Report

Smart Grid Data Analytics: A New Approach for Utilities

Table of Contents

  1. Summary
  2. Overview and Opportunity
  3. Business Impact for Utilities
  4. Solution and Process Considerations
  5. Key Takeaways
  6. About Bob Lockhart

Analysis

Smart grid data analytics are a key sector of the industrial Internet of Things (IoT), with the potential to help utilities address key operational, financial, and customer challenges. Leveraging the large volumes of data created by the smart grid, along with information available through third-party data services, these solutions are made possible by modern technological developments that allow the integration, processing, and analysis of multiple sources of data quickly, at unprecedented scale.

Software-as-a-Service and managed services make it possible to implement solutions rapidly and at lower cost than traditional, on-premises software implementations. For maximum success, utilities must clearly define problem sets, data source requirements, and the employees who could best make actionable use of data-driven insights.

Key findings of this report include:

  • Smart grid analytics enable utilities to more rapidly and effectively address issues regarding improved grid operations, customer engagement, and financial management. Often, improvement in a single activity, such as revenue protection, can justify the investment in data analytics.
  • Driven primarily by distribution grid optimization and customer engagement improvements, the current global smart grid data analytics market will grow from $1.3 billion to $4.8 billion by 2022, with a compound annual growth rate of 16 percent.
  • Utilities can choose between point solutions aimed at a specific problem or business area, or providers offering a suite of solutions that span the enterprise, operating from a single software platform. Point solutions may offer a quicker route to solving a specific problem, but enterprise-wide approaches offer greater flexibility to solve future problems with existing analytical tools.
  • Data analytics can be implemented on-premise or as a managed service. While on-premise implementation provides the most direct control, it requires significant upfront investment, upfront IT work, and ongoing involvement. SaaS enables faster time to deployment, mitigates talent and execution risk, reduces capital expenditures, and provides an overall lower total cost of ownership.

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