We combine passionate people with sector-specific expertise. Our approach is collaborative, creative and human. Our outputs are transparent, robust and transformative.
From problem identification, data management and data science, to visualisation, interpretation and the delivery of actionable intelligence – we work across the entire data lifecycle.
A robust strategy can unlock all the benefits your data has to offer. By harnessing emerging technology. By exploiting new techniques. By maximising value from your data estate.
Our strategy experts deliver the framework and direction for all data led initiatives to follow. Guiding you on the journey to becoming a data driven organisation.
We solve challenges through collaboration and innovation.
Group sessions, interviews and research provide our strategy experts with a comprehensive picture of your data and how you use it. We need to understand the decisions it informs (and, as importantly, those it does not) and its value to your organisation.
With these insights, we can design a data strategy that considers regulatory environment and related constraints (for example, GDPR); opportunity analysis driven by your ambitions; and an artefact review with the potential development of a Target Operating Model (TOM).
From our findings, we can create a joint roadmap to support a successful strategy rollout.
For clients who have an existing strategy, we offer a range of standalone packages to enhance the data journey. This includes cloud and AI readiness assessments; data science roadmap(s); and data and information governance models.
Target Operating Model (roles, functions, technology and investment)
For a client within the defence sector, we established a Data Science and AI Centre
of Excellence. We embedded its working practices, culture, standards, ethics and
As part of the data strategy, we’ve developed a Target Operating Model and a Front Door for data science and AI project requests. Our goal is always to build a long-term service that the client could take full control of.
Building from this success, we’re now delivering AI-CNN and machine learning for a range of projects including NLP and algorithmic test beds.
Evolution starts with exploration. Whether solving a specific problem or implementing a broader strategy, our user centric discovery process dissects your existing data solutions to reveal possible barriers and identify untapped potential.
Both are essential to plotting the best course forward for data-led success.
Our discovery phase can be focussed on a specific problem or broader business goals.
We have two separate approaches to meet differing needs:
We can apply sector dependant requirements such as the Government Digital Service (GDS) – a public sector methodology comprising user research; service design; data architecture and prototyping.
Or, if we’re informing a specific decision or particular problem, we can collaborate to identify, define and agree on the specific questions that require answering. We then begin the discovery, understanding your current data and technical solutions to map the way forward.
A service aligned to and meeting user needs
Service concepts (an MVP or Alpha) with which to test and learn
Investment efficiency on a specific service and/or project
Guidance on how to proceed and procure, build and deliver a service/project
For East and North Hertfordshire CCG, we were asked to explore an approach to
population health analytics that would enable more proactive healthcare.
There were two linked purposes: to use data at a population level to inform service planning and design; and to leverage person-level data to inform direct care.
We embarked on a six-week phase of user research. We wanted to understand exactly how they wished to consume data and what, ultimately, they wanted to do with it.
Our research was broad enough to encompass their current data assets, environment, and how they could both be developed. We also explored predictive models with the aim of being able to plan model development for local purposes.
Our discovery outputs were comprehensive. We provided: the build phase requirements and key objectives, such as achieving increased usage of a linked-dataset for population health monitoring. Self-service access to underlying data. Robust and updated long-term governance agreements. And cloud-based platform design that’s scalable, extensible and interoperable, incorporating the latest security and governance approaches
A client in the defence sector is responsible for oversight and delivery of
equipment assets around the world. If an item becomes obsolete or isn’t working,
it’s either disposed of or sent to storage.
The client had a question: they wanted to know if their systems could be linked together to understand the asset journey in more detail. To help answer it, we provided insight and advice using a data science discovery.
Our exploration focused on a number of areas: improved approaches to enable better analysis of data and insight. Mapping the end-to-end process of disposals, spanning pan-defence systems and teams for the first time. Building a backlog of disposal hypotheses relating to inefficiencies in the process and areas of fraud risk. And, finally, using a data-driven approach to test those hypotheses and propose recommendations based on the results of machine learning analysis.
The discovery outcomes highlighted the areas within the previous process that were wasteful of both time in disposing of equipment, and through the cost of renting equipment storage space. With this knowledge, we were able to provide visibility around the disposal unit and movement of assets, as well as a process map to document best practice for asset disposal.
Your data strategy and user needs drive our governance-first design approach. We work to understand the constraints, regulations and ethical consequences of your desired solution.
Then we translate your strategy into technical designs that are secure, robust and scalable – building a solid foundation for future delivery.
We start with a clear problem to be solved or solution to be found. Then, working
closely with you, we ensure two things: that your governance requirements are met;
and the ethics of deriving value from your data are considered.
