Intelligent Enterprise

I'm having some problems with a March 20, 2008 article titled "Gartner: Emerging Technologies Will Help Drive Mainstream BI Adoption." This has been the Holy Grail of BI vendors for over a decade — to increase the number of "seats" using their products, widely reported to be about 20 percent of an organization but clearly much less than that. What troubles me the most about this article, or rather, about Gartner's analysis, is the supposition that new technology is going to crack this old chestnut. It won't. There are only two pieces of enterprise analytical software (broadly speaking) that ever gained currency in organizations in the past two decades — Excel and Google. Wouldn't it be a good idea to understand why?

BI software was invented for marketing and finance people to gather and report information outside the boundaries of the installed operational systems. In essence, it is designed to inform people and, to a lesser extent, allow certain numerate people to investigate the information a little further. But it was never designed to fit seamlessly into the computing environment. Insights gleaned from its use had to find their way back into systems manually. What people want, and the reason they have rejected BI, is a seamless, relevant, understandable process to be informed and to be able to act on it within the same metaphor. That isn't BI. Can you spell mash-up?

Ultimately, the other 80 percent or more of an organization not using BI today may not need it at all. The path to ROI with BI is better decisions, not better dashboards, and better decisions have to be fast, consistent and agile. Data warehouses with data marts, ODS's, cubes and obscure metadata layers in BI tools are not agile. They can't close the loop. BI capabilities need to be embedded in applications.

So let's break these five emerging technologies down and see if they drive adoption (I've left out the word "mainstream" because that's a topic my partner James Taylor covered in the blog, "What Does Mainstream BI Adoption Mean?"). They are:

Interactive visualization
In-memory analytics
Search integrated with BI
SaaS
SOA

Interactive visualization, In-memory analytics, SaaS and SOA are hardly "emerging" technologies. They're already here. Interactive visualization will NOT be the most common front-end for BI. The most common front-end will be no front end at all. BI will be embedded in decision services, operational/process intelligence and operational systems. I can go along with search integrated with BI because, as I said, Excel and Google are the tools people use. Interesting mash-ups of these would be intriguing, but I'm still not convinced that anyone other than the current users/numerates would use them directly.

Two comments by Kurt Schlegel really jumped off the page at me. First, "As a result of this innovation, individuals and workgroups will be less dependent on central IT departments to meet their BI requirements." I don't see a basis for this statement. You can't do BI without data and data is still the most miserable part of BI. Data Quality. Data Governance, Data Modeling, Data Integration. All of these things make BI about as agile as poured concrete. The second statement really floored me. "BI teams need to understand how to leverage these emerging technologies to drive BI adoption, but do it in a way that doesn't undermine the organization's existing BI architecture and standards." Ouch. First of all, if there are BI "teams" there isn't going to be less dependence in the user communities. And one thing BI "teams" have not learned how to do is "leverage... to drive BI adoption." It never happened and there is no connection between these five "emerging" technologies and a new understanding of what people need. Finally, the reference to not disturbing the BI legacy? That's a big problem because BI is notoriously expensive to maintain. If new technologies really change the face of BI, the old systems can't remain.

In-memory analytics will power the kind of embedded analytics needed to support real-time decision services but it will not reduce dependence on IT or lower IT costs. If there is a small savings in time from building aggregate tables and indexes, it will be consumed by the delicacy of stuffing terabytes into memory and keeping things in sync. Besides, it isn't speed that de-motivates people over BI, it's the lack of relevance to the work they actually do.

Search is incredibly useful. And the people who use BI will be delighted with the ways it can help, but it is not going to widen the tent. SaaS is something to watch, but it comes with some problems to overcome, especially flexibility and security. To the extent that BI-type applications emerge using the SaaS model, this could expand the population of users, but is that really BI?

I was especially stunned to see that Gartner was predicting that SOA would foster more departmental application development. Don't we call that Shadow IT (though they did add a caveat about rogue development)? Now I'm not opposed to individual (or departmental) ambition, but creating services and doing model-driven development seems like a reach to me. It's pretty much agreed that the benefits of SOA still remain to be seen and BI is not likely to be one the first places an organization sticks it's toe in the water. I think throwing SOA into the five categories for BI was a reach and I don't see the connection, except in theory.

In a way, this is all nit-picking. The real issue here as I see it is that the dream of expanding the BI base is still alive, but there is no basis for it. All the people who are going to use BI are already using it, at least directly. I believe there is a huge opportunity for the BI vendors to expand the reach and value of their software, but not through visualization interfaces and more powerful analyst tools. That train left the station. It's time for BI to take its place on the porch with the big dogs and tackle the real operational processes of organizations. Classic BI is not going away, it provides a valuable function, but not everyone needs it.

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