Tiny Insights. #bigdata

The carbon footprint of a beef burrito is 5 times that of a chicken burrito. That’s per Eugene Cordero, Professor at San Jose State University, and came my way via my pal, Frank Scavo, a few weeks ago.

You want to take massive causes or opportunities and humanize them down to a single unit of “Human Computational Threshold” (which I think should be a standard hi-tech marketing measure in this current climate of buzzword bingo, BTW), that’s how you do it.

You can keep going on about the impact of climate change and the virtues of sustainability, but nothing’s more effective than winnowing it down to a tiny digestible unit – a burrito in this case, to get you to understand what you can do about it as an individual.

Cloud has it – its called SaaS apps for the enterprise that touches users. Or simple elegant tools such as Dropbox and Expensify and Foodspotting that distill the essence of cloud computing down to 2-3 simple  but ridiculously useful capabilities. These apps humanize the cloud and get us to appreciate the value of this massive opportunity that otherwise would only appeal to CFOs lured by Opex benefit.

Social Business doesn’t. But I wrote a whole post about it.

You know who else needs its burrito? Big Data, that’s who.

The opportunity from Big Data (of which social data is a part) is gigantic. Even that doesn’t do it justice. But Big Data needs its unit of human computational threshold so it appeals to the billions that can benefit from it.

Me? I’m waiting for Big Data to become Tiny Insights. Tangible bites of intelligence that help me make better decisions and improve outcomes. Make no mistake: Tiny Insights doesn’t mean tiny value. Tiny insights inform massive decisions for business or important decisions for individuals.  Alert me when I walk into a restaurant that just got panned consistently across many social networks, or an employee I follow on my enterprise social network who might be able to help with my presentation for next week, or a real time reset of which component supplier is best suited  the minute my production requirements or S&OP assumptions change. There’s very little of this discussion and too much chest thumping.  We need to make billions of consumers, and end users of enterprise wares give a hoot.

Constellation Research Analyst Neil Raden made a similar, hilarious point on Twitter about the careless use of Big Data, saying: “I heard #Bigdata found Jimmy Hoffa”. That sums up the hubris.

Big Data provides the source to be processed. But until we start talking about tiny hidden insights delivered fast (in-memory), in context (apps), where I need it (device agnostic/mobility) with my social/enterprise network to help me parse it , and in a way that shields me from the enormity of the data size and complex behind the scenes computational effort, Big Data as its currently touted may well be one gigantic opportunity that progress left behind.

So to those of you on the Big Data wagon I say, órale vato. Find Big Data’s beef burrito.


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Fred McClimans
Fred McClimans

Excellent post, Sameer. I saw some of this at the recent FOCAS12 conf (Aspen Institute). There is too much "non-relevant" data out there. People are all trying to open the gates, but unless you can transform that data into a meaningful actionable (edible?) nugget, it will likely do more harm than good.

Laurence Lock Lee
Laurence Lock Lee

Hi Sameer ... you make a few useful points here that I would like to amplify. One is the difference between what we call 'Big Data' but really mean 'Social Data' and traditional university style research you quote from. Academic research works on 'Big Data' as well but prides itself on the rigour of its analysis, validity, verification etc... Even then the results will be challenged by fellow academics who may have come to different conclusions from a complementary study. In essence the academics are 'socialising' insights and arguing their relative worth based on the respective 'research evidence' obtained.  Now I think that the current 'Big Data' focus takes the socialisation of these insights out of the hands of academics and into the hands of the every day worker. Traditional research rigour, validation and verification will undoubtedly be minimal for current 'Social Analytics' initiatives compared with academic approaches. On the other hand the community socialising the proposed insights will be far greater and varied. What may be lost in terms of analytical rigour will be more than made up for in the community socialising these potential insights. Of course the meaning of an 'insight' is contextual to an individual and their own personal experience, meaning that even a weak signal in the right hands can be a powerful advantage.  In my own work with Social Network Analysis we have a standard practice of running  'sense-making' sessions using the initial results. We regularly see in these 'socialising insights' sessions outcomes that we could not have predicted from applying a purely academic perspective, as the audience brings their own rich context to each session. So my response to your beef burrito challenge? Don't try and boil the ocean looking for a 'defensible truth'. The menu is much larger than burritos alone. Weak signals can be turned into strong signals through effectively socialising your insights. 


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