Made Tech partnered with the Met Office to embed a more data-driven approach to product development. Working together as a single Data & Insights team, the engagement focused on helping the organisation turn data into actionable insights, build confidence in evidence-based decision-making and create a culture where every product change could be linked to measurable value.

The challenge

The Met Office was not short of data. User feedback, customer reviews, behavioural analytics and product performance metrics were all available across the organisation. The challenge was ensuring that this information could be consistently used to support product decisions.

Like many organisations undergoing digital transformation, the opportunity was as much cultural as technical. Product teams, stakeholders and specialists all needed a shared understanding of how data could inform decision-making. Without that alignment, valuable insights risked remaining isolated within individual teams rather than shaping the wider direction of products and services.

At the same time, the Met Office wanted to deepen its understanding of users. Beyond simply measuring engagement, the organisation was committed to helping people better understand weather and climate information, enabling them to make more informed decisions in their everyday lives. To achieve this, product teams needed clearer evidence about what users valued, how they behaved and where improvements would have the greatest impact.

Key learnings

A data-driven transformation is not primarily a technology challenge. It is a cultural one. Success depends on creating shared understanding, encouraging collaboration and ensuring evidence is accessible and meaningful to everyone involved in product delivery.

The Met Office’s commitment to understanding and empowering its users provided a strong foundation for building a more evidence-led approach to product development.

The approach

Made Tech’s approach centred on a simple principle: every product decision should be linked to measurable value.

Achieving this required more than implementing tools and dashboards. The joint Data & Insights team worked closely with stakeholders across the organisation to build engagement with data and develop confidence in evidence-based decision-making. Information was made accessible through a range of formats, from detailed datasets for technical users to dashboards, presentations and reports that provided context and explanation for wider stakeholder groups. This helped create a shared understanding of what the data was revealing and why it mattered.

To provide a complete picture of user needs and behaviours, the team brought together multiple sources of information. Customer surveys, user reviews, behavioural analytics and engagement data were combined to generate richer insights than any individual source could provide. By connecting qualitative and quantitative evidence, teams gained a deeper understanding of both what users were doing and the motivations behind those actions.

A key element of the engagement was the introduction of Success Measures, a framework designed to ensure that value was clearly defined before delivery began. For every proposed user-facing change, product owners were asked to articulate the outcome they expected to achieve. The Data & Insights team then worked with stakeholders to translate those goals into measurable metrics and identify the data required to assess success.

By introducing Success Measures at the earliest stages of product development, teams could identify dependencies sooner, align around common objectives and ensure the necessary analytics and feedback mechanisms were in place before implementation. This transformed measurement from a retrospective exercise into an integral part of the product development lifecycle.

Alongside this work, the team helped establish a consistent framework for measuring product performance. Core metrics, including customer satisfaction and users’ perception of forecast accuracy, were aligned to recognised industry standards, creating a shared language for evaluating success across products and teams.

Key learnings

Defining value before delivery creates stronger products and more effective decision-making.

By establishing measurable success criteria at the outset, organisations can move beyond assumptions, align teams around shared outcomes and create a repeatable framework for evidence-based product development.

The results

The engagement has helped establish a stronger culture of evidence-based decision-making across the Met Office’s digital product teams.

Working as a single Data & Insights team, Made Tech and Met Office colleagues combined user feedback, behavioural analytics and product performance data to provide stakeholders with a clear understanding of how users were responding to changes within the Met Office app. This enabled teams to make informed decisions, prioritise improvements and respond quickly when emerging issues were identified.

The introduction of Success Measures has also strengthened collaboration across teams. Product owners, analysts, designers and technical specialists are now aligned around clearly defined outcomes from the earliest stages of development. This has improved visibility of dependencies, encouraged earlier engagement between teams and ensured that measurement is considered throughout the delivery process rather than after changes have been released.

The ability to combine large-scale quantitative data with qualitative user feedback has created greater confidence in decision-making. Rather than relying on isolated feedback or anecdotal opinion, stakeholders can draw on a broad evidence base to understand user needs and justify product decisions.

The team’s insight work also directly influenced product priorities during the phased rollout of the refreshed Android app. Feedback analysis revealed that requests to reinstate widgets consistently ranked as the most prominent user concern, accounting for around 13-14% of feedback during the early stages of rollout. Even after eight weeks, widget-related feedback remained one of the most common themes among approximately 8,000 respondents.

