Do you trust the quality of your company’s data? If not, you aren’t alone. In fact, 55% of business leaders lack trust in their organization’s data.  

When businesses make decisions based on inaccurate, outdated, or incomplete data, a range of negative consequences can result. The longer poor-quality data is allowed to flow through an organization’s systems, the more damage it can cause. In other words, poor data quality is a serious problem that cannot remain ignored. 

Without a data quality strategy, it becomes extremely difficult to prevent poor-quality data from entering your systems. It also becomes harder to identify and eliminate poor-quality data that does slip through the cracks. However, with an effective data quality strategy in place, these data governance in healthcare problems can be eliminated. 

High-Quality Data Is a Precursor to Improved Decision Making

Many organizations use data to drive business decisions that maximize the potential for success. The quality of the data they’re using is reflected in the outcomes of these decisions. Some business applications of high-quality data include data integration, data migration, supply chain management, and regulatory compliance. 

MANTA’s founder and CEO, Tomas Kratky, recently highlighted the importance of data quality for insideBIGDATA’s “Heard on the Street” round-up column. “Data-driven decisions can only be as good as the quality of the underlying data sets and analysis. Insights gleaned from error-filled spreadsheets or business intelligence applications might be worthless—or in the worst case, could lead to poor decisions that harm the business,” commented Kratky. 

Plenty of businesses recognize that having poor data quality is problematic. However, knowing how to fix it is another story. This is where an automated data lineage solution helps.

The Role Lineage Plays in Enhancing Your Data Quality Strategy 

Data lineage is a key component of an effective data quality strategy. Automated data lineage reveals your company’s data flows, sources, transformations, and dependencies, allowing you to take control of your data assets without the heavy lift of mapping out lineage manually. More confidence in your data information and reports translates to a higher degree of data quality and accuracy. 

When speaking with Betty Carpenito, Director, Data Governance & Data Quality, United Community Bank, she stressed the importance of data accuracy for better business outcomes. “Everyone at United Community Bank is committed to a shared goal: delivering an extraordinary customer experience,” said Carpenito. “By deploying MANTA, we get visibility into data that helps us achieve that goal. We can see where data persists in different systems, giving us ‘a-ha’ moments of insight, so that we can provide greater value and offer the best possible experience to our customers.”

Yet, despite its role in assuring data quality, lineage often goes overlooked. O’Reilly found that 80% of organizations were not managing data lineage in 2020. By understanding the role automated data lineage can play in your data quality strategy, your company can reap the benefits of improving data quality. 

MANTA’s new whitepaper on Supporting Data Quality Processes with Automated Data Lineage goes into greater detail about how your company can use automated data lineage to support data quality processes. Click on the banner below to access the whitepaper and learn more.

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Supporting Data Quality Processes with Automated Data Lineage

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