WhereScape

Although Jersey is not a part of the UK Smart Meter roll out, Smart Meters are a central part of a large-scale government project to upgrade the UK’s energy system, replacing around 53 million existing gas and electricity meters by 2020, allowing consumers a more accurate view of their energy consumption. Designed to provide ‘near real-time’ information on energy use, smart meters will enable consumers to manage their energy use, allow supplier switching to take place more easily and remove the need for suppliers to read meters and provide estimated bills. As the sole supplier of electricity across the island, Jersey Electricity decided to proactively rollout smart metering ahead of the rest of the UK, aiming for installation of up to 45,000 meters for domestic and commercial customers by the end of 2018. Planned as a three-year project, Jendev, its subsidiary utility software development business, examined a variety of options to deliver key components, including the smart meters themselves and the critical data warehousing technology to gather, store and analyse the data provided by every smart meter. This data collection and analysis presented an important challenge. In every property, the smart meter needs to take a daily consumption read as well as 48 half hourly reads, 365 days a year – an eventual total at the end of the project rollout approaching 1 billion reads per year. This data is passed from each property to a data concentrator in its local substation, which are all polled daily to collate data in the central data warehouse for processing and analysis.

Toyota Kreditbank's BI team was looking for a new solution to standardize the data model, renew the technical infrastructure and implement a unified methodology for each country to deliver its data in a standard XML format and on a daily basis. The team would then only need to do the sourcing once and reapply for each country.

Micron Technology, a leading innovator in memory and semiconductor technologies, faced a significant challenge in managing and extracting manufacturing big data from 12 separate sites. The company was unable to deliver data within the requested timeframes, leading to many sites attempting to access OLTP data directly. This resulted in redundant processes, resource inefficiency, and data integration and performance issues. The data warehousing and analytics solutions developed were not able to keep up with the needs of the business. Projects that did occur were isolated, with no ability to build onto or modify when needed.