Armed with clear evidence of user demand, the app team re-prioritised development work and accelerated delivery of widgets ahead of the full launch.

The Data & Insights team also monitored app usage trends to understand the broader impact of the refresh. While weather conditions naturally influence usage levels, analysis showed that total app users during the three months following launch exceeded the equivalent period in the previous year and were higher than any comparable period in the preceding five years. This provided further evidence that the refreshed experience was being positively received by users.

Key learnings

The most valuable outcome of a data-driven transformation is not the data itself but the ability to turn evidence into action.

By combining user feedback, behavioural analytics and clearly defined success measures, the Met Office can make confident, transparent decisions that are linked to measurable outcomes and demonstrable user value.

Insight in Action

Summary

Made Tech helped the Met Office build a culture where product decisions are guided by evidence and linked to measurable outcomes. Through a combination of stakeholder engagement, user insight, cross-team collaboration and the introduction of success measures, the joint Data & Insights team has strengthened the organisation’s ability to understand users, evaluate opportunities and continuously improve its digital services. The result is a more transparent, evidence-led approach to product development that delivers measurable value for both the organisation and the millions of people who rely on Met Office services.

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Our task was to bring all this data together into one platform and improve data insight for the adult social care sector. As a result it will be easier for providers of all sizes, councils and researchers to access and use the data.  By doing so, they’ll be able to better track care quality, identify gaps in services and plan more effectively. 

Working with Edge Health, we built a prototype and technical proof of concept(PoC). We gathered user feedback to help transform future care data use. Importantly, the PoC passed the Government Digital Service alpha assessment. It met all of the 14 standards, allowing us to proceed to the next phase (beta) of the project.

The challenge: Data interoperability

One of the main challenges is that care data is spread across many different systems, making it hard for providers to get a clear view of what’s happening. Data quality also varies. Especially for smaller providers who lack the time or tools to manage it well.

While bigger care providers have data analysts to help them understand the information, smaller providers struggle without that support. Health and social care systems don’t link up well either, which makes it difficult to coordinate services.

Our approach: Testing ideas results in successful alpha

The 10-week alpha phase focused on testing ideas and proving a unified platform could work:

User research: We spoke to about 20 local council analysts and care provider staff, from owners to managers and analysts, to understand their needs and shape the project’s direction.

Proof of concept: We successfully showed that the platform could pull in data from different sources, create key metrics and present useful insights. This helped us pass the Government Digital Service (GDS) alpha assessment, meeting all 14 required standards.

Iterative development: During this phase, we found that while local councils had access to some helpful tools, care providers were missing out on the data-driven insights they needed.

Solution: A unified data platform

We developed a platform to pull in data from multiple sources. This gives care providers and councils access to the same insights. Ultimately, by bringing data together, we’re aiming to create a more equitable playing field. A place where everyone can make informed decisions based on accurate, comprehensive information.

Results: data-driven insight for adult social care

Why Made Tech

• Sector knowledge: Made Tech’s has in-depth experience in both the health and social care industries. Our recent work with Skills for Care showed the value we could bring to this alpha and made us a clear choice for DHSC.
• Strong partnership: Working closely with Edge Health, experts in data analysis, made sure we could deliver insights that were both relevant and practical. We’ve successfully worked with a variety of partners on multiple projects and this experience has honed our skills in developing strong relationships.
• Agile methodology: Made Tech successfully introduced a faster, more agile and flexible way of working to the project.

Moving into beta

Our successful alpha passed the Government Digital Service (GDS) assessment, meeting the standards required to move to beta.  

We’re now starting beta with a private release and moving towards a full public launch.

Key developments include:

• Better accessibility and tech foundations – The platform is being built to be accessible and robust enough for wider adoption.
• Live minimum viable product (MVP) testing and improvement – Care providers and local authorities are using the platform, and we’re refining it based on their feedback.
• More useful data in one place – We’re making it easier for care providers to access a broader range of public data to support better care.
• Understanding the value for local authorities – We’re exploring how the platform can support local authorities, particularly in areas like commissioning and prevention.
• Considering AI features – We’re looking at how AI could be used to add value in future.

By breaking down data silos, we’re helping to create a future where everyone involved in adult social care can access the same valuable insights. This will ultimately lead to better care outcomes and more efficient service delivery across the sector.

