Accurate trustworthy data is at the foundation of any modern data initiatives. Those in pursuit of an effective data quality strategy need to think about fostering a data-driven culture with a top-down approach.
At Data Summit Connect 2021, Jairo Gomez, senior manager, infrastructure & technology, ACAMS—Association of Certified Anti-Money Laundering Specialists, explained how to use data cleansing and enrichment solutions to assess and improve the quality of data.
Critical to data processes, he said, is the establishment of a comprehensive data governance methodology and identifying the ground rules for the collection, processing, transformation and maintenance of data.
With the advent of big data, it has become well-understood that information is a foundational part of a company’s strategic decision making. But there are many challenges that affect a company's ability to collect and analyze data and produce insights, said Gomez. For example, you could say that data is perishable and can become irrelevant, and data needs to be able to be visualized in user-friendly way in order to be actionable. In addition, any data-related processes from collection to mining or reporting should also be efficient, and preferably automated. But perhaps the most important challenge about dealing with data is its quality, said Gomez.
Problems with data reliability and quality stem from a number of issues, said Gomez, who cited a study looking at problems found by users of customer data management software. Many respondents to the survey said they found the data was not useful. Duplicate records and outdated information ranked as the top reasons why users struggle when trying to draw any insights, and nearly half of the respondents said there were no processes in place to prevent new data from creating problems, such as having internal controls for validation rules or error handling.
In addition, he noted, companies often seem to lack a basic understanding of who really owns the data or is responsible for its quality. But the answer is that everybody in the business is responsible for ensuring the quality of its data whether it is the implementation of controls or permissions, sales and service operations teams at the time of entering new sales orders, or confirming and updating information during a support call with a customer. Therefore, data quality requires a cross-functional.
Gomez also showcased a case study of how the Health and Human Services Office of the National Coordinator (HHS ONC) tackled the challenge of duplicated and mismatched records which was impacting both patient care and the processing of claims. He explained the steps taken by the HHS-ONC to improve data quality, and the ultimate benefits it was able to achieve.
He also examined the tactical matters related to data quality that start with a basic approach on how to clean data and presented a framework to help attendees visualize the steps to take in order to address the most common, yet critical, data quality issues companies face. The framework is agnostic and transferable regardless of the type of organization, organizations, or systems that you use.
Gomez’s session was titled “Developing a Data Quality Strategy.”
More information about Data Summit Connect 2021 is available here.
Replays of this and all Data Summit Connect 2021 sessions will be made available to registered attendees for a limited time and many presenters are making their slide decks available as well.