In all types business, the power and potential of data continues to grow. Advancing technologies are continually changing the data landscape, but the one constant is the persistent problem of bad data. It’s time for a data quality reality check, as skyrocketing data volumes, external data integrity threats and real-time data contribute to seemingly endless data quality issues that challenge organizations worldwide and adversely affect decision making processes.
Trustworthy, high quality data is essential to successful business outcomes, but notoriously difficult to achieve and maintain. Accurate, consistent and complete information produces valuable analytical insights, mitigates compliance risk, improves operational efficiencies, drives growth and competitive advantages. In contrast, bad data at any point in the data supply chain can pollute systems and processes, derail business intelligence initiatives and raise financial and reputational risks.
That’s why data quality must be a priority in every organization’s data management strategy. But before businesses execute a plan, they must learn what’s causing their data quality issues in the first place. Here are six data quality lessons on the root cause of data integrity issues:
Companies have a multitude of disparate source systems. Any incomplete or inconsistent data at the source increases risk when transferred to the target system. To solve this problem, organizations need increased visibility into this risk and must place a greater emphasis on data quality to prevent unreliable data from proliferating as soon as possible within the data lifecycle.
Organizations ingest information from a long list of outside sources, including IoT devices, social media, e-commerce websites, online transactions, web applications and more.
An effective first step to ensuring the quality of all that external data is to apply data integrity checks to all new data that enters a company’s data supply chain to ensure data elements are complete as expected, meet specific patterns or not entered as “99999999” or “XXXXXXXX”. Conducting routine checks for accuracy and completeness upon receipt of data from third parties helps monitor data from the moment it enters the enterprise to ensure quality is maintained through every system and environment.
In complex environments quality processes can often become “silo’d” with little visibility into the full process a specific dataset or transaction is configured for. The number of data platforms and applications are constantly increasing, along with the speed and scale of data. As a result, risk to data quality also grows. Companies must establish appropriate data quality oversight to monitor changes within complex data environments, whether related to internal data systems or outside demands.
Extracting and transferring data across systems is often a risk due to varying structure logic and loading processes. Businesses require appropriate data extraction rules to prevent any data errors and need to implement quality monitoring to ensure that data integrity is not degraded as it moves from one environment to another. Commonly, organizations feel that ETL (extract/transform/load) tools provide quality functions, which many do, but having those processes audited and validated can significantly improve overall quality.
Process failures typically occur because of improper formatting, blank fields and transformation errors, all of which prevent data from loading properly onto target systems. Implementing an enterprise data quality program with automated business rules helps to ensure seamless transitions and avoid any process deficiencies as data abnormalities can be identified and repaired prior to moving to downstream applications.
Businesses must regularly make changes to or update their data. Utilizing a single quality technology enterprise-wide allows for updates to business rules when things like valid values change within reference data.
Understanding the repercussions of data errors infrastructure-wide before diving into a data quality program is key. By learning critical data integrity lessons and establishing end-to-end business processes that score and monitor data quality, enterprises can ensure accurate, reliable and consistent data throughout the business.
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