Site hosted by Build your free website today!
Harvesting Big Data

The ultimate goal of harnessing big data is to improve customer service and achieve enterprise business goals while increasing the reliability, resiliency, and efficiency of operations. Thus, business drivers should dictate data needs and the technology roadmap to achieve ongoing improvements in these areas.

A data-driven utility should first identify its fundamental business drivers to understand precisely what intelligence is needed for operations and the enterprise and what specific technology supports the creation of intelligence and value, both for current business challenges and for future businessneeds and technology functionalities. Intelligence, and automation, relies on a twoway, integrated communication system based on standards; thus a utility must first develop a “strong” grid by establishing an information and communications technology foundation based on an open architecture and standards. Whereas in the past, leased line prices were prohibitive, they have dropped substantially in price now and are well within the range of most businesses.

This first step requires that information technology and communications groups work together to understand and support the functional requirements suchasnetworkresponserequirements,bandwidth,andlatency,ofeachdisparatedatapath—from sensor to end user—for current and future systems and applications. Then a data-driven utility should develop a “smart” grid, which requires the convergence of information technology and operations technology and their respective staff - the beginning of an operations - and enterprise-wide cultural shift to holistic utility management that focuses on value creation and eliminates organizational silos.

On the technology side, integration of data-producing devices and systems precedes automation. Determining substation automation applications relies on observing the behavior of data over time (daily, seasonally) and diverse conditions

(weather patterns).

On the organization side, all operations and enterprise groups should cooperate to identify their data needs to create a data requirements matrix. Information and operations technology personnel can then determine the least number of platforms and the most efficient paths to route data from device to end user, taking security into account. Access and authentication rules ensure that only the right person gets the right data at the right time.

A key concept in a data-driven utility is that every internal stakeholder who can create value from data should have secure access to that data. Operational data is routed to the control center in real time, while nonoperational data is extracted from intelligent electronic devices,concentrated and sent across the operations firewall to be stored and processed in a data mart for on-demand access by enterprise groups and their applications. Three case studies illustrate the value of a data-driven utility in terms of asset management and safety, the fundamentals of standards and interoperability, and the enterprise value,in dollars, of increased visibility into the transmission and distribution network.