Learning that leads to action

Our approach to data analysis and reporting

We believe that meaningful improvement comes from helping people see their work differently. We bring together evidence, professional experience and organisational context to help services understand practice more clearly and identify practical opportunities for improvement.

Technology, including responsibly used AI, strengthens our analysis. Professional thinking and judgement guide our conclusions, with privacy, transparency and accountability underpinning the work.

Understanding services in context

Learning information can offer a valuable view of how people understand their work, where confidence is strong, where knowledge is uncertain, and which questions deserve further exploration. Its value grows when considered alongside professional experience, the service’s circumstances and the evidence relevant to its work.

We bring these perspectives together to help organisations recognise strengths, explore patterns and identify practical opportunities for improvement. We consider what the information can reasonably tell us and where discussion or further evidence is needed. Learning results provide a starting point for understanding; any conclusions about practice need appropriate context and scrutiny.

Respecting clinical time

Clinical and operational time is valuable. We respect the time it takes to read and use our findings. Our work should help services make progress without creating unnecessary demands on people delivering care.

We put the main findings and priorities first, use clear language, and keep recommendations focused on the service’s circumstances.

We judge the value of a report by whether it helps people understand, discuss and act on the findings. The time needed to read and use it is part of that judgement.

Professional thinking and judgement

Our recommendations are developed through evidence, professional expertise, reflective practice, and an understanding of organisational context. We check the basis for our findings, consider alternative explanations, and clarify the limits of the analysis.

Technology should strengthen professional thinking and judgement, not replace it. Used responsibly, AI can help us bring information together, identify themes and explore relationships efficiently. We use it within defined safeguards, with checked anonymous information and human review. Identifiable and pseudonymised learner records are excluded from AI analysis.

Trust in how information is used

People should be able to trust how their information contributes to learning. We protect personal information, limit access, and use it for agreed purposes. Before information is annonimised or used for AI analysis, we check the risk of identifying people through individual details, combinations of information, or links to other records.

Our analytical work supports organisational learning and improvement. Our AI workflow does not profile or make decisions about identifiable staff or learners. We explain how information is handled and make our governance arrangements available.

Data Protection and Responsible AI

  • Privacy Notice

    Explains to learners, client contacts and website visitors how we use personal information and how you can exercise your privacy rights.

    The notice describes the information collected, its sources, purposes and lawful bases, together with sharing, overseas processing, security and retention arrangements.

    Open

  • Responsible AI Policy

    Explains how we use AI responsibly to support analysis while protecting confidentiality and retaining human oversight.

    The policy sets out permitted uses of AI, the checks required before information is submitted, and the safeguards applied to platforms and outputs.

    Open

  • Terms of Service

    Explains what services can expect from FrontFoot’s e-learning service and the terms applying to their purchase.

    The terms cover service delivery, support, payment, ownership of course materials, and client responsibilities. They explain how learner licences are counted, restrictions on transferring licences, and how flexible allocations work where included in the proposal.

    Open

  • DPIA and Controls

    Provides DPOs and information governance teams with a detailed assessment of FrontFoot’s data-handling risks and the controls used to manage them.

    The document covers secure preparation, anonymisation, verification, approved platforms, international processing, retention, and incident response. It also describes proportionate records, emergency access and recovery arrangements, and the checks applied to analytical reports. It supports client assurance and any assessment required by the client organisation.

    Supplied with proposal.

  • Data Processing Schedule

    Establishes the agreed responsibilities and safeguards for our handling of personal learner information on an organisation’s behalf.

    The Schedule defines the information processed, its purposes, authorised providers and overseas processing arrangements. It covers confidentiality, security, client instructions, individual rights, breach notification, anonymisation and deletion.

    Service-specific versions supplied directly.