What is data quality?

Data quality describes the degree to which data meets the requirements of its intended use, whether for analysis, operational processes, or strategic decision-making. It determines how accurate, complete, consistent, up-to-date, and fit for purpose a dataset is—and is therefore a key prerequisite for reliable business intelligence and data-driven corporate management. Bissantz ensures that companies work with reliable data and can make well-informed decisions.

Data quality refers to the degree to which data is suitable, accurate, and reliable for a specific purpose. It is assessed based on various quality characteristics such as accuracy, completeness, timeliness, consistency, and uniqueness. High data quality ensures that data can be processed and analyzed reliably and used as a basis for well-founded decisions.

Data quality is not merely a technical detail; it is a key prerequisite for business success. Reliable, up-to-date, and complete data enables companies to generate sound insights and build reliable automated processes. In an era of business intelligence, artificial intelligence (AI), and data-driven decision-making, the quality of the underlying data is critical.

A high standard of data quality means that data must not only be technically correct but also understandable, relevant, and usable in the right context. Ideally, data should support processes rather than hinder them. Companies that recognize this invest systematically in capturing, validating, cleansing, and continuously monitoring their data.

Data quality in a company means that data is fit for its specific purpose: for month-end closing, sales planning, or an AI project. A dataset is never inherently good or bad; its quality always depends on the requirements of its users. In the literature, this principle is known as “fitness for use.”

Data quality goes beyond technical correctness. Data must also be consistently defined, understandable, and usable in the right context. A revenue figure is of little use if sales and controlling interpret it differently – for example, as gross revenue versus net revenue.

In business intelligence and controlling, data quality determines whether reports inspire confidence or trigger discussions about the numbers. Data quality is therefore not a one-time project but an ongoing responsibility shared by business functions and IT.

Data quality is the measurable outcome that data integrity and data governance make possible.

Companies with high data quality not only work more efficiently but can also respond more quickly to market changes because they have access to reliable data.

Poor data quality affects:

Data quality is generally measured across six core dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness. Each dimension can be assessed using its own verification question and set of rules.

What other criteria are relevant?

In addition to the core dimensions, other criteria are important, particularly for use by business functions. The ISO/IEC 25012 standard defines a total of 15 data quality characteristics.

• Relevance: The data answers the questions for which it was collected.
• Understandability: Metrics and fields are clearly defined and documented.
• Provenance transparency: Every figure can be traced back to its source (data lineage).
• Accessibility: Authorized users can access the data on time and without unnecessary effort.

What are the characteristics of good data quality in everyday use?

Good data quality is evident when people no longer debate the numbers themselves but focus on what they mean. Typical characteristics include:

• Reports from different departments show the same values for the same metric.
• Controllers no longer manually correct figures in Excel before publishing them.
• Anomalies in reports have business-related causes, not technical ones.
• Business functions know who is responsible for their data and report errors at the source.

Data quality is assessed by defining verification rules for each dimension, measuring the current state using metrics, and comparing the results with a target value. Without measurement, data quality remains a matter of assumption: according to Gartner, 59 percent of organizations do not measure their data quality.

What steps are involved in an assessment?

• Identify critical data: Which data feeds into management metrics, planning, or AI applications? This is where verification begins.
• Define verification rules: Business functions and IT define what “correct” means for each dimension, such as mandatory fields, value ranges, or reconciliation rules between systems.
• Measure the current state: Data profiling reveals completion rates, value distributions, duplicates, and formatting errors.
• Define thresholds: A target value is defined for each metric, such as at least 98 percent of mandatory fields being populated.
• Monitor continuously: Metrics are checked with every data load and made visible in a data quality report.

Which metrics are suitable for measuring data quality?

Thresholds depend on the intended purpose. For consolidated financial statements, a reconciliation difference of zero is mandatory; for a marketing trend analysis, lower accuracy is often sufficient.

Poor data quality costs money, time, and trust. A Gartner study from 2020 estimates the cost of poor data quality at an average of at least $12.9 million per organization per year (Gartner). The figure is based on estimates for large companies and should be understood as an order of magnitude.

The later an error is detected, the higher the cost. The so-called 1-10-100 rule describes this effect: an error that costs €1 to correct during data entry can cost around €10 when corrected later and around €100 when it results in a bad decision. Checks at the source and during data integration are therefore the most cost-effective.

