From Networked BI to Collaborative BI

Back in September 2005, I commented on some material by MicroStrategy identifying Five Types of Business Intelligence. I arranged these five types into a 2x2 matrix, and commented on the fact that the top right quadrant was then empty. 

 
The Cloud BI and analytics vendor Birst has now produced a similar matrix to explain what it is calling Networked BI, placing it in the top right quadrant. Gartner has been talking about Mode 1 (conventional) and Mode 2 (self-service) approaches to BI, so Birst is calling this Mode 3.

While there are some important technological advances and enablers in the Mode 3 quadrant, I also see it as a move towards Collaborative BI, which is about the collective ability of the organization to design experiments, to generate analytical insight, to interpret results, and to mobilize action and improvement. This means not only sharing the data, but also sharing the insight and the actioning of the insight. Thus we are not only driving data and analytics to the edge of the organization, but also developing the collective intelligence of the organization to use data and analytics in an agile yet joined-up way.

I first mentioned Collaborative BI on my blog during 2005, and discussed it further in my article for the CBDI Journal in October 2005. The concept started to gather momentum a few years later, thanks to Gartner, which predicted the development of collaborative decision-making in 2009, as well as some interesting work by Wayne Eckerson. Also around this time, there were some promising developments by a few BI vendors, including arcplan and TIBCO. But internet searches for the concept are dominated by material between 2009 and 2012, and things seem to have gone quiet recently.

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Previous posts in this series

Service-Oriented Business Intelligence (September 2005)
From Business Intelligence to Organizational Intelligence (May 2009)
TIBCO Platform for Organizational Intelligence (March 2011)


Other sources

Gartner Reveals Five Business Intelligence Predictions for 2009 and Beyond (Gartner, January 2009). Dave Linthicum, Let's See How Gartner is Doing (ebizQ, May 2009)

Chris Middleton, Business Intelligence: Collaborative Decision-Making (Computer Weekly, July 2009)

Ian Bertram, Collaborative Decision-Making Platforms (Gartner 2011)

Wayne Eckerson, Collaborative Business Intelligence: Optimizing the Process of Making Decisions (April 2012)

Monique Morgan, Collaborative BI: Today and Tomorrow (arcplan, April 2012)
Tiemo Winterkamp, Top 5 Collaborative BI Solution Criteria (arcplan, April 2012)

Cliff Saran, Prepare for two modes of business intelligence, says Gartner (Computer Weekly, March 2015)

The Future of BI is Networked (Birst, March 2016)


Updated 21 April 2016 (image corrected)

Does Big Data Release Information Energy?

@michael_saylor of #MicroStrategy says that the Information Revolution is about harnessing "information energy" (The Mobile Wave, p 221). He describes information as a kind of fuel that generates "decision motion", driving people - and machines - to make a decision and take a course of action.

We already know that putting twice as much fuel into a vehicle doesn't make it twice as fast or twice as reliable. (Indeed, aeroplanes sometimes dump fuel to enable a safer landing.) But Saylor explains that information energy is not the same as physical energy.

1. Information energy doesn't follow conservation laws. Information can be created, consumed repeatedly, but never depleted or destroyed. (Unless it is lost or forgotten.)

2. Whereas physical energy is additive, the energy content of information is exponential.

3. The value of information depends on its use, and who is using it.


Let's look at his example.

"Total wheat production for a single year is valuable information; but total wheat production for ten years, combined ten years of rainfall data and ten years of fertilizer represents thirty times more data droplets, but probably contains one hundred times more information energy, because it shows trends and correlations that will drive a greater number of decisions." (pp 221-2).

In other words, thirty times as much data produces a hundred times more information. He doesn't say this extra information MAY drive more decisions, he says it WILL drive more decisions. In other words, the Information Revolution (and our increasing reliance on tools such as MicroStrategy's products) is a historical inevitability.

But is it really true that more data produces more information in this exponential way? In practice, there is a depreciation effect for historical or remote data, because an accumulation of small changes in working practices and technologies can make direct comparison misleading or impossible. So even if the farmer had twenty years' worth of data, or shared data from thousands of other farmers, it would not necessarily help her to make better decisions. Five years' data might be almost as good as ten years'.

Data is moving faster than ever before; we're also storing and processing more and more of it. But that doesn't mean we're just hoarding data, says Duncan Ross, director of data sciences at Teradata, "The pace of change of markets generally is so rapid that it doesn't make sense to retain information for more than a few years." (Charles Arthur, Tech giants may be huge, but nothing matches big data, Guardian 23 August 2013)

According to Saylor, the key to releasing information energy is mobile technology.

"The shocking thing about information is not how much there is, but how inaccessible it is despite the immense value it represents. ... Mobile computing puts information energy in hands of individuals during all waking hours and everywhere they are." (p 224)

What kind of decisions does Saylor imagine the farmer needs to make while sitting on a tractor or milking the cows? Obvious it would be useful to get an early warning of some emerging problem - for example an outbreak of disease further down the valley, or possible contamination of a batch of feed or fertilizer at the factory. But complex information needs interpretation, and most decisions require serious reflection, not instant reaction.

So it is not clear that providing instant access to large quantities of information is going to improve the quality of decision-making. And giving people twice as much information often leads to further procrastination. Surely the challenge for MicroStrategy is to help people deal with information overload, not just add to it?

Furthermore, as I said in my post Tablets and Hyperactivity (Feb 2013), being "always on" means that you never have long enough to think through something difficult before you are interrupted by another event. There is always another email to attend to, there is always something happening on Twitter or Facebook, and mobile devices encourage and reinforce this kind of hyperactivity.

Saylor concludes that "the acid of technology etches away the unnecessary" (p 237). If only this were true.

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Related posts

Service-Oriented Business Intelligence (September 2005)
On The True Nature of Knowledge (April 2014)


Updated 19 June 2014