Right now, every manufacturing leader is facing the same strict mandates: cut operational costs, improve energy efficiency, hit aggressive corporate manufacturing decarbonisation targets, and manage the volatility and uncertainty in energy markets.
But there is a massive disconnect between boardroom objectives and the reality of factory floor operations at production sites. The business asks for consolidated numbers for comparison, clear baselining, and predictive insights to plan energy procurement and even operations.
However, both IT and OT teams are stuck wrestling with fragmented, messy data. Metering data lives in scattered systems, operational planning in their respective tools, and SCADA data in another. None of them speaks the same language, let alone being comparable across sites with often different technologies in use.
Connecting these silos manually takes months, leading to painfully slow time-to-insight and brittle, one-off reporting dashboards. You cannot optimise what you cannot consolidate. You cannot forecast if you don’t have the historical insights with the desired granularity. To actually drive change, you need a different approach to energy data consolidation over operational systems.
The engine: The Databricks Data Intelligence Platform
To handle the sheer volume, velocity, and complexity of industrial time-series data, you need a robust, unified platform. The Databricks Data Intelligence Platform serves as this foundational enabler. It provides the massive scale, unified governance via Unity Catalog, and reliable storage required to merge high-frequency consumption metering streams from field metering and SCADA layer sources. Databricks handles the heavy lifting of industrial data workloads perfectly.
But even with the most capable platform, reinventing the wheel and building from scratch is not the optimal approach for achieving justifiable time-to-value.
The catalyst: Solita’s Energy Management Foundation
This is why we built the Solita Energy Management Foundation (EMF).
To be clear: EMF is not a closed-box software product or a restrictive SaaS application, but a Databricks Brickbuilder accelerator. It provides a proven, standards-derived model for energy consumption and forecasting data consolidation from energy assets and production equipment as an elemental context. We have combined the reference data model with a reference architecture for Databricks to accelerate the bring-up and development of applications combining real-time and historical data on the energy domain into operational systems. Real-time, batch and alert data are read to the Databricks Lakehouse data management system using the Databricks Lakeflow data pipeline solution. In Lakehouse, the data is aggregated and enriched with predictions and forecasts. This data is then moved to Databricks Lakebase serverless Postgres database for low-latency data servicing. Additionally, sending real-time data also Lakebase via the operational system enables seamless combining of historical data, forecasts and real-time data for operational applications.
Think of it as a massive head start. Instead of spending months in architectural whiteboard sessions trying to figure out how to model your factory data, EMF provides the blueprint to map your metering telemetry and utility data directly into an ISA-95 derived asset hierarchy. It skips the foundational trial-and-error and gets your teams straight to the analytics with a recommended architecture.
The business case: Why do this now?
By accelerating your energy data consolidation, you unlock immediate, concrete value across both operations and the broader business:
Tracking true efficiency: By tying energy consumption directly to production output, you can finally calculate and track Specific Energy Consumption (SEC) per asset, line, or site, and analyse the ratio between baseloads and dynamic loads.
Forecasting & load optimisation: A foundation for consumption metering and forecasting data allows operations teams to further develop predictive analytics to identify opportunities for load shifting and peak shaving.
Reporting for compliance: Automating the data collection and reporting process dramatically reduces the administrative burden of maintaining ISO50001 certifications, keeping track of the impact from energy improvement initiatives, and monitoring significant energy uses from operations.
Data evidence for energy improvement: You move away from estimated emissions to hard, data-evidenced tracking, allowing you to prove progress against your corporate decarbonization goals.
Faster value for IT and data teams
For the technical teams, EMF eliminates the need to reinvent the wheel for every new production line or acquired factory site. You get a standards-driven and scalable industrial data architecture deployed directly within your existing Databricks environment. The data remains yours, fully governed and ready to be used for advanced AI and machine learning use cases down the line.
Energy efficiency improvements are not temporary projects, they require a permanent, scalable capability. You need the right enabler platform, and you need the right blueprint to get there quickly.
Ready to stop wrestling with fragmented energy metering data from operational sources or monthly consolidation spreadsheets?
Reach out to our industrial data experts, or contact your local Databricks Account Executive to see how the Energy Management Foundation can accelerate your time-to-insight.