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In January 2024, NHS England launched the Pharmacy First initiative, enabling over 10,000 pharmacies to treat common conditions such as sinusitis, earache, shingles and urinary tract infection (UTI) without the need for a GP appointment or prescription.

To ensure patient records remain accurate and up-to-date, pharmacies need to be able to share consultation data with GPs. Without a digital solution, this data sharing often relies on outdated methods like phone calls or faxes, leading to delays and potential mistakes.

Interoperability has long been a challenge for the NHS. With multiple systems and suppliers in place, making sure that patient data flows smoothly between both services is critical for both safety and efficiency.

The NHS Direct Care API Team asked us to develop a standardised approach, ensuring data could flow seamlessly between pharmacies and GPs with the click of a button. Our role involved setting clear standards, assuring supplier compliance and conducting thorough testing.

Our approach: streamlined data sharing

• Defined clear standards: We started by developing detailed technical specifications outlining how the code should be written and how the systems should communicate. These were designed to ensure consistency, security and accuracy in data sharing. 
• Supported suppliers: We worked closely with 6 suppliers (2 GP and 4 pharmacy system suppliers). Our experienced team, composed of technical architects, business analysts, delivery managers and user researchers held regular sessions to answer their questions, resolve technical issues and ensure their builds met the required standards.
• Technical assurance: We tested the solutions against strict technical standards to ensure they were secure and reliable.
• Clinical assurance: Clinicians reviewed the systems to confirm they worked correctly and safely for real-world use.
• Streamlined testing: We introduced a more agile process to speed up clinical assurance. Instead of lengthy, fragmented reviews, we organised joint sessions where all suppliers, clinicians and safety engineers could review the solutions together. This reduced delays and encouraged faster issue resolution.
• First-of-type rollout: Once the solutions passed all tests, we supported a trial phase in real-world settings. This ensured the systems performed as expected and allowed us to address any final issues before a wider rollout.

Results: improved patient safety

Since launch, up to 4 million people have accessed the Pharmacy First service, many through GP referrals. Impressively, only 8% of patients using the service are referred back to their GP. This highlights the effectiveness of pharmacists in managing conditions independently.

Specifically, the solution has helped to:

Next steps for Made Tech and NHS England

NHS England has now asked us to help them improve the end-to-end assurance process, building on our deep understanding of the system. We’ll streamline implementation, enhance standards and governance, and scale these improvements across healthcare services to ensure faster, more reliable solutions that benefit patients and staff nationwide.

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Many of us in the UK use the Met Office website and apps to check weather forecasts – often planning our day’s activities around it. From a technical side, this requires a lot of infrastructure under the bonnet. All of which help the website and apps to run, pull in the correct data, scale when it gets busy and send notifications to the public.

The Met Office wanted better visibility of its costs for these activities across all different accounts – in one easy to see place. This would help teams to understand how much each feature costs and where to make changes with data-driven insights backing them up. This is called financial operations, or FinOps. Working closely with the Met Office we set out to make its cost information visible and accessible with a new data platform. One where everyone can see it, ask questions about it and work together to save money.

Our approach

The app and services run on Amazon Web Services (AWS) using a Pay as you Go pricing model. This means information is in multiple AWS accounts that not everyone who needs cost information has access to. The process of gathering that data can also be time consuming. For the Met Office, having good visibility and tracking of costs can be a challenge.

We didn’t want to reinvent the wheel. As AWS was already providing all cost information, we simply wanted to work out how we could pull all of that information into one central location that made sense to everyone – from top level product owners to the developers working on individual system components. We set out to present the data in a bespoke way that worked for the Met Office. A way that allowed teams to answer the question ‘How much does this product/feature etc cost?‘ within a matter of seconds.

To keep costs low we took advantage of existing AWS solutions. We also used existing offerings within the Met Office, such as its business intelligence platform, as a way to visualise information.

Spending 3 days with the team we understood what they really needed from a data platform. This meant better visibility on how they use data, how to make savings and a clearer understanding of costs. We created a lightweight, low cost solution to get the data, processed it and then put it on their visualisation platform to get feedback. This allowed us to react quickly to changing business needs.

Results we achieved together

Creating stories out of data

As an example we can use the new platform to see that July running costs are higher, why is that? We suspect it’s because people are off on holiday and checking for weather updates – now the team can test this hypothesis with existing data to see exactly how traffic impacts cost.

