The Ultimate Guide to Managing Data Quality

Widespread digital transformation in the utilities sector has enabled companies to accelerate the development of new sustainable energy systems. To reach the desired endpoint, utility companies must adapt to new requirements for more automation, business agility, large volume data processing, faster access to data for analytics and monitoring, and improved security. Companies that fail to achieve this end up paying in increased work hours, inefficient processes, and data duplication and inconsistencies.
Utility companies must strive to achieve higher levels of quality when collecting and analyzing data. Almost all (98%) of utility companies expressed a desire to transform their data into insight, yet many businesses struggle to ensure that the base quality of the data remains high.
Most utility businesses have adopted some form of technology to improve operational efficiency and gain insight into their customers’ usage habits. However, this data is often of low quality, containing errors, empty values, improper conversions, or outdated information. As seen below, when data is collected, it is often presented in its raw form without any context. Business leaders need context to analyze the information for actionable insight and effectively apply that knowledge to business operations.
Businesses that collect and share high-quality data can use that information to break through organizational silos by democratizing access to that data. This gives key decision makers operating in disparate business divisions an accurate overview of the enterprise. When deployed effectively, a good data model improves decision-making, reduces risks, and helps optimize spending.
A report on the topic revealed that businesses spend as much as a quarter of their annual revenue correcting errors caused by poor data quality. Duplicates of customer data, inaccurate recording, missing values, and other issues can cause existing data pools to fail in their intended purpose to provide actionable insight for utilities. Utility companies can use these indicators to help them evaluate whether their data meets their requirements.
Data inevitably has to be moved from one source to another or combined from multiple data sources. This conversion can be complex and time-consuming if done manually. With Utilihive, businesses can make use of cutting-edge software to automatically apply a conversion rule or to identify the right base value for conversion.
Data evaluation can help businesses quickly and effectively identify areas that require resolution. Using VEE data processing to process large volumes of data from smart meters and other IoT sensors means that modern iPaaS solutions can be used to apply validation, cleansing, and normalization steps during data processing. It is also possible to flag records with data quality or integrity issues such as data inconsistencies, mismatched data, or missing data.
Despite the effectiveness of modern data management software, it is inevitable that human error may cause certain data elements to be missing. In those instances, software such as Utilihive allows businesses to adjust the target system and edit data manually post-migration. Ultimately, data management does not have to be complex, time-consuming, or cumbersome. With modern AI-based solutions such as Utilihive, utilities can make sure that their data is collected, analyzed, and migrated effectively and efficiently.
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