In the fast-moving world of data-driven organizations, leaders expect their teams to use data to draw insights, make informed decisions and develop strategies that outperform the competition. Few would argue that business leaders know the importance of data, especially when setting goals or using analytics to increase revenue. However, with organizations having access to more data than ever before, staff in every industry are plagued with data quality issues because of human errors, duplicate information, data inconsistencies, missing values and more. When data is riddled with errors and mistakes, users won’t trust it. If users don’t trust their data, they won’t use it.
Poor data quality undermines critical business functions and slows down decision making, potentially causing missed opportunities, overlooked business problems and, long-term, millions in time, resources and profits.
Compounding data quality issues is GDPR, the California Consumer Protection Act (CCPA) and other data privacy laws. Inconsistent data quality can also lead to hefty fines.
So, how do you address data quality issues to ensure accuracy, completeness, consistency, build data understanding, ensure trust and successful usability? Here are four must-have strategies.
Everyone is frustrated when data is incomplete and inconsistent. Yet, from the moment data enters an organization, it moves through various complex processes, systems and networks, transforming along the way.
With all this data in constant motion, these changes not only threaten data integrity, but users can’t depend on the information when they don’t know where it came from, its meaning or its form–posing an operational, financial and security risk.
To address these issues, organizations must track business and technical data lineage, documenting data’s flow from origination through consumption. 3D data lineage empowers users to quickly uncover the impacts of data within its context and business processes.
By confirming how data flows through an organization’s systems and how it impacts processes, users gain confidence that there is oversight and transparency with any format, function and integrity level changes.
By tracking data lineage from varying perspectives, organizations establish data trust and empower users to leverage data as a valuable business asset.
Often, data quality is reliant on the strength of data governance because, without it, users don’t have data understanding.
Data understanding ensures that data is consumed appropriately to maximize value and mitigate risk.
Successful data governance fosters data understanding. It requires organizational alignment, an enterprise-wide framework, clearly defined business requirements and detailed objectives. Without data governance in place, users would not only lack access to usable data but may not know what data is available and its potential value.
A data governance framework also provides organizations with clear direction on the usability and quality of any data asset and establishes parameters for data access, understanding, collaboration, visibility and accountability.
When data quality improves within the data governance program, it assures data’s accuracy, completeness and relevance, tackles data credibility and trust and ensures information is used appropriately and effectively.
In addition to ensuring data understanding, data governance serves many functions.
One example is creating a comprehensive data catalog that organizes the technical details around data assets, or metadata, into defined, meaningful and searchable business assets. A data catalog is essential to business users because it synthesizes all the details about an organization’s data assets across multiple data dictionaries by organizing them into a simple, easy-to-digest format. The data catalog provides clarity into data definitions, synonyms and essential business attributes so all users understand and can leverage their data as an asset.
A data catalog identifies data owners, stewards, and subject matter experts so business users know where to go when they have important data questions—enabling easy collaboration between different departments.
Not only will users gain a more consistent understanding of data across the organization but an easier time discovering, consuming and analyzing data.
A data catalog also documents data lineage, providing depth and origin to the data users need.
The reality is that in today’s data-infused marketplace, organizations require a suite of capabilities for data governance, data quality, data catalog and analytics to ensure business-critical data is available to users across the enterprise. The ultimate integrated solution not only improves enterprise data value and resolves data quality issues before they multiply across systems but helps empower business and technical users to extract value from data.
Data quality monitoring and improvement capabilities should include high-capacity data integrity checks along the data supply chain, including checks for completeness, conformance and validity. Automated quality checks can then go beyond basic quality dimensions to ensure that as data transforms as it flows between and across systems, it remains reliable.
Comprehensive quality rules between sources and systems assure that data quality is maintained throughout the data supply chain.
Data quality, combined with a well-designed data governance strategy, ensures users can quickly find, understand, use and trust data to enhance data understanding, achieve business objectives and drive ROI from data ingestion through consumption.