Intelligence design
Organisations that want to make better decisions need more than good intentions — they need the right data, the right infrastructure to manage it, and the analytical capability to turn it into insight. Mortar's Intelligence Design practice brings these three things together: building the data foundations, governance frameworks, and bespoke analytical models that enable organisations to understand their populations, test their options, and act with real confidence.
This is not a theoretical exercise. We embed ourselves in your data, your systems, and your decision-making context — and we build things that work in the real world, not just in a proof-of-concept environment.
An end-to-end discipline
Data audit and discovery
Understand what data you hold, where it lives, how it flows, and what constraints govern its use — before designing anything.
Infrastructure and architecture
Design the data infrastructure that connects your systems, standardises your data, and scales with your organisation.
Governance and frameworks
Develop the governance structures, data sharing agreements, and compliance documentation your organisation needs to use data responsibly and at scale.
Model design and build
Develop bespoke analytical models — calibrated to your context, validated against your data, and stress-tested against edge cases.
Scenario testing and simulation
Run structured simulations to explore how your systems and populations respond to different interventions, constraints, and conditions.
Insight communication
Translate complex outputs into clear, interpretable findings that decision-makers can act on — through dashboards, scenario reports, and facilitated sessions.
Data and analytical power
Data strategy
A clear, actionable data strategy aligned to your goals and grounded in an honest assessment of your current data maturity — a practical guide to real change, not a report that sits on a shelf.
Data architecture
Systems that bring your data together, reduce duplication, and create the single source of truth your organisation needs — working with both modern cloud infrastructure and legacy on-premise systems.
Interoperability and standards
Open standards and integration frameworks that enable your systems to connect with each other and with external partners — whether FHIR for health data, Open Referral for service directories, or custom patterns designed to your context.
Data governance
The ownership models, quality frameworks, retention policies, and information governance documentation needed to satisfy regulatory requirements and build internal confidence.
Demand forecasting
Statistical and machine-learning models that project service demand over time, accounting for seasonality, demographic change, and policy effects — supporting capacity planning and resource allocation with evidence rather than assumption.
Simulation and scenario planning
Models of how people, cases, or resources move through a system — identifying bottlenecks, testing interventions, and stress-testing plans under different assumptions — so organisations can act with confidence rather than guesswork.
The case for intelligence
Closing the knowledge gap
The gap between what organisations know about the people they serve and what they are able to do with that knowledge is one of the most significant barriers to improving public services. Intelligence design addresses this at the root.
Navigating complexity with confidence
Public services operate in environments of genuine complexity. Modelling and simulation give organisations the tools to navigate this — not by eliminating uncertainty, but by understanding it well enough to act more wisely.
A compounding strategic asset
Organisations that invest in data foundations and analytical capability build a strategic asset that compounds over time — improving their ability to respond to new challenges, adopt new technologies, and demonstrate measurable impact.