Data Quality: The Invisible Requirement for a Successful Data Space

A data space may have the most secure connector, the best-organized catalog, and perfectly defined data-sharing agreements, yet still fail if the data flowing through it cannot be trusted. No data governance manager wants to discover—after sharing a dataset with a partner—that records were missing, duplicates existed, or values were inconsistent. At that point, it is no longer just a minor technical issue; it becomes a matter of trust.

Data quality is precisely that invisible requirement that ultimately determines whether a data space delivers real value or becomes a source of disputes between data providers and consumers. If you are a Chief Data Officer, a data governance manager, or a data analyst, you already know that trust in a dataset is not something you simply claim—it must be demonstrated through verifiable controls.

In this article, we explore the key dimensions of data quality, how they can be validated through business rules in both batch and real-time workflows, and how a modern data space automates these checks before any dataset is published in a federated catalog.

 

Why Data Quality Is the Foundation of Trust

When two organizations agree to exchange data through a data space, they implicitly assume that the information they receive is immediately usable—or, at the very least, that any limitations are clearly documented.

Without data quality controls, that trust relies solely on good faith, which is insufficient when business decisions, public reporting, or analytical models depend on the information.

Modern data governance is not about writing policies that no one verifies. It is about embedding automated controls that are executed every time data is created, transformed, or shared.

This is what distinguishes a data space that works only on paper from one that performs reliably in production.

 

The Four Key Dimensions of Data Quality

Although several data quality frameworks exist, four dimensions account for the vast majority of issues encountered in business and public-sector environments.

Completeness

Completeness measures whether values are missing where they should exist. For example, if a municipality publishes waste management data with missing records for several months of the year, the resulting time series is incomplete and may lead to incorrect conclusions about service performance. Completeness is validated by ensuring that all mandatory fields are populated and that no unexpected gaps exist in historical data series.

Accuracy

Accuracy measures whether recorded values correctly represent reality. Consider an agricultural cooperative that records production volumes but mixes measurement units, such as kilograms and tonnes. This seemingly minor data entry error creates an accuracy issue that may remain unnoticed until the dataset is compared with another source.

Uniqueness

Uniqueness determines whether duplicate records exist where only one should. In public administrations managing cases across multiple offices, synchronization errors often result in the same procedure being recorded twice. Without uniqueness controls, aggregated analyses overestimate actual activity.

Consistency

Consistency measures whether data values are logically coherent with one another and comply with the rules of the business domain. Examples include: A contract end date cannot precede its start date or a postal code must correspond to the province specified in the same record. Consistency issues are particularly dangerous because they often pass individual completeness and accuracy checks, only becoming visible when multiple fields are evaluated together.

 

Validating Data Through Business Rules

Data quality dimensions become operational through business rules—specific conditions that records must satisfy in order to be considered valid within a particular context.

Unlike generic format validation, business rules incorporate knowledge of the business process itself.

Examples include:

• Energy consumption datasets: a meter reading cannot be lower than the previous reading in the same time series unless the meter has been replaced.
• Human resources datasets: an employee's hiring date cannot be later than the date of their first recorded payroll.
• Municipal management datasets: every case marked as resolved must include a resolution date.
• Agricultural production datasets: declared production volume cannot exceed the registered maximum capacity of the production facility.

These rules are not universal. Every organization—and every dataset—requires its own set of business rules, ideally defined together with domain experts rather than solely by technical teams.

 

Data Quality Validation in Batch and Real-Time Processes

No todos los flujos de datos se comportan igual, y el control de calidad debe adaptarse al ritmo con el que llega la información.

Not all data flows behave in the same way, and data quality controls must adapt to the pace at which information is generated.

Data Quality in Batch Processes

When data is loaded in batches—for example, through a monthly export of public administration indicators—quality checks can be performed across the entire dataset before it is published.

This makes it possible to apply complex validation rules that require comparing records with one another, calculating aggregate statistics, or detecting anomalies based on historical trends.

A typical example is a distribution company that consolidates weekly inventory data from multiple warehouses. Before the consolidated dataset is published in the data space catalog, the batch quality process verifies that the total inventory across all warehouses matches the company's overall inventory figures.

Data Quality in Real-Time Processes

When data arrives as a continuous stream—such as IoT sensor readings or transactional events—it is not practical to wait until the entire dataset is available before validating it.

Instead, quality checks must be performed record by record or in micro-batches using rules that can be evaluated with the information available at that moment.

Typical real-time validations include:

• Valid value ranges.
• Consistency with the previous known value.
• Threshold-based anomaly detection.

For example, a facility transmitting energy consumption data in real time can automatically flag or reject any reading that exceeds a predefined variation threshold compared with the previous measurement. This prevents faulty sensor readings from contaminating the shared dataset.

The key is ensuring that both batch and streaming processes rely on the same catalog of business rules whenever possible, so that data is validated consistently regardless of how it is received.

 

Automating Data Quality Before Publishing to the Catalog

In a modern data space, data quality is managed through a dedicated data application integrated directly into the data transfer workflow, much like anonymization.

The process typically works as follows:

1. Business rules are defined for a dataset—or a family of datasets—in collaboration with the business owners responsible for the data.
2. Before the dataset is published in the federated catalog (exportable in DCAT-AP format), the data quality application evaluates all configured validation rules.
3. Records that fail validation are flagged, automatically corrected when correction rules are available, or excluded according to the organization's data quality policy.
4. A data quality report is generated and associated with the dataset. This report is available to the data provider and, if agreed, to data consumers as well.

This final step is particularly valuable from a data governance perspective. By providing a quality report alongside every published dataset, consumers can determine whether the data is suitable for their intended use before integrating it into their own systems.

Why Automate Data Quality?

Automating validation through business rules instead of relying on occasional manual reviews offers significant advantages:

• Consistency: The same validation rules are always applied in the same way, regardless of who is responsible for reviewing the data.
• Speed: Datasets can be validated and published without waiting for manual approval cycles.
• Traceability: Each dataset includes a history of which validation rules it passed or failed, providing valuable evidence in the event of disputes between providers and consumers.
• Scalability: As the number of datasets and real-time data streams grows, automated validation becomes the only practical way to maintain effective quality control.

 

Build Trust Through Data Quality

Trust within a data space is not established through governance documents alone.

It is built dataset by dataset, rule by rule, through automated controls that validate information before it is shared.

If your organization wants to publish or consume data with reliable quality guarantees, partnering with a technology provider experienced in data spaces can help you design the business rules and validation workflows best suited to your specific needs.

Ultimately, data quality is much more than a technical requirement—it is the foundation that enables organizations to share information confidently, make better decisions, and unlock the full value of data within trusted, interoperable, and scalable data spaces.