Friends Don’t Let Friends Overpay for BI

Business Intelligence projects are famous for low success rates, high costs and time overruns. The economics of BI are visibly broken, and have been for years. Yet BI remains the #1 technology priority according to Gartner. We could paraphrase Lee Iacocca and say: People want economical Business Intelligence solutions and they will pay ANY price to get it.

Nobody argues with the need for more Business Intelligence; BI is one of the few remaining IT initiatives that can make companies more competitive. But only the largest companies can live with the costs or the high failure rates. BI is a luxury.

I believe that the bad economics of BI are rooted in the IT department/BI vendor duopoly on BI infrastructure. This post focuses on IT’s inability to deliver efficient BI projects; I will write about the BI industry in my next blog:

There are three fundamental reasons why IT departments in their current form fail to deliver economical BI solutions:

1) They don’t understand elastic scale

IT departments are good at scaling: adding more and more hardware and software but scaling makes sense for tasks that are highly predictable. Given the ad hoc nature of BI we not only need to increase the compute power when we need it for a complex queries but we also need to be able to decrease the compute power when it’s not needed to keep the costs down. Elastic is more important than scalable. And this precisely why internal BI solutions will always be either too expensive or too slow for complex queries…

2) They try to control BI with a single version of the truth

While the volatility of business environment is increasing the IT departments are trying to button up the business knowledge (data, metadata, processes) into a top-down, inflexible and lengthy process that should produce a single version of truth. The problem is that the underlying business is changing so rapidly that by the time this is done the resulting analysis and reports are not correct anymore and the BI project becomes shelfware.

3) They cannot measure success of BI

“If you can’t measure it, it’s not worth doing!” is one of the selling point of BI but it is difficult to measure the success of BI projects. IT delivers on initiatives that are quantifiable (throughput, response time, performance, data sizes) and since the data size is one of the few easily measured aspects of BI it is the only metric where IT can claim success. This is why we often read about terabyte and petabyte datawarehouses. But it is a small portion of the BI market (2%) and they happen to be places where data goes to die.

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