Data Quality
Data quality is commonly defined as the measure of how well data meets expectations around accuracy, validity, completeness, consistency, and more.
What is data quality?
Data quality is commonly defined as the measure of how well data meets expectations around accuracy, validity, completeness, consistency, and more. It is a component of data management and data governance as it helps ensure that data is trustworthy and reliable for analysis, reporting, and decision-making.
High-quality data is fit for purpose, will deliver accurate results, and can be used to confidently make decisions. Measuring and monitoring data quality helps organizations identify, adjust for, and correct potential issues with data.
Digging deeper, data quality has seven dimensions that enable organizations to better define, quantify, and measure data quality:
Accuracy is how data reflects the entities it represents.
Completeness ensures that all required data elements are present
Consistency is uniform data across different sources, systems, or periods.
Timeliness is how up-to-date data is.
Relevance means the data is applicable and meaningful for the intended purpose.
Integrity covers data security and protection from unauthorized access or corruption.
Validity measures whether data conforms to a standard format with no duplicate records.
Why is data quality important?
It’s obvious how accuracy is a measure of data quality. However, other dimensions of data quality can lead to poor or misguided decisions, operational inefficiencies, and financial missteps. Bad data can also erode customer trust through incorrect information and frustrating experiences and lead to regulatory fines and penalties caused by misstatements and non-compliance.
For example, using out-of-date information to manage inventory can lead to over- or under-stocking materials. Duplicate data can overstate a certain input’s positive or negative impact and lead to decisions that exacerbate an already bad situation. Inconsistent data can cause different teams to make conflicting decisions that waste time and resources.
As artificial intelligence (AI) tools become more popular, their reliance on large amounts of data for training makes access to high-quality data more crucial than ever for trustworthy insights, actionable results, and reduced bias risks. As AI makes more decisions, organizations must have confidence and trust in the data used to train AI.
Benefits of high-quality data
Maintaining data quality at high levels provides several benefits, such as:
Better decision-making based on reliable insights that enable well-informed choices to achieve growth, profitability, efficiency, and other goals.
Enhanced efficiency with processes that run more smoothly and with fewer interruptions caused by data errors.
Improved customer satisfaction through better experiences and increased trust.
Improved compliance with more consistent reporting and data management processes.
Lower costs by preventing rework, bad decisions, disruptions, and costly errors.
Challenges to improving data quality
With data being generated from so many different sources and innovations continuously adding to data volumes, managing data quality has many challenges.
Common issues organizations run into when approaching data quality initiatives are:
Too much data overwhelms teams and processes.
Siloed, disconnected, and rogue systems block the ability to gain a comprehensive view of data.
Defining and quantifying the elements of data quality can cause disagreements and inconsistencies.
Ownership, stewardship, and accountability for data slows data quality efforts.
Best practices for improving data quality
As more organizations recognize the importance of improving data quality, best practices have emerged for launching and maintaining data quality improvement initiatives.
Key elements of a data quality program include data governance tools and processes to define and enforce policies and accountability, monitoring and reporting on quality-related metrics, automated data quality monitoring to identify potential issues, and data quality audits to ensure effectiveness.
It’s also a best practice to use a data quality framework to implement and manage these and other components of a data quality program and to adjust as organizational needs change.
Modern tools are available to support data quality programs, with some data quality solutions using AI and machine learning to detect data anomalies and predict data quality issues.
How a data catalog helps improve data quality
Data catalogs are crucial in data quality efforts by providing a central solution for metadata, data governance, data lineage, and related information and processes.
Key features of a data catalog for data quality include:
Enhanced data discovery
Improved data governance
Automated data lineage tracking
Seamless collaboration for sharing insights
Automation to accelerate data-related processes
Next steps: Learn more about data quality
Learn more about the importance of data quality, how to launch a data quality program, and how a data catalog improves data quality with the following resources: