Within Adult Social Care, our data teams produce dashboards and reports that provide insight into performance and demand. These tools allow practitioners, managers, and Heads of Service to see patterns, identify areas for improvement, and make evidence-based decisions.

Dashboards consolidate information from our case management system, Mosaic, and present it in a way that highlights key metrics. For example, dashboards can show caseload volumes, safeguarding activity, timeliness of assessments, and care package costs. We have two main business intelligence tools.

• ASC Operational, which is focused on service pressures for each of the service areas; Neighbourhoods, Mental Health and Learning Disabilities. It also contains information to help respond to emergencies.
• ASC Finance, which is focused on budget monitoring, service details provider information.

For access to any of these we require a signed Mosaic Acceptable Use Policy (AUP) and an up-to-date DBS. Please contact [email protected].

There is also ASC Safeguarding, an application specific to our safeguarding responsibilities. Access to this is limited and is managed on a case-by-case basis.

However, dashboards and reports are only as accurate as the data entered into the system. Errors, omissions, or assumptions in source data can distort reporting, leading to incorrect conclusions and potentially poor decision-making. For example, a workflow left open when the work has been completed can result in inaccurate pressure reporting.

It is therefore critical to correct data at source, directly within Mosaic, rather than attempting to fix errors only in reports. Updating the original record ensures that everyone accessing the system sees accurate information and that dashboards, national returns, and audits reflect reality. Practitioners should ensure that data within their own work is accurate and up-to-date. Managers should monitor accurate data reporting and ensure that issues are corrected at the source.

Introducing feedback loops further strengthens data quality. Feedback loops involve reviewing reports, identifying anomalies or gaps, and communicating these back to practitioners for correction and clarification. Over time, this cycle improves both data accuracy and recording practices. Feedback loops can take many forms, including:

• Regular data quality reviews by managers,
• Targeted guidance and training for staff on accurate recording,
• Peer reviews and team-level discussions about trends and errors.