Data quality is paramount to successful business outcomes, still, data quality is one of the most persistent and pervasive challenges in data management. And more often than not, organizations only prioritize quality when revenue, reputation or mission-critical data is at risk.
Yet due to complex regulatory compliance industries like healthcare, insurance, banking, and so on are prioritizing data quality as a critical component, understanding that they cannot wait for data quality horror stories to provide evidence that poor data quality is having an impact on your organization.
To gain a clearer insight, Digital Journal spoke with Emily Washington, EVP of product management at Infogix, a leading provider of data management tools.
Digital Journal: How important is data for a modern business?
Emily Washington: Data is arguably a business’s most valuable asset. It is critical for modern companies to derive analytics from their data to evaluate the competition, identify emerging business trends and ultimately, gain a competitive advantage.
DJ: How important is that these data are ‘good data’?
Washington: Organizations with high quality, accurate, trustworthy and reliable data are better equipped to track consumer spending habits, revenue patterns, predict their business future, fuel innovation and increase revenue.
DJ: What is the impact of poor quality data on a business?
Washington: If data’s integrity isn’t verified, business users across disparate lines of business won’t trust the data, let alone utilize information for analytical insights. And if they do leverage poor quality data, the negative consequences of faulty insights can proliferate across the enterprise. Bad data leads to increased risk in reporting non-compliant information, phony analytics, bad customer experiences, loss of profit and missed opportunities.
DJ: How can data quality be ensured?
Washington: To ensure data quality enterprise-wide, companies require a variety of data quality checks, including traditional checks to ensure the completeness, consistency and conformity of data. These validations are necessary for measuring the quality of data used for business intelligence. Balancing and reconciling information across data are equally important to ensure data remains accurate and consistent at each location. Missing or inaccurate data can quickly lead to lost revenue or reputational damage.
Timeliness checks are also vital to monitor when files arrive and flag any late or missing files. Timeliness validations are especially valuable when dealing with outside information. Statistical controls are essential to validate data sets based on industry- or organizationally-defined statistical values. Finally, reasonability checks are key to affirm data values meet defined and expected thresholds. These checks empower businesses to find data issues that might otherwise go unnoticed by traditional checks.
DJ: What role does data governance play in this process?
Washington: As data is generated internally or from a third party, it is essential to verify and maintain its quality. As data travels through the data supply chain, it is exposed to new processes, procedures, transformations and uses, which all introduce risk in minimizing the integrity of data. It is vital to proactively solve data integrity issues before they create significant problems for companies and negatively impact their business. Data governance provides a framework for protecting data quality by establishing people, technologies and processes to enable business users to easily understand, access and apply data. Governance educates data consumers on data usage, meaning and quality levels, to build trust and encourage data utilization across all lines of business.
DJ: Does data regulation get in the way?
Washington: Laws like the General Data Protection Regulation (GDPR) in Europe and new legislation in certain U.S. states requires organizations to protect the personal data of consumers. However, they actually spur organizations to ensure enterprise data quality, which enables broader operational and analytical benefits. Businesses require strong data governance to identify and protect personal data, control data access and track lineage as data moves from sources to systems and processes, but data quality also plays a key role in minimizing compliance risk. Poor quality data can easily result in compliance violations that negatively impact a company’s brand and bottom line.
DJ: Can businesses combine good data while meeting regulatory standards?
Washington: Yes, absolutely. By implementing quality checks to ensure the integrity of data before use, while simultaneously governing data to ensure compliance with complex regulations, while providing business users with easily understood, well-curated data, organizations can develop meaningful analytical insights.
Emily Washington is executive vice president of product management at Infogix, where she is responsible for driving product strategy and product roadmaps. Since joining Infogix in 2002, Emily has worked closely with product development teams and customers to drive the introduction and adoption of all new products. Before Infogix, Emily worked at Cyborg Systems and Respond.com. Emily holds a Bachelor of Arts degree from San Jose State University. She also holds a certification in graphics design from The Art Institute.
