GreenFlex uses machine learning to identify energy consumption anomalies

Overview

GreenFlex, a European leader in environmental management and energy efficiency services, gathers IoT energy consumption data from customers and leverages machine learning to identify consumption anomalies, helping customers devise tailored energy optimization strategies. The company modernized its data infrastructure with the Databricks Data + AI Platform and Unity Catalog to manage and govern terabytes of energy consumption data supporting these ML and BI use cases.

The challenge

As the amount of data GreenFlex gathers and analyzes grew exponentially, the company needed a simplified way to manage and govern the data it collects and make it easily and securely accessible for data exploration, business intelligence and ML use cases, and it was complicated and painful to deal with security and access controls over tables in each of three unconnected workspaces.

The solution

GreenFlex gathers energy consumption data from its customers and leverages machine learning to identify any consumption anomalies, helping customers devise energy strategies best suited to their needs; with the Databricks Data + AI Platform, GreenFlex modernized its data infrastructure, and Unity Catalog simplified access management with a single pane of glass for standardized SQL access policies.

Reported business value

Using Unity Catalog, GreenFlex has centralized data governance and access management, accelerating the company's ability to onboard new employees and grant access to data from about a week to a few minutes, giving data engineers and scientists more time for value-added tasks.