QueBIT: Strategic Big Data Analytics to Drive Business ROI
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QueBIT: Strategic Big Data Analytics to Drive Business ROI

Gary Quirke, CEO, QueBITGary Quirke, CEO
The essential need for improved analytics is driving the discussion around big data in the competitive and evolving utility market. Traditional data warehouses, which haven’t necessarily been designed optimally to support analytics, have proven to be slow and very costly to maintain. CIOs have been turning to alternative architectures, such as Data Lakes, to store very high volumes of structured and unstructured raw data, both inexpensively, and in a manner that is optimized for current and future predictive and prescriptive analytics needs. “Having a deep experience in applying predictive and prescriptive analytics across all verticals, including energy, we offer advanced forecasting methodologies to generate more accurate forecasts that include causal variables such as weather, macro-economic indicators, and supply chain constraints,” explains Gary Quirke, CEO, QueBIT.

The company provides a cutting-edge predictive maintenance platform that leverages predictive modeling and is optimized through the use of prescriptive algorithms. “The platform allows content management system data to be consumed in real-time to predict the heath of assets employed,” says Quirke. The prescriptive algorithms generate optimized maintenance schedules based on constraints, which maximize asset up-time, usable life, and minimize service costs.

Open source technologies such as Apache Hadoop and Apache Spark act as game changers for big data analytics. Focused at democratizing analytics on big data, Apache Hadoop enables companies to perform high-performance data access, cleansing and analysis on big data. “Leveraging machine learning algorithms in MLLib (Apache Spark) allow data scientists, in conjunction with IT, to build robust advanced analytics applications and deliver analytical insights quickly and efficiently on massive data sets,” says Quirke. Apache Hadoop/Spark enables companies to store and analyze both structured and unstructured data in a far more scalable manner. Combined with scalable text mining capabilities, data scientists are able to extract structured data from unstructured text, and use it to augment predictive models. “The end-result is a far more accurate analysis of information, and this enables better decision making, lower costs, and increased revenues and profits.”


We offer advanced forecasting methodologies to generate more accurate forecasts that include causal variables


QueBIT’s growth plans are centered on the increasing demand for advanced analytics and expanding the leverage of highly valuable data assets across all industries, including utilities. Solutions and predictive techniques developed for other industries have significant potential application for the Utility Industry. For example, QueBIT’s Predictive Police Deployment (QPPD) solution generates high-resolution heat-maps and analyzes recent crime events and environmental conditions. This advanced analytics solution predicts future crime ‘hot spots’. QPPD consumes historical crime data, in addition to weather, community events to model violent and non-violent crime behavior. Delivering future weather forecasts, community events and other environmental conditions, the predictive models can predict where crimes are likely to occur hour-by-hour, over an entire metropolitan area. “These updated hot spot maps are delivered to police officers via mobile devices so that patrol areas can be adjusted as conditions change or planned or unplanned occur,” explains Quirke. The exact same techniques employed for predictive policing have relevant application for the utility industry in predicting consumer demand and outage occurrence.

QueBIT envisions a world where the Internet of Things (IoT) massively evolves the amount of data for analytics. This will further increase the need for big data strategy and deployment, and both mobility and location awareness will be critical in the maximization of ROI from IoT integrated analytics. “Our go forward strategy is aligned to the ‘Third Platform’ (the convergence of cloud, mobile, social, and analytics), with the IoT being a significant driver for the expansion and adoption of Third Platform,” says Quirke. Furthermore, QueBIT’s growth plans will significantly focus around big data strategy and optimization, traditional data warehousing optimized for analytics, business intelligence, financial and operational modeling, predictive analytics, and decision optimization solutions.