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.

Source: Experian

What is data quality and why is it important?

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 However, this data is often of low quality, containing errors, empty values, improper conversions, or outdated information.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.

How do you measure data quality?

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.

  • Completeness: Customer data is often collected in bulk. It is often difficult to ensure that all the elements of each piece of data is present in the master data pool. Businesses should identify the key data elements that are required for analysis and ensure that those elements are intact.
  • Consistency: Utilities must ensure that data stored in separate locations are in sync and consistent. This means democratizing access to data across the enterprise and having regular checks to ensure that the data is up to date in all locations.
  • Conformity: Data is sometimes collected and stored in inconsistent formats and data types. This requires businesses to standardize data input methods, structures, and configurations. Ensuring that each record conforms to the same standards reduces errors and allows data to be extracted easily at any point.
  • Accuracy: Utility companies have reported that at least 20% of their customer data is inaccurate. This means that the data stored in the business database does not accurately reflect its value in real life. This makes it difficult for businesses to derive any insight as the base data for analysis cannot be trusted.
  • Integrity: Integrity is often used interchangeably with quality. However, the integrity of data refers to the completeness of the relationships that exist between data sets. This can occur when certain data elements are duplicated within the data set, leading to values that are “orphaned” or not linked to the right customer.
  • Timeliness: Utilities must ensure that their data is truly reflective of real-time customer habits and business operations. High-quality data is complete and available for analysis at a moment’s notice.

How Utilihive Enables Better Practices for Effective Data Management

Properly define the conversion rules

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.

Ensure the use of clean data at all times

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.

Adjust the target system

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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