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East Africa's largest power distributor Kenya Power, has selected IBM for an automated system which will make available a real time status of all business processes, enabling Kenya Power to implement its strategic expansion plans. Kenya Power is to use its technology for expansion of its client base by another one million clients which presently stands at 2.6 million and also to expand its electricity generation capacity to 5000 plus megawatts by 2015 which is presently 2025 megawatts. The company plans launch of electricity distribution services across more areas of Kenya as its drives a comprehensive strategy to get additional demand from under-served regions.
The new system will be designed to consolidate data from ten key operational sources for provision of a single view of enterprise data. This new infrastructure will be using advanced IBM analytics allowing Kenya Power to study and compare real time and historical data facilitating better monitoring of business operations and trends, as well as to anticipate future electrical requirements. KP teams will be able to access data on demand on one dashboard compared to 10 different dashboards. One will be able to access the real-time analytics through cloud computing, enabling executives working remotely to use mobile devices to view data pooled from the entire company, providing unparalleled insight into the electricity company's operations, and allow instant decision making to help improve business performance. Additionally, this new system is designed to provide greater insight into customer needs.
Kenya Power at present uses various systems for billing, customer relationships and matching customer information with government census details. The IBM solution offers a central repository for various data sources and the analytic capabilities reconcile, correct, and quality-assess the information to provide a consolidated view of Kenya Power's customers. Ben Chumo, Chief Executive Officer, Kenya Power, said, “The analytics solution gives us the ability to rapidly perform complex queries on data. For example, we can better understand the varying needs of our customers across different regions in Kenya based on customer buying behavior or manage the power grids more efficiently in specific areas during certain times of the day.”
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