Integrating Unified Messaging with the "Internet of Things" allows people, smart systems and smart devices to subscribe to messages from each other.

The last article in this "M2M in the Cloud" series posed a question: Has it arrived? The answer, as evidenced by short takes on three solutions coming from SensorLogic, Viewbiquity and Palantiri, was a resounding Yes. There are others and of course more will come. Enterprise environments are migrating to the cloud, and putting M2M communications there as well gives IT control of the real-time, actionable data coming from the devices.

IT does not like anything coming onto their turf that they don't control. I'm old enough to recall their objection to PCs. More recently we have seen serious concerns being expressed about the unauthorized use of smartphones, which came into offices via jacket pockets and handbags. That development was covered in my series on the Mobile Enterprise. The hyperlink will take you to the first article.

Different Types of Mobile Data
If M2M solutions are located in the cloud then IT can manage the devices and also enable the integration of device data with mainstream business databases like ERP and CRM. But there has to be a business case.

On the other hand, the business case for the Mobile Enterprise (ME) is predicated on manual access to those databases in order to obtain and input data. This allows management decisions to be based on up-to-date information.

Therefore if M2M integration is viable, then one would expect to see synergistic relationships with ME. And the resulting converged solution would leverage both investments. But before considering the business case for convergence of these two elements, we should look at a fundamental technology issue.

Right now M2M and ME inhabit different domains. Regular IT systems are not set up to handle real-time data: ERP, the most obvious candidate for convergence, gets batch updates from factory floors and warehouses. And most M2M solutions were not designed to store a lot of post-processed data in order to enable analysis at later dates. However, it can be provided, with relative ease, if the solution is in the cloud.

Convergence: Does It Make Sense?
The integration of these two types of data is feasible, despite the issues, but to be honest while I wrote about this topic in the past I never stopped to consider how it would work in practice. Moreover, why would an enterprise want to do it? And what benefits would accrue?

The answer was there in Palantiri's solution, but it didn't manifest itself to me until ThingWorx (a somewhat unusual but memorable name) acquired this company. ThingWorx was working along similar lines, so the spin was that a merger made sense.

Today's software platforms focus on execution processes that generate one of three types of data--unstructured, transactional, or time series. People work in unstructured ways; computer databases such as CRM and ERP employ transactional data; and we can classify M2M data as being based on time and events.

For each of these data types, a specific set of intelligence tools have been developed in order to provide "insight", e.g., a basic Google search for unstructured data. However, the rigid structure of data sets limits the questions that can be answered to those known in advance.

The platform treats all "Things"--people, systems and the physical world--the same way. It enables the creation of a unified environment in which smart things talk to people and each other and where every person or thing can listen and react to every other person or thing.