Data quality can only be improved sustainably when organizational and technical measures work together: clear responsibilities, consistent definitions, and automated checks in data integration. A one-time cleansing exercise removes symptoms but not the underlying causes.

Organizational measures

• Assign a data owner to each data domain, such as sales for customer data and procurement for supplier data.
• Define metrics in a shared glossary so that “revenue” means the same thing in every report.
• Establish data entry rules and train employees accordingly.
• Have errors corrected at the source instead of overwriting them in reports.

Technical measures

• Validate entries in source systems, for example through mandatory fields and drop-down lists.
• Build verification rules into ETL processes so that faulty datasets are detected before loading.
• Consolidate data in a central data foundation that serves as a single source of truth.
• Detect and merge duplicates using matching rules.
• Measure and report data quality metrics with every data load.

BI tools improve data quality because they make errors visible where business users can recognize them: in reports, variance analyses, and drill-downs. A controller can spot an implausible monthly figure faster than any generic verification rule.

How can BI tools improve data quality?

The most common mistake is treating data quality as a one-time IT project. Without ongoing checks and clear responsibilities, old errors quickly return.

Data quality and artificial intelligence are connected in two ways: AI needs validated data, and AI can help identify data errors. Both aspects are becoming increasingly important as AI is used in controlling.

Why does AI require high data quality?

AI models inherit errors from their data and pass them on as seemingly well-founded results. The principle is known as “garbage in, garbage out.” An AI forecast based on incomplete actual data may appear plausible and still be wrong.

Language models introduce another layer: when a large language model accesses documents through Retrieval-Augmented Generation (RAG), the timeliness and uniqueness of the texts also matter. Outdated policies or duplicate versions can lead to contradictory answers.

Regulators are also imposing requirements: Article 10 of the European AI Act requires training, validation, and test data for high-risk AI systems to be relevant to their intended purpose, sufficiently representative, and, to the greatest extent possible, free of errors and complete (Article 10 of the EU AI Act).

How does AI improve data quality?

AI identifies patterns that fixed verification rules may overlook. Typical use cases include:

• Detecting anomalies and implausible values in large datasets
• Identifying duplicates with different spellings
• Classifying unstructured information, such as from conversation transcripts
• Suggesting mappings and cleansing steps when connecting new sources

Bissantz’s AI-powered data integration automatically detects format inconsistencies, typos, and implausible values, suggests cleansing measures, and reduces manual verification loops (AI for data integration). The business evaluation of these suggestions remains with the human, supported by explainable AI.

The key trends are moving data quality upstream into data integration and making it a prerequisite for AI. Instead of looking for errors in finished reports, companies are increasingly checking data automatically and continuously.

A mid-sized manufacturer reports its monthly revenue from both the ERP system and the BI system. The figures regularly differ, and controlling spends days reconciling them in Excel before every close. Management has begun to question the sales planning figures.

The analysis identifies three causes: after the CRM system was connected, duplicate customers exist; new products have not been assigned to a product group; and credit notes are posted at different times in the two systems.

The result: Causes are fixed in the source system instead of being overwritten in the report. Controlling comments on remaining anomalies directly in DeltaMaster, and management can focus on actions rather than debating the numbers.

Bissantz relies on the highest possible data quality as the foundation for reliable analysis and effective business management. After all, intelligent BI is only as good as the data it is based on. For this reason, DeltaMaster consistently relies on structured, validated, and integrated data models, for example from data warehouses or ERP systems. This ensures that all analyses are consistent, traceable, and correct – regardless of how complex the underlying company data may be.

Bissantz BI solutions actively support data quality by:

Data quality is a business-critical factor, particularly in integrated planning, forecasting, and AI-powered analysis. That is why Bissantz does not treat data validation as a preliminary step but as an integral part of the entire analysis and management process – from import and modeling through to decision-making.

DeltaMaster thinks along: Faulty, incomplete, or contradictory data is not processed silently but deliberately made visible – visually, linguistically, and through system support. This turns potentially misleading information into reliable knowledge – and transforms a high standard of data quality into a tangible competitive advantage.

Data quality is the silent foundation of all data-driven activities: only when data is accurate, complete, consistent, and up to date can analyses, reports, and AI applications deliver reliable results. It is not a one-time project but an ongoing process, embedded in data models, ETL processes, and an established data culture. With DeltaMaster and its consulting expertise, Bissantz makes data quality an integral part of corporate management—visible, manageable, and sustainably secured.

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