Verging on Converging

  • The integration of new technology with traditional applications is a point of convergence.
  • Harnessing convergence technologies like cloud, big data, and the IoT requires flexible business models.

To converge means to come together.  While this could apply to anything from the technorati converging at SXSW in Austin to a meeting of the minds at the United Nations, in this case we are talking about convergence of different software and hardware components that make up the classical architecture in a typical Enterprise.

In a recent Profit Oracle article, “Trends in Enterprise IT: Convergence,” I discussed ways how convergence poses challenges accompanied inevitably by big opportunities.

Business processes and applications

Traditional applications—packaged as well as custom—will need to work with new technology applications, such as big data and the Internet of Things (IoT). This may converge by extending core functionality or by integration and interoperability. Due to the complex nature of the integration, smaller and more agile businesses will likely go the custom route and use integration as a convergence vector. Those businesses that can wait, on the other hand, will see packaged applications adding functionality. One key driver behind this development is the expanding scope of business processes that take advantage of new technologies. For example, a consumer products business leader would love to see aggregated feedback from social media right next to revenue numbers for product lines—all on a single pane of glass, all integrated and tightly woven together, tracked over time.

Platform architecture

Adding big data and IoT, the platform footprint of a typical business will see the addition of NoSQL databases and Hadoop architectures, as well as analytics, to accompany new data sources and unearth new insights. Some executives will need to incorporate sensors and data collection into the computing fabric and decide if they can apply traditional data processing rules. They may have to treat the additions with big data methods — or something even more unique or hybrid solutions. For example, a data warehouse with all the data collection, aggregation, transformation, tooling and analytics built in may become a source for big data analysis. Or it may simply need to co-exist with a data store coming in from a sensor-based network. The key point to note is that silos are expensive; therefore, we need to avoid creating separate pillars of computing platforms whenever possible.


With cloud computing making steady advances, hybrid clouds and cloud federation will see a steady uptick with such classic considerations as latency, network bandwidth, and site scalability—private, public, or hybrid—still remaining. Some interesting wrinkles could come from the introduction of high volume, velocity, and variety (3Vs) data into the mix and the resulting analytics applied to that data. For example, does one have a big data warehouse in a private cloud and an M2M repository in a public cloud? How would cloud services be orchestrated and managed? How would an analytics layer span the two ?  Does one really need to analyze every bit of data, or use aggregated data ?

Fluid and flexible business models and technology architectures with a high degree of interoperability will differentiate businesses moving forward. Nimble businesses that realize the full potential of new technologies, such as big data, cloud computing and IoT, will surpass their competitors in 2014. Without proper planning, some organizations will create computing silos and create barriers to integration and convergence—a move that could prove very expensive.

Are you employing convergence technologies in your business? If so, which ones have you found most applicable or beneficial to your enterprise? What challenges have you faced, and what solutions have you discovered?
The Networking Exchange Blog Team About NEB Team