Big Ideas

Trends in Enterprise IT: Convergence

Interoperability between new technologies can deliver an edge over the competition.

by Ron Batra

January 2014

The past decade has seen a rapid rise of new technologies. As these technologies get established and advance in acceptance and maturity, interdependence among them is going to a key critical success factor. Let us take a quick look at the top three key technologies in consideration.

Cloud computing

2014 will be a big tipping point time for cloud computing characterized by strong gains in Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) delivery models accompanied by advancing strength in cloud systems management, tooling, and big data. The significant investment made over the years in on-premise models will drive hybrid cloud deployments as businesses find efficient ways to inter-operate and scale their cloud and non-cloud models.

Big data

2013 was a big year for decision makers to absorb the potential of big data as a lever for competitive advantage. From a business view, organizations will have to drive the value proposition of such efforts—whether it means direct monetization of data points and streams or using big data to improve products and offers. From a technology perspective, understanding of the technology architecture—data collection, the co-existence of SQL and No SQL databases, Hadoop, and analytics and visualization—will continue to increase.

Fluid and flexible business models and technology architectures with a high degree of interoperability will differentiate businesses that can realize the full potential of big data, cloud computing and IoT.”
Internet of Things (IoT) and Machine to Machine (M2M) Communications

This is relatively a new but very fast-growing area. Event-driven architectures and sensor-based communications have the potential to explode the traditional boundaries of computing in uniquely innovative ways--whether it involves a tractor on a farm measuring yields or a transportation network tracking goods as they pass through different temperature zones. IoT and M2M have the potential to create literally millions of data points.

As these new technologies make their way into the enterprise, a key point to remember is that the typical applications that run today in the enterprise—front office, back office, e-commerce—are not being replaced, but rather complemented. Thus, integration and interoperability will drive convergence at many levels, since it would be highly inefficient to create computing silos.

Convergence will likely occur in the following areas:

Thought LeaderBatra-headshot-cropped

Ron Batra is a director of Cloud Product Development at AT&T and an Oracle ACE Director.

  1. Business processes and applications: Traditional applications—packaged as well as custom—will need to work with big data and IoT applications. 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, while those who can wait will see packaged applications adding functionality. One key driver behind this is business processes whose scope gets expanded with 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.
  2. 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. Some executives will need to incorporate sensors and data collection into the computing fabric and decide if they can apply traditional data processing rule. They may have to treat the additions with big data methods or something even more unique or hybrid. 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; hence, we need to avoid creating separate pillars of computing platforms as much as possible.
  3. Infrastructure: With cloud computing making steady advances, hybrid clouds and cloud federation will see a steady uptick with the classic considerations such as latency, network bandwidth, and site scalability—private, public, or hybrid—still being relevant. Some interesting wrinkles would come from the introduction of high volume, velocity and variety (3V’s) data into the mix and the resulting analytics. 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?

Fluid and flexible business models and technology architectures with a high degree of interoperability will differentiate businesses that can realize the full potential of big data, cloud computing and IoT in 2014. Without proper planning, organizations may tend to create computing silos and then worry about integration and convergence—a move that could prove very expensive.

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