What is Data Quality?
Data Quality is the degree to which data meets an organization's requirements in terms of accuracy, completeness, consistency, timeliness, and validity. High-quality data is data that teams can trust to make decisions, generate reports, and feed AI models.
Data quality is typically evaluated through six dimensions: completeness (absence of null values where data is expected), uniqueness (absence of duplicates), validity (data that meets expected formats and ranges), consistency (data that is coherent across different systems), timeliness (data that is recent enough for its intended use), and accuracy (data that reflects reality).
It is important to distinguish between perceived data quality and measured data quality. Many organizations assume their data is good until an incident proves otherwise. An effective data quality program proactively measures these dimensions through automated rules and alerts.
Why it matters
According to IBM, poor-quality data costs the US economy approximately $3.1 trillion per year. Gartner estimates the average impact per organization at $12.9 million annually. These costs are not only financial: they include poor decisions, damaged reputation, regulatory fines, and lost productivity.
With the rise of generative AI, data quality becomes even more important. AI models trained or fed with low-quality data produce incorrect or biased results, amplifying problems rather than solving them. The "garbage in, garbage out" maxim has never been more relevant.
How it works in practice
A data quality program defines rules that are automatically executed against real data. For example: "the email field must not be null in the customers table" (completeness), "the ID field must be unique" (uniqueness), "the date field must follow the format YYYY-MM-DD" (validity). Each rule produces a score (percentage of records that pass) and generates alerts when the score drops below a configured threshold.
These rules are executed periodically (daily, hourly, on each ingestion) and the results are aggregated into quality dashboards that allow monitoring trends, identifying degradation, and prioritizing fixes.
Data Quality in Linedat
Linedat executes 6 types of quality rules against real data in connected sources: completeness, uniqueness, validity, consistency, timeliness, and accuracy. Each data asset receives an aggregated quality score and alerts automatically notify when quality degrades below thresholds configured by the team.
Related terms
Data Governance is the framework of policies, processes, and roles that ensures data quality, security, and correct use across an organization.
What is a Data Catalog?A Data Catalog is a centralized inventory of all of an organization's data assets, with metadata, descriptions, and lineage.
What is Data Impact Analysis?Impact analysis evaluates what will break before making changes to data, tables, or columns. It prevents downstream incidents.
What is a Data Contract?A Data Contract is a formal agreement between a data producer and consumer that defines schema, quality, SLAs, and responsibilities.