The potential source of information available to the utility seems infinite. It is up to the utility to ensure that this data is maximized by properly analyzing and utilizing it effectively for the benefit of the utility itself and its customers. The analysis of energy consumption is essential if utilities are aiming to enhance customer relationships and remain competitive in the industry. We cover these aspects in our interview with Joel Hagan, CEO, Onzo [Data Analytics=Improved Convenience?]
Kevin Monte de Ramos, a data science executive at KMDR Research, in his article, Big Data Requires a Grand Vision, suggests that utilities take into consideration the variety, velocity, volume and veracity of the data when making decisions on data management:
Variety-There are many forms of data that can be used in business operations. Structured data is created by devices, such as meters or technology that monitors the utility substation. Unstructured data is that which is created by individuals-this includes emails, text messages, pictures, and documents. Variety is important if the utility hopes to assess the wide variation of data available.
Velocity-This addresses the rate at which data is transmitted to the collection point. This is essential for utilities especially when it comes to predictive analysis of data.
Volume-This is the overall size of the data collected from all sources that feed into the business operation. While volume can serve the utility well, it can also create a lot of unnecessary work for data analysts. This is where analysts will need to employ technology to literally “sift through” the data in search for essential information which can be utilized effectively by decision-makers.
Veracity-This assesses the reliability and quality of the assimilated data. The “sifting through” of data is becoming increasingly necessary as the volume of data escalates. As the volume of the data grows, the number of erroneous data elements will increase in proportion.
Today, corporations generally aim to limit the variety of data collected. They also try to slow the velocity of data flowing into their operations, restrict the volume of data available at any given time, and reduce the impact of errant data on its customers. Informational technologists will often seek to reduce each of these elements due to costs and practicality:
Cost is often the main reason for wanting to reduce big data because as data pools grow, greater resources are needed to manage them. These cost money.
Practicality is another reason for wanting to cut down on data. Most firms have no interest in storing significant volumes of data that will never be analyzed or utilized in the organization’s daily operations.
Utilities should manage big data with directed purpose and strategic direction. It is advisable that utilities develop a vision of their operations. Without future perspective, data won’t be used effectively to reach goals.
Infrastructure investments and data must add value today and serve the best interests of the utility and customer of tomorrow. If the utility’s leadership team fails to set a clear vision for their future operations, utilities will be walking blind into a very unpredictable, competitive and volatile energy market-a very dangerous place for them to be.