Boosting Data Quality With Data Governance and Automation
Data’s quality is threatened as it is ingested, used, transformed, and manipulated. If enterprise users can’t gauge the quality of their data, they won’t use it.
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Most organizations know data is one of their most valuable assets. Yet, while that asset is at risk of ongoing corruption, protecting and increasing data quality is often an afterthought.
With so much at stake, organizations are looking to strengthen enterprise data quality with automation and machine learning.
Data’s quality is threatened as it is ingested, used, transformed, and manipulated. If enterprise users can’t gauge the quality of their data, they won’t use it. Nor should they. Low-quality data generates inaccurate, unreliable data intelligence.
By contrast, quality data delivers valuable analytical insights, improves decision making, and reduces compliance risk. To effectively take on today’s vast data quality challenges, businesses must understand the downstream repercussions information errors have across the enterprise.
Understanding Today’s Data Quality Challenges
Clean, high-quality data is difficult to achieve and maintain. Quality data is also essential to ensure meaningful insights that drive growth, increase revenue and improve business processes and operations. Yet, many companies struggle to enact an effective plan because they can’t find the source of quality issues.
The most common data quality problems and fixes include:
- Quality Issues From the Source: When source systems contain incomplete or inconsistent data, risk increases when it’s moved to other target systems. Organizations must then emphasize data quality at the source so bad data doesn’t perpetuate downstream.
- Third-Party Challenges: When organizations ingest data from external sources, the quality of that information is typically unknown. It is critical to apply data quality checks for completeness and accuracy on all data that enters an enterprise.
- Complex IT Infrastructure: As the number of information sources, platforms, and applications increases, so does risk. Therefore, it is essential to consolidate and monitor changes within the complex IT environment to ensure that internal and external data remains accurate and consistent.
- Data Transfer and Process Deficiencies: Transferring information requires rules to prevent data structure errors because improper formatting, blank fields, and transformation errors can prevent data from loading properly onto the target system. Simplifying extraction and loading processes can help organizations maintain data quality during processing.
- Updates to Reference Data: Data is in constant motion. Changes and updates are occurring every second. Standardized reference data enhances data quality rules to flag potential incorrect inputs, preventing errors from affecting other systems.
With data always moving across disparate systems and platforms, comprehensive visibility into data processes and procedures is key. These challenges highlight the urgent need for businesses to take advantage of machine learning and automation technologies to track, protect and provide quality data.
Utilizing Modern Tools and Techniques to Confront Data Quality Issues
It’s easier and less expensive to fix quality issues early in the process before they cascade into other systems. Through an enterprise data governance framework with an integrated data quality program and automated business rules, organizations will prevent data quality failures.
Where to begin depends on the maturity level of a data governance program. To start, organizations must identify all critical information flows and baseline data quality metrics. This includes data provisioning systems, external source systems, along with their data lineage.
System owners must also define any known data quality issues, pain points, and risks. A cost-benefit analysis can help organizations evaluate the appropriate response, prioritize high-risk information and deploy controls to resolve quality issues.
With information controls and exception management processes to address the identified risks, automation can improve efficiency. In addition to automating business rules and reference data validation, machine learning and AI techniques immediately flag data outside quality thresholds the second it enters a system. Additionally, machine learning algorithms automatically monitor new information against historically resolved issues to categorize, relate and ultimately improve data quality.
Utilizing machine learning automation and data governance to proactively resolve data quality problems at the source builds trust with data users. It also provides the most cost-effective approach for continuously monitoring and solving data quality challenges as information moves within and across various systems.
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