In the six or so years I’ve been working with enterprise information technologies, I’ve seen three technology sets take off: social-and-mobile (2006-’07), cloud computing (2008-’09), and big data (2011-’12).

Thinking about all three together recently, and pondering Drew Conway’s excellent Venn diagram defining “data science,” I had an epiphany. It took the form of a Venn diagram representing a sort of Grand Unified Equation for Enterprise 2.0. Here it is:

trifecta diagram

The conceptual equation is roughly Enterprise 2.0 = (Social + Mobile) AND (Cloud) AND (Big Data). The fun part is in the labels for the parts of the diagram where you don’t have all three. I’ll leave it to you to figure out why I chose the labels I did for the partials.

If you drop any one of the three elements, the value doesn’t drop to 66%. It drops to 5%. If you drop two of the three, you drop down to close to 0%. Or if you’re the glass-half-full type, you could say that when we just had social + mobile, the potential value added to Enterprise 1.0 was x. This jumped to 5x with the addition of cloud computing and to 100x with the addition of big data technology.

The cost-benefit argument with any one or two elements is hard to justify. But argue for all three intelligently and suddenly the equation starts to look really good and truly radical.

And what’s more, the implied organizational model no longer looks like a coat of paint. It starts to look radical enough to actually merit the 2.0 designation, rather than a 1.1. A CEO who understands the Venn diagram above will realize that it represents a significant business model shift. He or she is no longer being presented with an expanded IT budget request. It’s a business model decision now.

Now that the trifecta is complete, the potential value has skyrocketed. Half measures will no longer do.

Data ubiquity changes everything
The reason you need all three is simple: Enterprise 2.0 business models aren’t actually very much better than regular ones when you’re dealing with conditions of data scarcity. But under conditions of data ubiquity, they’re radically better. The catch is that they’re radically better only if you deploy all three technologies astutely.

If you’re talking about a single Excel spreadsheet or warehouse-generated business intelligence report, it’s hard to argue that you need a wiki or a blog to make use of it, or a cloud vendor, or aMapReduce ninja. Even with a terabyte of data, nothing much has changed–that data will fit on a $150 hard drive these days.

Move up to a petabyte of fast-changing, poorly structured data, and suddenly Excel and your old BI toolkit start to look like a joke. Your normal reporting and communication structures start to look silly.

Data ubiquity changes everything. Nice-to-have technology toys turn into must-have survival equipment.

All three technology sets really only come into their own when you’re dealing with huge fire hoses of data that would overwhelm conventional command-and-control management models.

Enterprises have had access to the data fire hose for a while now, but lacking the ability to do anything meaningful with it, they’ve mostly thrown it away–what’s starting to be known as data exhaust. What happens if you decide not to throw it away?

You get data flows voluminous enough to burst reporting pipelines. Flows so dynamic that data warehousing- and reporting-based IT falls apart. Flows so demanding that you need more infrastructure expertise than most IT departments can build in-house.

You get flows that are unstructured enough so that SQL queries can’t probe them. Flows torrential enough that even after you apply all available process automation, analysis, dashboarding, and visualization technologies, the flows are still too big to use summarize-and-funnel-up models converging at the CEO.

To illustrate this “data ubiquity” regime of business operations, it’s useful to speculate on the perfect storm business problem that E 2.0 doesn’t just solve 10% better than E 1.0, but provides a solution whereas E 1.0 fails completely.

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