The Problem
Our non-profit client wanted to modernise and automate their existing reporting processes. This reporting was crucial for the client’s compliance requirements, but the prior reporting process was slow, manual and required a significant amount of human intervention and resources. It was also built within Excel spreadsheets with numerous pages that were prone to errors due to the labour-intensive processes and several stakeholders contributing to the piece.
Additionally, the prior reporting was not scalable and could not provide trends, a comparison of historical data, or insights outside of the current month. The data was also only collated monthly, resulting in no ability to monitor output or data quality issues throughout the month.
The Solution
The various source systems contributing to this reporting were identified, and the data was automatically ingested and collated into a Databricks warehouse.
A Power BI solution was developed that not only replicated and automated this significant set of reporting, but enabled the comparison of historical data, identified trends and provided the ability for self-service reporting. This solution also ensured that the reporting was being accessed securely in the Power BI Service and negated any data breaches that could occur through email.
As the source data refreshes daily, users can see the most recent data each day rather than waiting until the monthly reporting is completed to view it. This process also allows identification of errors in the source system, which can be amended with changes flowing through to the reporting the following day.
Business Benefit
- Heightened accuracy in reporting figures and decision-making
- Decrease in resources required to complete the reporting
- Increased productivity and focus on core business activities
- Ability to see reporting trends using historical data and identify anomalies
- An increase in the frequency of reporting with data updated daily instead of monthly
- Reporting is securely shared and accessed
- Enhanced data quality through identification and amending of errors in source systems
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