Poor data quality continues to derail data-driven initiatives and prevent organizations from reaching their full potential. Why? Many believe, albeit incorrectly, that data quality is inherent in their systems. Unfortunately, the reality is that many of those systems are like “Ring—the doorbell”— assuring users that their job is done with delivery. But the reality is that while you can see that a package was left at your front door, it’s not until you open the package that you can say with certainty that it was delivered to the right address, to the right person, with the right content. Same goes for “data quality capabilities.” With so much data available to improve business operations, it is imperative for companies to ensure end-to-end data quality throughout the enterprise.
Organizations today face a vast increase of data quality challenges that highlight the essential need for comprehensive data integrity rules. Clean, high-quality data is difficult to achieve and maintain, which is why companies invest significant time and resources to ensure meaningful business insights drive growth, revenue and improve the customer experience. These rules also provide visibility into the health of important business processes.
However, before developing a plan to effectively monitor data integrity, organizations must understand the root causes of data quality issues.
Common Data Quality Challenges
Understanding why data integrity issues happen is the first step in executing a plan to properly manage data. The most frequent data integrity complications include:
- Source System Data Quality Issues: If incomplete or inconsistent data from the source makes its way to the target system, risk of leveraging bad data increases dramatically. Instead, businesses must place an emphasis on data quality at the source to prevent bad data from proliferating downstream.
- Third Party Data Challenges: External data poses a similar problem as source system data. To combat third-party data integrity issues, companies require routine checks for completeness, conformance and consistency when receiving data. It also helps to ask third parties if they have data quality rules in place. If not, apply the same data quality rules with third party data as you would source system.
- Complex IT Architectures: As the number of platforms, applications and data increases, so does the probability for failure. Businesses need to consolidate their infrastructure and apply the end-to-end oversight to monitor changes within intricate environments.
- Complicated Data Transfer Processes: Data is constantly moving across applications and systems. Data structure logic and loading processes are required for data extraction to avoid errors.
- Process Breakdowns: Anything from improper formatting, blank fields or transformation errors will prevent data from loading correctly onto the target system. To prevent data integrity failures, it is essential businesses leverage an enterprise data quality program with automated business rules to ensure a seamless transaction.
- Changes to
Reference Data: Information is constantly changing as system updates occur. To
prevent changes from affecting further calculations system-wide, companies need
data quality rules in place to flag any incomplete or incorrect information.
- Numerous Unpredictable Reasons: Ranging from human error, holes in processes, timing problems, source system bugs and/or ETL bugs, as data moves through an organization, the combination of states and events make a virtually infinite number of unpredictable and difficult to trace causes for data quality degradation. Often, data quality cannot be fully evaluated unless combined with other data and circumstances, including the element of time.
Understanding the consequences of data errors across the data infrastructure is critical if businesses want to take steps to avoid bad data. In addition, companies are increasingly working with data in motion, disparate systems and platforms. As a result, businesses require data quality powered data governance for real-time operational reporting and visibility into process-level information.
Fostering Data Quality Success Through Data Governance
To ensure end-to-end data quality, organizations must establish a complete and sustainable data governance program that features integrated, comprehensive data quality initiatives.
When data is first created or ingested, the ultimate goal is to make sure users can easily understand, access and trust their data to generate actionable insights and optimize business decisions. By determining data attributes, lineage, metadata and quality, users can easily discern what data means and its quality level. By combining both data governance and data quality efforts, companies can monitor and improve business-critical data.
Additionally, high-volume data quality checks and rules within the governance program can verify the integrity of data across the organization and ensure trust among all data users. Advanced business rules that protect data integrity across different functions ensure downstream data issues don’t ruin data-driven initiatives. In addition, machine learning technologies enable self-learning to continuously improve data integrity.
By implementing data quality directly integrated into a data governance program, companies can eliminate data quality roadblocks to increase profitability and reach their full potential.
Early Stephens, CEO of Infogix.
As a visionary executive, Early brings over 30 years of experience in virtually all aspects of growing innovative and market-leading software companies. In his role as CEO, Early is responsible for leading company strategy, operations and customer partnerships. Before joining Infogix, Early served as executive vice president of strategy at Manatron, where he was responsible for all sales, marketing, product management and M&A activities. He holds a BBA in marketing and computer science from Western Michigan University.