Root cause analysis (RCA) is a problem-solving methodology that can be applied to identify the source of data quality issues. RCA delves beyond surface-level symptoms to uncover the underlying causes of an issue. Instead of applying band-aid solutions, RCA aims to identify and rectify the fundamental reasons behind a problem, leading to more effective and long-lasting resolutions.
Originating in the manufacturing industry in the mid-20th century, RCA has since expanded its reach to various fields, including healthcare, aviation, software development, and now, data management. Its versatility and effectiveness have made it a valuable tool for identifying the root causes of complex problems, preventing their recurrence, and improving overall system performance. In the context of data management, RCA can be a game-changer, helping organizations pinpoint the origins of data quality issues and implement targeted solutions to ensure the data accuracy, data reliability, and usability of their data assets.
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What is a data quality issue?
In today’s data-driven world, organizations rely on accurate and timely information to make informed decisions. Data is entered, collected, aggregated, and stored in various platforms like database systems, data lakes, or specialized systems of record such as CRM (Customer Relationship Management) or ERP (Enterprise Resource Planning) systems. Before reaching the dashboards that support decision-making, this data undergoes a series of transformations and integrations, passing through multiple systems and processes.
However, this complex journey can introduce vulnerabilities where data quality issues can arise. These issues can stem from various sources, such as invalid data formats during collection, errors in data transfer, processing, or transformation, or even problems during loading into intermediate databases. These issues manifest as a particular type of IT incident, specifically related to the reliability and accuracy of data, which can have far-reaching consequences for an organization’s decision-making capabilities and overall performance.
Data stakeholders
Resolving data quality issues, especially those with potential business impact or necessary process adjustments, often demands collaboration among various departments and business functions. This collaborative effort ensures a comprehensive approach to problem-solving, leveraging the expertise and perspectives of individuals across different roles and responsibilities within the organization. Collectively, these individuals who have an influence on data are called data stakeholders.
The data stakeholders who may need to be engaged in resolving data quality issues are:
Data Owners/Stewards: Lead the RCA process, ensuring proper coordination and communication among stakeholders. They make final decisions on solutions and oversee their implementation.
Data Analysts/Scientists: Conduct in-depth analysis, develop and test hypotheses, and provide expert insights into the data.
IT/Engineers: Address technical causes of data quality issues, implement and maintain solutions, and ensure the stability of data systems.
Business Users: Provide critical context about how the data is used, validate solutions, and help prioritize which issues to address first.
Root cause analysis for data quality issues
The first step is to clearly define the problem, for example, “Sales data for Region X in Q1 2024 is missing.” Then, all the relevant people get together to collect and analyze data related to the problem. This might involve looking at the original data, changed data, system logs, and any related documents. They can also trace how the data moves through different systems to see where the problem might have started.
Based on the analysis, the team will come up with possible reasons for the problem. To check these reasons, they can ask “why” five times to get to the bottom of the issue, use a visual tool like a fishbone diagram to organize possible causes, or even try making controlled changes to see what happens.
Once the root cause is found, the team develops solutions to fix the problem. They test these solutions before applying them to make sure they work and don’t cause new problems. The solutions could involve fixing technical issues, changing data processes, or updating systems. After the fixes are implemented, the team keeps an eye on things to make sure the problem doesn’t happen again. They might set up systems to track data quality and take preventative actions like setting rules for data quality, cleaning up incorrect data, or making sure data is accurate before it goes into the system.
It’s important for everyone to work together, find the real cause of the problem instead of just a quick fix, and keep working to improve data quality over time.
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The role of a data quality platform
Data quality platforms play a crucial role in the validation of data during the root cause analysis process. These platforms allow users to implement comprehensive data quality checks, ensuring the accuracy, completeness, and consistency of data across all relevant platforms along the data lineage. By profiling data and identifying data anomalies or inconsistencies, these platforms can help pinpoint the exact location and nature of data quality issues by using data quality dashboards.
Furthermore, data quality platforms enable the configuration of continuous monitoring systems for identified data issues. This means that once a data quality problem has been resolved, the platform can actively track the data to detect any recurrences of the issue. This proactive approach ensures that data quality remains high and that any potential problems are identified and addressed promptly, minimizing the impact on business operations and decision-making processes.
Below is the full process of root cause analysis for data quality issues involving a data quality platform.
What is DQOps Data Quality Operations Center
The data quality market is occupied by many vendors, and most solutions are closed-source SaaS platforms. You can start a trial period on these platforms and expose access to your data sources from the cloud to run data monitoring on your systems.
Another option is faster and avoids exposing your data to a SaaS vendor. You can try DQOps, our source-available data quality platform. You can set up DQOps locally or in your on-premises environment to learn how a data quality tool that combines integration with data pipelines, no-code data profiling and data quality management for non-technical users can help shorten the time to resolve data quality issues.
DQOps is an end-to-end data quality management solution that supports continuous data monitoring, data profiling, and data quality incident management to facilitate issue resolution. Its unique feature is customizability, which allows organizations to define custom and fully reusable data quality checks that can detect data quality issues from a business perspective.
Follow the DQOps documentation, go through the DQOps getting started guide to learn how to set up DQOps locally, and try it.
You may also be interested in our free eBook, “A step-by-step guide to improve data quality,” which outlines a proven process for fixing data quality issues and facilitating the process.
