the references to predictive modeling add a note of scientific harmony to what is a very clunky sequence of business methods.
We have a hype machine in low gear, folks. Now this is just my opinion, but I think the baloney about business intelligence is heading for the same type of push back that hit enterprise search, Web content management, and SAP style enterprise resource planning systems.
First, systems which disconnect the user from understanding the choices of what data to use and what algorithmic procedures are best suited to a data set can increase risk. Even the folks trained to use an old school system such as SPSS require education, specialized training, and experience to make decisions that minimize the likelihood of a big mistake.
Second, most business professionals with whom I work at this time are not comfortable figuring out what a particular algorithm’s limitations are. There are choices of great import involved when setting up even run of the mill analytic routines. Do you know when to embrace lambda calculus or invoke the math of my dear, departed relative Vladimir Ivanovich Arnold? What this means is that users of today’s “training wheels” systems point and click their way through options until a chart is displayed that “answers the question.” In some US government agencies, the appearance of the chart is more important than the data themselves.
Wow. Effective decision making for sure.
How’s that working for some of the licensees of systems from big names in social media analysis? Not so well I hear. One big telco dumped a big name analytics vendor because the system spewed data which did not match the reality the managers lived in. Replacing one system with another did not solve the problem. The people lack the expertise to make use of even primitive tools. Software is not smart. People are less adept than they think when it comes to number crunching. Result? A worsening problem for the licensee and the vendor. Those revenue shortfalls are caused by slow sales and clients who cancel deals. However, I think operating costs help push some analytics firms into acts of desperation marketing.
Third, the claims made for numerical recipes are amazing. we just completed a report on “predictive analytics.” My conclusion. The systems worked within a narrowly defined problem space, with a restricted domain of content, and with the ministrations of a person trained in statistical methods. Once you apply predictive methods to a big flow like Twitter messages, the systems choke. “Big hat, no cattle” was my conclusion. Some of the predictive vendors are raking in the money. One example is Palantir, which has cash from numerous venture firms and a valuation of about $1 billion or more. Does the system work? Sure, when the caveats are applied. When one embraces a next generation system without attending to the caveats, the results are spotty. The clue is that the companies with predictive systems are NOT making money picking stocks or horses. Google is marginalizing Urchin to embrace an improved system. What’s that tell you about the need to reinvent collections of methods? The reason? The predictions don’t work in real world situations or match the data which must be processed in a useful manner. Does that thought cross the minds of the companies looking for a something-for-nothing solution to their information woes? Nah.
So what did I learn from the “commentary.” It is a marketing piece. The assertions are largely unfounded. Nothing today takes the place of knowing how analytics systems works and the importance of the data set, the method selection, and the options for setting thresholds.
In short, business intelligence is just like enterprise search, cloud computing, or a multi million dollar ERP system. It sure sounds good. Real world use of the systems is just a little bit different from the marketing baloney. Just my opinion, gentle reader. Honk. (For even more blunt opinions, sign up for our opt in newsletter by writing thehonk@yandex.com.)
Stephen E Arnold, July 18, 2012
Sponsored by Polyspot
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2 Responses to “Now Business Intelligence Is Dead”
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Nice piece, Stephen. Thanks for the amusing commentary and even more amusing example of corporate-double-speak marketing gibberish!
There is genius in there, as you hint at–the headline got my attention, for sure, but it’s also true that I’m seeing a lot of “–your phrase here–is dead” headlines and titles. It’s only a matter of time before that phrase too becomes as interesting as “New!” and “Improved!”
-Nicholas
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