Data quality – the foundation of effective analysis

In projects, challenges rarely end with building a report, selecting the right metrics, or implementing data visualization tools. All of this only makes sense if the data we work with is reliable.

Unfortunately, the aspect of data quality is often marginalized, yet it is the one that most significantly determines whether reports will truly support decision-making or become a source of erroneous conclusions and costly mistakes.

A polished report doesn’t always mean reliable analysis

Sometimes organizations have extensive reports that look very professional in terms of visuals. Problems arise when users start to question the results. Numbers don’t match other sources, key information is missing, and data consistency raises concerns.

In such cases, it turns out that the issue doesn’t lie in the form of the report itself, but in the quality of the data it is based on. Even the best analytical tools can’t make up for incorrect, inconsistent, or outdated data.

What constitutes data quality?

In day-to-day analytical work, we evaluate data based on several core dimensions:

• Completeness – Do we have all the necessary information?
• Consistency – Is the data from different sources aligned?
• Accuracy – Is the data correct and reflective of reality?
• Timeliness – Does the data represent the current state of knowledge?

Picture 1. The four fundamental pillars of data quality

Verifying these aspects should be a standard part of every analytical project. In practice, many reporting problems stem from insufficient data verification during the preparation phase.

Data quality is not a one-time task

It’s a misconception to treat data quality as a one-off task – something that only needs to be evaluated at the beginning of a project. In reality, data constantly changes: processes are modified, source systems are updated, and business rules evolve. All of this affects the quality of the information feeding into reports.

Well-designed analytical solutions incorporate mechanisms for continuous data quality monitoring, which can be based on predefined thresholds or outlier detection. This includes automated validations during data refreshes, alert systems for signaling anomalies, and regular data integrity analysis.

What does an organization gain by prioritizing data quality?

An organization that actively ensures data quality primarily gains confidence in the accuracy of decisions based on reports. Users don’t have to wonder whether the results are correct – they can focus on interpretation. Operational errors are reduced, teams respond faster to changes (thanks to automated alerts), and can make better-informed decisions.

As a result, data analysis becomes a genuine business enabler – not just a source of historical insights, but a tool for planning, forecasting, and optimizing activities. Having high-quality data can be the starting point for projects leveraging AI & ML tools.

„Data quality is not a nice-to-have – it’s a necessity. Without consistent, complete, and up-to-date data, no metric holds value, and any report can be misleading. For analysts, key concerns include source integrity, ETL logic accuracy, and data version control. If you can’t trust your data, you can’t trust the decisions based on it—and that’s a business risk. In today’s fast-paced market environment, trust in data is the foundation of effective action.”

– Karolina Skowrońska, Data Analyst