Half measures won't work. Enterprises must adopt social/mobile, cloud, and big data technologies—all of them, not just one or two.
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.
Imagine a grocery store chain with 1,000 employees getting opt-in permission from a customer base of 50,000 people to track location data, at city and in-store levels as well as on Facebook, Twitter, and Foursquare, in return for deals. Imagine that you also have point-of-sale data from a loyalty program that has high penetration (i.e., you can match the behavior data to purchasing history). And throw in video feeds from all store cameras for good measure.
Yeah, this is Big Brother territory, but we'll glibly assume that you obtained opt-in permission in not-evil ways and that your customers know what you're doing.
This is a huge fire hose of complicated, loosely structured data. Clearly, there will be immense value hidden in the fire hose: sentiment data, competitive data, fine-grained lifestyle psychographic data, shopping patterns, price-sensitivity data ... the list goes on.
It will not fit pre-defined data models very well. So it can't be sucked into a conventional BI/warehousing infrastructure and turned into push-button reports. You will need big data technologies.
It will have high volatility and unpredictable peaks, so fixed hardware infrastructure with worst-case sizing won't do. You will need on-demand infrastructure. You will need to use the cloud or face ruinous costs.
And finally, when you've Hadooped and Cassandra-ed it for all it's worth, and funneled the digested stuff into the best analytics and visualization dashboards you can build, it's still too much for one mind to process. So piping it all into a huge wall-sized screen in the CEO's office (which Procter & Gamble does) won't be enough. You need social business models to throw collective intelligence into the mix.
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