Data quality management helps companies make decisions based on reliable information instead of inconsistent reports, erroneous analyses, and data scattered across multiple systems. To build trust in data, organizations must streamline processes, standardize metric definitions, and implement solutions that ensure control over data at every stage of its flow. In the rest of this article, we show what problems most commonly reduce data quality and how to effectively eliminate them.
What is data quality?
Data quality determines whether data is actually suitable for use in analysis, reporting, and business decision-making. The problem today is not the lack of data itself, but that organizations often work with information that is outdated, inconsistent, or interpreted differently by different departments.
As a result, the same metric can have one value in the ERP system, another in CRM, and yet another in the BI dashboard. This is precisely why data quality becomes the foundation of modern analytics and Business Intelligence environments.
High-quality data:
- reflects the actual state of the business,
- contains no gaps or duplicates,
- has consistent definitions across different systems,
- is current and available at the right time,
- can be safely used in data analysis and reporting.
The most important dimensions of data quality include:
- accuracy,
- completeness,
- consistency,
- timeliness,
- uniqueness.
In Business Intelligence environments, data quality is critical because even the most advanced Qlik dashboards or analytical models will not deliver reliable results if the input data is inconsistent or incorrect. That is why companies are increasingly using data integration and quality control solutions such as Talend, which enable automatic error detection, data standardization, and quality monitoring throughout the analytical process.
Read also: Data governance and data quality – how to build a solid foundation of trust in organizational data
Why is data quality a problem in many companies?
Data quality problems most often do not stem from a lack of technology, but from information chaos and inconsistent processes within the organization. Data is scattered across multiple systems, different departments use their own metric definitions, and some information still enters analyses manually – most often through Excel or imports performed without quality control.
As a result, companies work with data that is difficult to trust. The same customer may appear multiple times in different systems, the sales report may show different values than the financial report, and the BI dashboard ceases to be a reliable source of information for management.
Below we describe the most common causes of data quality problems.
Scattered data sources
In many companies, data is scattered across ERP systems, CRM, e-commerce, Excel spreadsheets, and local databases. Each department uses different information sources, leading to inconsistent reports and data analysis problems.
The same metric can have different values depending on the system or department. Data duplicates, errors, and problems with information currency also appear.
That is why organizations are increasingly implementing Business Intelligence solutions and data integration tools such as Talend and Qlik, which enable data integration and the construction of a single coherent reporting environment.
Different definitions of the same metrics
One of the most common problems in reporting is the lack of a common data language. The same metric can be calculated differently by sales, marketing, finance, or controlling. As a result, different reports show different values, even though they are based on the same source data.
Such chaos makes it difficult to analyze results and undermines the credibility of dashboards and BI reports. To prevent this, organizations create central KPI definitions and common data models in Business Intelligence environments.
Manual processes and Excel
Manual copying of data between systems is still a common source of errors in organizations. Importing from Excel, manually updating reports, or local files created by different departments quickly lead to duplicates, data gaps, and version control problems.
The more manual operations, the greater the risk that reports will cease to reflect the actual business situation. This becomes particularly problematic with a larger number of users, systems, and data, where even a minor error can affect analysis results and business decisions.
Lack of data owners
Data quality problems often stem from a lack of clearly defined responsibility for data in the organization. When it is unclear who is responsible for the correctness, currency, and consistency of information, errors go unnoticed and over time begin to affect reporting and business analyses.
This is especially true in environments where data comes from multiple systems and departments. Without roles such as Data Owner or Data Steward, it is difficult to maintain uniform standards and effectively manage data quality across the entire organization.
What are the most important dimensions of data quality?
Data quality can be assessed through specific dimensions that show whether information is suitable for use in reporting, data analysis, and business processes. It is these areas that determine whether an organization can trust its dashboards, KPIs, and analytical models.
In Business Intelligence and AI environments, each dimension of data quality directly affects the credibility of analyses and the effectiveness of decisions made.
Data accuracy
Data accuracy determines whether information actually reflects the business state. If data is incorrect or inconsistent with reality, reports and analyses lose their value. The problem can affect both simple customer contact data and financial metrics or sales data.
