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Data quality

Data quality refers to the fitness of a data set for a specific purpose, and is an important indicator of the value that can be derived from the data. Here you can find a data quality definition and an overview regarding key performance indicators (KPIs).

Data quality dimensions

What is data quality?

Data quality is a measure for the suitability of data for specific requirements in the business processes where it is used. High data quality in this context means that data is fit for its purpose. A low level of data quality will reduce the value of the data assets in the organization because its usability is minimal. Companies are, therefore, striving to achieve the quality of data required by the business strategy using data quality management and a strong data governance.

Data quality defines how well-suited data sets are for intended tasks

  • Data quality characterizes the degree of how given data sets satisfy the needs (fitness for use) of consuming business processes.
  • In a broader sense, it refers to both the quality of data content as well as the performance of the underlying data management processes.
  • Data quality measurement is used to assess the data quality level for selected quality dimensions that are relevant to the chosen business uses. Typical examples for data quality dimensions are completeness, consistency, validity, uniqueness, or timeliness.

The most essential tools for determining data quality are data quality rules. With their use, it is possible to check whether the information meets the defined criteria and contains the required attributes. In addition, they can also be used to improve quality, such as validation, cleansing, deduplication, or enrichment of data records.

Data quality is essential for the value of data

Poor data quality has a negative impact on the value of data (as reflected by the popular idea of "garbage in, garbage out"). In the digital economy, the role of data is changing. Data is transforming from a secondary asset that supports business processes and decision-making even to a primary one enabling digital business strategies and business models. Recent studies identify data management and data quality as two major pain points when it comes to launching business intelligence and advanced analytics/data science initiatives.

In many companies, business partner master data is still maintained manually or cleansed and deduplicated for recurring data quality initiatives. The innovative CDQ Suite offers Data Quality as a Service (DQaaS), and automates master data maintenance in various stages, thus keeping data quality high in the long term. At the same time, the burden for companies is spread across many shoulders through collaborative data maintenance in the CDQ Data Sharing Community

Questions about corporate data quality?

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Operationalization of data quality dimensions

Data quality key performance indicators (data quality KPIs)

Data quality key performance indicators, in short data quality KPIs, are a quantitative measure of data quality. A data quality measurement system assesses the values for the quality of data at measurement points at a certain frequency of measurement. Data quality key performance indicators (KPI) operationalize data quality dimensions. The most important dimensions whose data quality can be assessed are:

  • Correctness: factual agreement of the data with the properties of the real world object that it represents.
  • Consistency: agreement of several versions of the data related to the same real objects, which are stored in various information systems.
  • Completeness: complete existence of all values or attributes of a record that are necessary.
  • Actuality: agreement of the data at all times with the current status of the real object and adjustment of the data in a timely manner as soon as the real object has been changed.
  • Availability: the ability of the data user to access the data at the desired point in time.

Source: Otto, Boris; Österle, Hubert: Corporate Data Quality: Prerequsite for Successful Business Models, 2015

Data quality KPI overview

Metric type Metric Description Typical measurement/reporting method
Business value metrics Impact on strategic goals Impact of data management on strategic business goals Assessed qualitatively and visualized by means of dependency graphs or traffic light charts
Economic value of data Financial value of data Assessed by means of the reproduction cost approach or the use-based approach
Impact on business process related goals Impact of data management on business process KPIs Visualized by means of dependency graphs or traffic light charts
Cost/time savings Cost/time savings due to more efficient data maintenance processes, automated data cleansing/data import processes Assessed by means of process mining
Satisfaction of external groups Satisfaction of customers, consumers, or business partners with respect to data excellence (e.g. quality of product catalogs, quality of shared data, adherence to data privacy standards and consents) Surveyed by means of questionnaires/ interviews
Data excellence metrics Data quality Quantitative assessment of data's "fitness for use" (e.g. consistency, completeness, or accuracy) Measured in terms of conformance of data with respect to certain data quality dimensions
DQ Audit findings Number of corporate data quality related violations during an audit (e.g. ISO 9001:2008) Measured by reviewing audit results
Data management performance metrics Cycle/ turn-around time Time passed from requesting a new master data object (i.e. a new supplier or consumer data record) until this record is available in operational systems (e.g. ERP) Measured by process mining, workflow logs, or ticketing system logs
Internal satisfaction Satisfaction of company-internal stakeholders such as data requestors and consumers in business processes Surveyed by means of questionnaires/interviews
Data management progress metrics Maturity score Maturity assessment of current capabilities from a strategic, organizational and technical point of view Surveyed by means of questionnaires/interviews
Supported use cases Percentage of agreed use cases fully supported by data management Tracked by means of a use case funnel
Rulebooks Percentage of data domains covered by rulebooks (i.e. definitions, data models, processes, roles, responsibilities, methodologies). Measured by means of a gap analysis between rulebook and data model
Data records under governance Percentage of data records covered by detailed rules Measured by means of a gap analysis between rulebook and data model
Geographical regions/ branches Percentage of geographical regions/ branches implementing data governance Measured by means of achieved milestones in rollout plans
Role assignments Percentage of geographical regions/branches implementing data governance Measured by means of achieved milestones in rollout plans
Trained people Percentage of roles assumed by appropriately trained people Measured by means of achieved milestones in rollout plans