The UK’s Leading Demand Side Response (DSR) Company Triples Investment in Data Analytics for Accurate, Flexible Power Supply Analysis

LONDON and SAN JOSE, Calif., June 30, 2016

Hortonworks, Inc. today announced that Open Energi has expanded its data management and enrichment investment to include Hortonworks DataFlow (HDF), powered by Apache™ NiFi.

Open Energi, providers of a unique technology known as Dynamic Demand, aggregates flexible energy demand from across customers’ sites and intelligently shifts it in real-time to help manage fluctuations in electricity supply and demand. Its technology is paving the way for a smarter energy system which is cleaner, cheaper, more secure and more efficient.

HDF has proved an invaluable addition to Hortonworks Data Platform (HDP) at Open Energi. The complexity and challenges of collecting and transporting data are only increasing as the industry evolves. HDF has enabled Open Energi to analyse data in motion in order to better serve businesses looking to optimise energy use.

Having implemented HDF, Open Energi can increase the speed at which data is analysed, achieving a number of benefits. First, data quality management has been greatly improved through HDF’s visual approach as non-technical teams can contribute to building the rules and processes. Crucially, HDF shows every step, action and impact point on the data. It remembers all actions by and to the data, not just those with negative consequences. This gives Open Energi a clear view on what data should or should not be retained after a set given time, rather than saving all the data ‘just in case.’ By creating this fully transparent trail for data provenance, Open Energi can guarantee a standardised method of delivering the data to the National Grid as well as its customers.

On top of data management improvements, Open Energi is also now enjoying amplified data enrichment. Data can be collated, calculated and analysed in real time against rules centrally across devices and then delivered to its data lake. This means that costs are lowered because 10-15% less data is transmitted across the network. On top of this cost savings, Open Energi has standardised the output of data across its different end point devices based on the rules created in HDF. And finally, it is much easier for Open Energi to on-board data from different devices and integrate it quickly and flexibly within HDP.

Michael Bironneau, head of technical development at Open Energi, commented: “The demand for data collection and movement is growing every day as we connect to more equipment and help drive momentum behind smart grids. From day one, Hortonworks has been committed to helping us manage rapid change. Hortonworks enable us to manage the complexity involved with data aggregation whilst producing unrivalled results. The addition of Hortonworks DataFlow is expected to significantly accelerate ROI for both our customers and ourselves thanks to real-time operational visibility, feedback, and control.”

John Kreisa, vice president of international marketing at Hortonworks, added: “We are pleased to see Open Energi renewing its commitment in Hortonworks and expanding its technology infrastructure to include data in motion analytics and adopt Connected Data Platforms. This is a testament to the high level of intelligence HDF can provide, along with its ability to fill in data gaps when devices go down, enabling Open Energi to deliver improved services to customers and a smarter energy grid for the nation.”

About Hortonworks

Hortonworks is an industry leading innovator that creates, distributes and supports enterprise-ready open data platforms and modern data applications that deliver actionable intelligence from all data: data-in-motion and data-at-rest. Hortonworks is focused on driving innovation in open source communities such as Apache Hadoop, NiFi and Spark. Along with its 1,800+ partners, Hortonworks provides the expertise, training and services that allow customers to unlock transformational value for their organizations across any line of business.