Errors most often appear during manual data entry, system migrations, or integration of multiple information sources. In Business Intelligence environments, even minor inaccuracies can lead to incorrect forecasts and business decisions. That is why organizations are increasingly implementing automatic data validation mechanisms and data integration tools that support information quality control.
Data completeness
Data completeness means that information contains all the required values needed for reporting and analysis. Missing data makes it difficult to analyze results, reduces the quality of BI dashboards, and can lead to incorrect conclusions.
The problem often affects CRM systems, sales forms, or data from multiple sources. If some records do not contain key information, the organization loses the ability to fully analyze customers, processes, or financial results.
In analytical environments, data completeness is particularly important in reporting automation and AI projects, which require large and consistent datasets for models to function correctly.
Data consistency
Data consistency determines whether the same information has identical values in different systems and reports. If sales reports different results than finance or controlling, the organization quickly loses trust in the data.
Consistency problems most often appear in companies using multiple ERP systems, CRM, or local databases. Each source may have different data definitions, formats, or methods of updating information.
That is why data integration and building a central analytical model become so important. Business Intelligence platforms such as Qlik help create a unified reporting environment based on common data definitions and KPIs.
Data timeliness
Data timeliness shows whether information is available at the right time and reflects the current business situation. Even correct data loses value if a report is based on outdated information.
This problem often appears with manual report updates or delays in data synchronization between systems. As a result, the organization makes decisions based on data from several hours, days, or even weeks ago.
Modern Business Intelligence environments increasingly use automatic data integration and real-time reporting. This allows dashboards and analyses to show a current picture of sales, finance, or operational processes.
Data uniqueness
Data uniqueness means the absence of unwanted duplicates in systems and reports. Duplicate customer, order, or product records make data analysis difficult and can lead to incorrect business results.
Duplicates most often appear during manual data import, integration of multiple systems, or lack of uniform information management standards. The problem is particularly visible in CRM systems and sales environments.
Maintaining data uniqueness is critical for Business Intelligence because even single duplicates can affect KPIs, forecasts, and AI models. That is why organizations are increasingly using data governance processes and tools that automatically detect duplicate records.
How to build trust in data within an organization?
An organization must be certain that data is consistent, current, and correctly interpreted by all departments. Without this, business analytics ceases to support decisions, and users begin to question the credibility of reports.
To effectively build trust in data, companies should address several key areas:
- common KPI and data definitions across the entire organization,
- data integration from different systems,
- automatic data quality control,
- clearly defined responsibility for data,
- transparency of information origin and flow,
- employee education in data analysis.
A key role is played here by data governance, which means organizing data management rules and assigning responsibility for their quality. More and more organizations are creating central data models and reporting standards that eliminate differences between departments.
Data transparency is also important, and users should know:
- where the data comes from,
- which systems are the source of truth,
- how data is processed,
- why a report shows specific values.
Data integration solutions and data lineage features available in tools such as Talend are of great importance in this area.
Building trust in data is also a matter of organizational culture. Data-driven companies promote fact-based decision-making and develop employees’ analytical competencies. This is particularly important in the context of AI, where data quality directly affects the effectiveness of models and predictive analyses.
What role does Business Intelligence play in data quality management?
Business Intelligence plays a key role in data quality management because it allows integration of information from multiple systems, standardization of reporting, and faster detection of data problems. A well-designed BI environment not only presents data in dashboards, but also helps control its consistency, completeness, and timeliness.
Analytical platforms such as Qlik are particularly important here, enabling data analysis in a single reporting environment and rapid identification of problems affecting the credibility of analyses.
Data integration and ETL solutions such as Talend are also playing an increasingly important role, automating data flow, controlling their quality, and helping maintain information consistency between systems.
This is precisely why data quality management is increasingly becoming an integral part of Business Intelligence projects. Companies no longer expect only dashboards, but a complete data environment that supports reporting, analytics, and AI development.
This approach is implemented by Hogart Business Intelligence, among others, specializing in BI implementations, data integration, and data governance and data quality projects. By combining business and technological competencies, organizations can build analytical environments that users genuinely trust.