At the heart of this collaboration is a mix of group sessions, interviews and research. From this deep dive, we’ll put together a business case and implementation roadmap – spanning from skills, capacity development and technical requirements, right through to investment.
If it supports the solution, we can also take advantage of our formal accredited partnerships with organisations such as Microsoft, Amazon and Tableau.
Understanding and developing:
Design, build and implementation enabling:
Understanding and bringing together:
Delivering value including:
DHSC required a new medical examiners platform to collect, secure, search and
analyse person-identifiable data on all deaths in the NHS in England & Wales. The
platform was to be a key part of the Department’s long-term plans to reform how
death certifications work in England and Wales.
Working to GDS standards, we took the non-technical client team through an end-to-end service design process. Our solution started with data strategy and discovery, before confirming and articulating the data architecture, governance and infrastructure designs. Finally, we delivered Alpha and Beta builds from our data engineering and interaction teams.
From an architecture standpoint, MedEx is a PaaS web application with an API application tier and an Azure Cosmos DB backend. The agreed n-tier architecture compromises technology, CI/CD and patterns that are leading-edge within the NHS.
The web-based production environment is now used by hospitals across the UK to input real-world case data and interactions with clinicians and the bereaved. It’ a 24/7 service with an SLA aligned to a range of stakeholders, meeting user need and commercial constraints.
As the data is highly sensitive, data governance was essential. Covering the deceased, their next of kin, medical team and other interested parties relevant to the investigations of the Medical Examiner, a comprehensive Data Protection Impact Assessment (DPIA) supported by robust data security was essential.
National regulatory requirements were reviewed and assessed constantly to ensure compliance, along with best practice and GDS protocols. Using encryption, database auditing and other measures, data was safeguarded in support of organisational security commitments and compliance requirements defined by NHS Digital.
As your strategy is deployed, higher volumes of data are produced from an increasing number of sources.
We create the processes, infrastructure and pipelines that will bring that data together and prepare it for data science and AI.
Data engineering typically begins at the point where the artifacts of the solution
design are known, (such as the high and low-level architecture, data model, logical
schemas and where the data resides). The rules that govern how data can be processed
and the environments and toolsets where data will reside are established.
We then build the foundational data infrastructure and tooling to integrate, consolidate and make the data suitable for analysis in a data science project. We do this using data pipelines, Extract Transform Load (ETL) scripts, data cleansing/profiling, and data warehouses or data lakes. We then apply reporting and visualisation tooling as required.
Typically, our approach follows an Agile development methodology to accelerate the process. This enables us to deliver functionality and capabilities, increase user engagement, and be responsive to changing sprint requirements with greater efficiency. Depending on your preference, our projects also follow CI/CD DevOps processes.
We strive to adopt a technology agnostic approach. The technology used is chosen as part of a solution design and often forms the input to a specific data engineering project. Our approach will typically comprise:
Bespoke data platforms
Database engineering including:
Verification & validation
Data-centric software engineering
Foundations enabling interrogation and analysis of data
Delivering value downstream via high quality data engineering
Providing infrastructure required to become a data-driven organisation
A resilient, secure and performant environment
The DVLA are in the process of modernising their existing systems. This involves
migrating mainframes, bespoke applications and other technology to a modern
Using an Agile methodology, our solution supplements and up-skills their existing team and prototype build. To achieve this, we called on our experienced Azure engineers with technical specialists in the Microsoft analytics suite, working in the existing Azure tenant, to build a secure MI/BI data engineering platform.
The conceptual design involved a number of components. Event Hub was used to ingest data from a variety of sources (JSON, .CSV, RDBMS etc.). Stream analytics were used to process and store data in Data Factory and Azure SQL. And analysis services allowed for ultimate display in multiple potential reporting suites such as Power BI.
The platform went live in 2020. Its first source system involved the surfacing of data from tachometers used on passenger carrying and goods vehicles. This represented a significant shift from taking snapshots at set intervals to real-time, event-based processing – this enabled faster visibility of data and a higher degree of reporting accuracy at any given time.
Our data science services break down the silos of internal and external data sets to deliver more powerful insights, enabling smarter decisions. Expertise we can transfer to you – growing your capability and confidence.
We achieve this by helping you frame a data related problem statement. We then apply the latest predictive modelling, AI techniques and statistical approaches to deliver intelligent, actionable outputs for you and your organisation.
We use AI techniques such as machine learning and neural networks to offer a clearer
and quicker way to solve problems.
