Four Reasons to Prioritize Data Quality

Few organizations have created dedicated data quality teams, according to a study from O’Reilly Radar. To make matters worse, only 20% of companies track and report data lineage. And while reliable analytical insights require high-integrity data, many organizations only prioritize data quality when revenue, brand reputation, or regulated data is on the line.

Companies can’t wait for a data quality emergency to provide evidence that poor integrity data harms an organization’s financials, reporting, customer experience, and more. Regardless of the company’s industry or size, data quality is critical to a data management strategy.

Reason 1: High-Quality Information is Critical to Business

Data is one of a business’s most valuable assets. Organizations use data analytics to gauge competition, uncover business trends and, ultimately, increase profits. Success in all areas depends on high integrity data. Companies with high-quality, accurate, trustworthy and reliable information are better equipped to gain a competitive edge and promote growth.

Reason 2: The Impact of Bad Data Reverberates Throughout an Organization

When data integrity is not verified, business users from various departments won’t trust analytical insights. And if by chance they leverage bad data, the negative consequences of inaccurate insights proliferate across the enterprise. Poor quality information leads to faulty analytics, inaccurate regulatory reporting, negative customer experiences, missed opportunities and lost income. This is also where a strong data lineage focus can help understand the impact of bad data and what downstream usage of that data might have negative consequences.

Reason 3: Data Quality is a Key Component of Data Governance

As data travels from ingestion to analysis through different systems it can degrade, putting data integrity at risk. Companies must proactively solve data quality issues before they create significant business problems.

Data governance establishes the people, technologies and processes required to actively protect data integrity and access and apply data. Governance also provides data understanding, including data quality levels, to build trust and encourage data utilization.

Reason 4: Regulatory Compliance Means Prioritizing Accurate Data

Organizations need strong data governance to identify and protect personal data, control data access, track lineage and prove regulatory compliance with laws like the General Data Protection Regulation (GDPR) in Europe and data privacy legislation in individual U.S. states. However, data quality also plays a crucial role in mitigating compliance risk. Low-quality data populating regulatory reports lead to compliance violations that could negatively affect a company’s reputation, productivity and resources.

Businesses need to ensure data quality enterprise-wide by adopting data quality checks, including:

• Traditional checks for completeness, consistency and conformity of data help organizations measure the quality of data used for analytical insights.
• Balancing and reconciliation techniques ensure that data remains accurate and consistent at each location in the system.
• Timeliness checks monitor when files arrive and flag any late or missing files.
• Statistical controls validate data sets based on defined statistical values.
• Reasonability checks affirm data values meet expected thresholds.

By implementing quality checks in conjunction with a data governance program, businesses identify data issues that might otherwise go unnoticed. Additionally, they ensure data integrity before use, establish regulatory compliance and provide business users with easily understood information. Consequently, business users can quickly develop meaningful analytical insights.

Are you ready to prioritize data quality?  Read our eBook Seven Metrics to Assess Your Data Quality in Precisely Data360 Govern to explore metrics for a data quality assessment and avoid exposing your organization to unnecessary risk.