7 requirements for the Enterprise of Things

7 requirements for the Enterprise of Things

7 requirements for the Enterprise of Things
Few companies have yet to formulate a strategy for dealing with the emerging use of both company deployed and users acquired “things.”

Indeed, I expect most organizations to deal with the Enterprise of Things (EoT) the same way they did with mobile; wait and react to end user demands rather than proactively manage the resources. It didn’t work well then and it won’t work well now!

I recommend (rather strongly) that enterprises get ahead of the curve. It’s the only way to maximize benefits and create a compelling business advantage. It’s also the only way IT can cope and not have to react in a panic as it did when mobile devices achieved critical mass deployments.

To this end, I suggest the most advantageous thing an enterprise can do in the near term is focus on the ability of any EoT deployments to provide actionable intelligence of monitored process, workflows and work spaces. This can quickly improve overall operations of the organization. Acquiring data and effectively analyzing the intelligence provided can lead to many important insights the organization can leverage.

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There are many ways to achieve this goal, but here I offer my seven requirements for EoT (although you will likely be adding more over time).

1.     EoT creates lots of data that must be analyzed. Don’t capture and store it if you can’t properly analyze it. Indeed, I see this issue as one of the key impediments to enterprises gaining real benefits from EoT. This is not a big data problem. Its a big insights problem.

2.     EoT without actionable intelligence is a wasted opportunity. But many companies simply don’t know what data is most important to capture. The lack of data modeling expertise in most organizations is causing wasted insights. Start cultivating your expertise in this area now.

3.     I estimate less than 10% of EoT data is currently used effectively. The trick is how to get value from the other 90%. In fact, it’s often the case that organizations store data they ultimately never analyze due to lack of tools, capability to know what to look for, and capacity constraints. Don’t get caught in that trap.

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