Future of Meter Data Management

Technology, Blog posts

This content was originally created by Energyworx.

Future vision

Metering has evolved from being just a component of the revenue cycle for meter-to-cash use case, to being an enterprise function that supports multiple core business processes providing operational benefits.

For utilities undergoing digital transformation having the right solution to drive mission-critical operations and new customer-focused solutions is crucial. The chosen solution should be more than a typical MDM platform, serving as the digital backbone for energy companies.

A solution should be oriented to provide its users a future perspective that defines new business models and use cases. And one that unlocks the possibility of integrating Machine Learning functionalities and other Data Analytics based solutions.

Modern technology for a modern market

Utilities, in the process of evaluating their MDM solution should consider one designed and built with the latest IT technologies as used by the modern hyperscalers and social network applications. It should be designed for interminable scale and maximum flexibility in a rapidly changing utility sector.

Flexibility for easy implementation of current and future smart grid regulations

The design of the solution should be made with flexibility in mind: a flex data model, smart integration, allowing ease of configuration for use cases in the energy value chain, driven by market, technology and regulatory changes.

Integration out-of-the-box

There should be an integration layer to manage and monitor the integrations and data exchange between the MDM and MDC on the one side and customer’s systems on the other side. This integration layer allows the utility to monitor the entire AMI / smart grid IT landscape and the end-to-end value chain (metering, collection, meter data management, billing, reporting to TSO, market communication) across all involved systems, integrations, and data exchanges. This will reduce project risks during implementation and minimize complexity during operations.

Benefits of using a managed service from the cloud

With traditional on-premise systems, the infrastructure capacity has to be planned in advance and the business is confronted with high upfront costs and the burden of underutilized infrastructure right from the start of the project. A cloud solution gives flexibility to use infrastructure on-demand.

Customers can run highly intensive analytics processes in parallel with high intensive base transactional loads. The elasticity of the cloud solution provides automatic up or downscaling of the required infrastructure, which leads to the lowest possible costs to run customers´ energy data transactional and analytical workloads.

Don’t worry about data archiving, database administration, monitoring, sizing, backup and restore.

Architecture design principles

The Architecture design principles should follow an application modernization strategy considering:

  • A microservices-based architecture

  • A smart way to ingest, crunch, find and correlate time-series with context data

  • Easy and simple to use by developers and users

  • Perform at scale, auto-scaling for peak loads, resilient and fault-tolerant

  • A Security proposal by design transforming data into smart decisions

Business foundation

    The capabilities of a selected MDM solution should provide a foundation that enables the Utility to:

    • Unify in a common repository all information associated with measurement management processes and systems, providing homogeneous mechanisms for inventory and measurement processing.

    • Streamline the processes associated with measurement management, taking advantage of the capabilities of a platform that provides the software infrastructure, services and data model for greater flexibility and agility in the business and adaptation to regulatory changes.

    • Enable new opportunities and business models by leveraging the capabilities of an interoperable and open platform with Data Analytics and Machine Learning technologies.


    Main requirements to consider:

    • Unification of measurement and asset information in a single repository, common to all data sources.

    • Definition of homogeneous measurement information processing mechanisms, adaptable by configuration (and exceptionally, for complex cases, by developing extensions) to functional and regulatory needs.

    • Reduction of the number of applications and systems involved in the Measurement Management processes.

    • Common integration interfaces on one platform, both with Head-Ends and Client OT/IT systems, supporting the standards defined by the utility.

    • End-to-end traceability and auditing mechanisms common to all processes and applications managed by the platform.

    • A single user-friendly interface, common to all applications, providing a homogeneous, user defined perspective for all users.

    • Transparently scalable architecture in the cloud, both horizontally to support the large number of connected devices, as well as vertically to address the variety of new solutions and use cases.

    • Flexible and Fastest Time to Market. The utility can fully focus on creating business value from data for their customers.

    • Unlike a custom developed solution, maintenance during operational life of the system and automatic access to new valuable features are included in SaaS.

    • Advanced Data Analytics and Machine Learning Capabilities.