Optimization and development of reports as part of the maintenance process
23 May 2025
Data analysis is a continuous process that doesn’t end with the implementation of a dashboard visualizing the data. Organizational needs evolve, new data sources emerge, metrics are modified, and data structures change.
That’s why systematic report maintenance and an iterative approach to analysis are key to keeping them current, consistent, and valuable for business.
Today’s report, tomorrow’s needs
What is crucial today for the board, operations team, or sales department may no longer answer tomorrow’s business questions. Reports designed for a specific data structure may become incompatible with new versions of ERP, CRM, or e-commerce platforms. As the business context shifts, so does the analytical logic, expected visualizations, and datasets.
That’s why at 3Soft, we offer maintenance and development of analytical solutions right from the implementation stage as an integral part of the entire reporting system lifecycle.
What does report maintenance include?
Maintenance means much more than handling failures. It also includes: proactive data analysis, systematic report validation, continuous adaptation to changes in source systems, and functionality development in response to new user needs. This includes, among other things:
- Monitoring data availability and quality,
- Validating KPIs and visualizations, adapting reports to source system changes,
- Managing permissions and access to reports,
- Expanding reports with new data sources,
- Handling client tickets, including those related to analysis.
Data quality matters – a core element of maintenance
You can’t talk about effective report maintenance without addressing data quality. Even the best-designed report won’t serve its purpose if the source data is incomplete, inconsistent, or incorrect.
Report maintenance also involves ongoing evaluation of data quality, identifying anomalies, and responding swiftly to problems. We covered this topic more extensively in the article: “Data quality – the foundation of effective analysis”.
Respond before the problem escalates
As part of the maintenance process, it’s worth implementing an alert system and automatic notifications to flag irregularities in IT infrastructure and data, including missing information. The principles of how alerts work are detailed in the article: “Alerts in reports – how data can report issues itself”.
Maintenance is not just about control – it’s also about growth
A well-designed maintenance process involves not only reacting to issues but also systematically developing the reporting system. This can mean expanding reports with new data sources, enhancing them with additional indicators, or optimizing performance.
Reporting must keep pace with the organization’s development and strategy.
Picture 1. Report development in response to changing needs
For one of our clients, we expanded a sales report to include data from Meta’s advertising system (Facebook and Instagram). This allowed for real-time correlation between active campaigns and sales results.
That’s why data integration, flexible report architecture, and collaboration with business users are crucial. Examples of such implementations are described in the article: “Combining data from different sources – how to gain a complete picture”.
Why is it worth it?
Systematic maintenance and development of reports directly impact the quality of business decisions. Users can be confident they are working with current, complete data, and the organization can respond faster to market changes.
Just as importantly, analytical teams are freed from reactive tasks and can focus on strategic activities: data exploration, KPI development, predictive analysis, or process automation.
„Reports don’t stop being useful and reliable simply because something breaks — but because businesses change. That’s why it’s crucial not only to build analyses but also to systematically maintain and develop them. Only then can reporting keep up with business reality instead of delaying it”
– Karolina Skowrońska, Data Analyst