Data Quality Review

Improved timeliness and accuracy of data through real time reporting, training, and system enhancement. Reduced administration hours by approximately 20%.

How we helped...

CI Projects were commissioned by a GovCo (Government Company) to carry out a data quality review to help achieve their strategic objectives. Our client identified that they needed good quality data to enable data-based decision making and insightful data analytics. There were concerns with the completeness, accuracy and reliability of the existing data help within their core workflow and ERP systems, therefore there was a requirement to understand the gaps in data quality and the impact of these issues.

We conducted a series of interviews with key stakeholders, and carried out a number of on-site process workshops to capture the process and data flows through the systems. The quality review included both qualitative and quantitative analysis so that we could understand and demonstrate the data quality, scale of the challenges and the risk to the organisation. We applied our Lean Six Sigma skills and methodology to carry out this review. 

When we carried out the review, we focussed on 5 key areas of data quality: Validity, Accuracy, Timeliness and Uniqueness and Completeness. Within each of these review sections we presented qualitative and quantitative data including charts and data samples. To present our findings and recommendations we used a RAG score for simplicity, with details within the report to justify the scoring. Throughout this review, we identified that there were significant challenges in relation to manual entry processing time, missing data, data entry defects and limited alignment between the data and systems.

The overall benefit was improved data quality across the organisation, including improved data accuracy through real time reporting, training, and system enhancement. We improved timeliness of data through implementation of mobile devices enabling real-time updates. We reduced administration hours by approximately 20% across the board (13 FTE). We improved ongoing management of data quality and identification of non compliance.



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