As the “Internet of Things” (IoT) expands, reaching 25 billion connected devices by 2020, according to Gartner, utilities will face an unprecedented volume of data generated from new digital equipment, systems, devices, and sensors on the grid and at their customers’ premises. Gartner also predicts that the proliferation of IoT will bring significant new application and data integration challenges as the number of new connections for IoT devices will exceed all other new connections for interoperability and integration combined.

Historically, application and data integration costs — both first-time and those associated with ongoing maintenance — have been significant and frequently underestimated, according to Zapthink. The more differences there are in application architectures and different approaches to integrating applications, the more costly the overall integration effort becomes. Both the proliferation of new data sources and the vastly increasing volumes of data being generated by IoT further exacerbate the integration effort, causing these costs to rapidly escalate over the next several years, says Gartner research.

Data analytics solutions to integrate, aggregate, and process these data are critical. Utilities must closely evaluate the relative merits of taking a platform approach or deploying multiple point applications to analyze these large data sets.

With a platform approach, utilities deploy an integrated family of cloud-based, smart grid analytics applications built on a common, enterprise data platform. In contrast, utilities could use multiple, independent, on-premise or cloud-based, point applications to address individual, specific use cases.

Taking an enterprise, cloud-based platform approach results in significant cost savings. To estimate the magnitude of these savings, consider a large utility with 10 million customers and three different operating companies. In order to create a comprehensive smart grid analytics capability across the value chain, the utility might need to procure and deploy five different analytics applications. Examples are:

  • Revenue protection to detect electricity theft.
  • AMI operations to optimize smart meter deployment and network operation.
  • Predictive maintenance to prevent asset failure and enhance operational and capital planning.
  • Voltage optimization to reduce overall system voltage.
  • Outage management to enable faster response to and better recovery from system outages.

The following analysis illustrates that the cost savings of deploying and maintaining an integrated family of applications built on a common, enterprise, cloud-based platform is up to $189 million over five years. The models and assumptions have been validated with IT operational and financial executives across numerous global utilities.

These cost savings accrue from four areas:

  • Data integration and implementation.
  • Hardware and software infrastructure and services.
  • Hardware and software maintenance, support, and operations.
  • Procurement of the solutions and support hardware and software.

Data integration and implementation

Gartner has forecasted that, in the coming years, companies will spend more on application integration than on new application systems. A platform approach minimizes these integration costs. Deploying an integrated family of applications that share a common data architecture and cloud-based platform enables a utility to perform a single initial integration without having to repeat the work with the addition of new applications. A platform approach also provides the benefit of being able to flexibly deploy applications either at one time or sequentially over time with little to no incremental effort or cost.

Recent experience has shown that deploying a single smart grid analytics application, whether on a platform or not, requires approximately 25 data source extracts. Adding four more applications on a platform typically requires only an additional 25 data source extracts for a total of 50. Many data sources are shared by different applications on the platform and all of the data are available to all applications deployed on the platform, which results in the minimal number of total extracts.