Our deep understanding of analytical methodologies allows us to design a solution that fits your needs. Data science and AI approaches are different to traditional mathematical and statistical techniques. They use the power of technology to deliver faster, more accurate and less obvious answers to questions – enabling data-driven decision making.
Our expert team of data scientists work collaboratively to enable seamless, ongoing knowledge transfer whether on site or remote. Our mixed-delivery model combines permanent staff and carefully selected, vetted associates. This ensures access to a large pool of technically accredited experts, and a broad range of skills and experience to call on.
Typically, we combine an Agile methodology to scope and agree packages of work, with rapid turnaround of deliverables over several iterations.
Delivery Content/Service Design
Building confidence, capability, capacity and knowledge
Becoming a proactive vs reactive organisation
Providing insight to inform clear questions and outcome
Delivering robust and explainable methodologies
Delivering clear and actionable findings
The CQC held vast quantities of notifications that were inconsistently categorised,
variably complete, and largely free-text statutory notification returns.
Traditionally, they were manually interpreted by inspectors who decided on an
appropriate risk response.
CQC was seeking a decision support tool using artificial intelligence (AI) to help their inspectors sift, present and link information to support decision making. Success would be measured against four key indicators: increased efficiency, assessable data, tacit knowledge leveraged and increased consistency.
The project involved processing a significant amount of unstructured data to apply a range of natural language processing (NLP) and textual analytical techniques. The processed output notification data contained a number of structured, classified risk and temporal aspects that provided meaning, along with risk or safeguarding impacts. Powerful insights that could inform inspector decisions and help them manage and prioritise their workload.
Outcomes included: previously unknown untapped information being made available to inspectors for review; consistency of processing unstructured data and analysing across boundaries, provider organisations and types; automatic processing of manual and time intensive tasks; and, finally, providing a methodology which allows CQC to improve organisational memory and to support inspectorate decision making.
We’ve been working to design improved development and business processes, data
science methodologies and governance frameworks for the existing data
As an independent analytical function, we provide real-time analysis about infection outbreaks through the provision of data science and data engineering teams.
Data in isolation has no power. It’s how the user interacts with the data that determines how impactful and effective it will be.
Combining user needs and experience design, we provide data input, visualisation and reporting services for your organisation. Giving your data the context it needs to inform decisions and drive progress.
We build intuitive frontends that allow users to easily collect, secure, analyse and
understand complex data.
Our product experts help you to explore your ideas – from capturing needs and wireframing, through to a fully embedded, live solution. We can also create static reports, presenting a clear, intuitive and faithful representation of our analysis.
Our experience, gained from years of working across multiple sectors, help us to interpret the outputs of analysis. We tease out the learning and work with you to identify the best path forward.
1. Understanding need
2. Requirements exploration and definition
3. Service design
5. User testing
7. Service deployment
8. Support & Knowledge Transfer
Microsoft Power BI
Facilitating better, faster and lower cost of decisions
Making data transparent and accessible
Enabling self-serve business intelligence
Interactive tools and services which appeal to users
Driving organisation wide success
GIRFT is a National NHSI programme run in partnership with the Royal National
Orthopaedic Hospital. It aims to improve the quality of care and investment in the
NHS by reducing unwarranted variation.
We worked with multiple specialties and clinical leads, supporting the programme across more than 30 health related services. Our role was developing specialist analytics data packs. Each one contained a suite of data visualisations showcasing variation across a specific clinical area.
Alongside data packs, we delivered national specialty reports working with the GIRFT team and national clinical leads. Each report shared findings from dozens of hospital visits and interrogated the data to demonstrate anecdotal findings and drivers of variation in outcomes and efficiency.
Based on the findings, we worked with the National Clinical Lead to develop potential service changes. These included: clinical practice changes through education and evidence, service model changes, and organisation and service structural changes.
We assisted healthcare technology provider, HasTech, with implementing their PAMMS
and Covid-19 data into a new Azure cloud data platform, as well as developing
Covid-19 intelligence dashboards in Power BI.
Our data engineering and interaction team developed a pipeline to ingest National Food Standards Agency (NFSA) ratings data to Azure, via API, using Data Lake, Data Factory and Azure SQL.
We then built three new Covid-19 intelligence dashboards in Power BI, re-designing 12 prototype reports. We supported the ingest of NHS England’s (NHSE) new Capacity Tracker data, providing local authorities across Greater London with reporting not currently available.
The outcomes were far reaching and impactful. We harmonised data reporting from 32 local authorities and other external sources, such as the Care Quality Commission (CQC). We provided users with a clean, interactive display to analyse Covid-19 information in care homes. And we future-proofed the collection, collation and display of information.