This platform is a way to empower the team to ask specific questions in relation to cause and effect. Specifically around weather and human behaviour, with data to back up their assumptions. Teams can break down how they view costs by business area, services and different parts of functionality. This means they can clearly see where to make savings and challenge the idea that more users always means higher costs. An example might be seeing cost spikes around the time push notifications are rolling out. By putting all parts of a story together, the Met Office can review when and how they send these out. Now, cost becomes everybody’s responsibility and we begin to see a culture change.

Changes to new and existing features

The new data platform also means teams have better control of cost estimates for new website and app features – thereby reducing risk. By collecting data over time, teams can predict how much a new feature will cost or what savings they’ll make by changing the way they do things . This allows the Met Office to be more flexible in any changes it makes to the apps and website – helping to really measure value for money.

By making data visible, the Met Office can now switch off features it doesn’t need. Since February, the Met Office has reduced the cost of non product environments by 54% as a result of seeing cost information. And product costs are down for its main service by 20% since February. The great news is that the Met Office has had to change very little to their ways of working to achieve this. These savings come down to everyone being able to see the cost information through the platform, giving teams the ability to make simple and speedy changes.

Keeping all data within the Met Office’s existing systems was a key achievement of this project. While on paper the platform is simple – it does exactly what the Met Office wanted and needed it to.

This project on understanding costs was cost neutral for the Met Office, thanks to savings made from our previous work with user feedback and forecasting accuracy. We’re proud to be a part of helping the Met Office build a bigger data picture.

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We seek opportunities to partner with organisations delivering truly impactful work. Skills for Care is the leading source of adult social care workforce intelligence in England. They work tirelessly to provide the government with up-to-date metrics on the state of England’s adult social care sector. This includes how many care workers there are and how they’re distributed throughout the country. A task that’s made all the harder when you realise there is no legal requirement for any privately owned care home to report on these statistics.

As you might imagine, this is a mammoth task. When we first landed, the analytics group was performing the heroic feat of collating, cleaning and analysing years of data. From tens of thousands of facilities through a semi-manual and time-consuming process. The process was taking them months to complete and they knew they weren’t using their data to its full potential. They knew technologies existed that could help, but implementing them required skills and experience they didn’t (yet) have. We were eager to help them with that.

Our approach

To deliver the best value, we set out to build a solution that was both easy to maintain and powerful enough to handle their vast data collection. We also needed to make sure this was something they could continue developing once we’d left. Making upskilling the Skills for Care analysts from near-zero coding experience vital to success. Once these things were in place, it was clear that the deep sector knowledge the analysts held could be put to work – improving their reporting. 

Our data engineers wasted no time building a data pipeline. One that would automatically store, version, clean, join and transform – making sure all data was ready for analysis. The Skills for Care analysts leapt at the opportunity to develop new skills. They quickly became valuable partners in the development process by identifying opportunities for improvement and contributing significantly to the codebase.  

We demonstrated the importance of test-driven development, the power of CI/CD and the necessity of generating effective, usable documentation throughout.

By the end of the project, the team had turned a task that usually took about a month to complete into an automated process that runs every 2 weeks and takes about 20 minutes.

While this was happening, our data scientist was interrogating their data. Uncovering trends and developing new processes designed to extract as much insight as possible. They held regular showcase sessions. Presenting new analyses to shine fresh light on the trends that Skills for Care knew to be true but had never had the data to demonstrate. By working closely with their analysts we developed new machine-learning models that enable much more granular reporting. 

To make sure this bump in accuracy remains (knowing the efficacy of all models decays over time), we built monitoring steps into the pipelines. As soon as model accuracy metrics fall outside of a set range, analysts are notified and re-training jobs are triggered. Deploying a model and simply assuming it will continue to be as performant as the day you deployed it is never enough. 

Results we achieved together

The impact of this work cannot be understated. Through this partnership, Skills for Care has gained a wealth of new development and data science technical ability. They now possess a suite of bespoke, fully integrated AI models that deliver data-driven insights all the way to the highest levels of government. This transformation has allowed them to rethink their data collection process. And has resulted in a massive increase in data quality – compounding their improved reporting capability.

Skills for Care now perform complex analysis using version-controlled Python scripts and notebooks.

This allows them to access a whole world of open-source analysis tools and frees up their time to perform other aspects of their job.

Most importantly, this work will enable Skills for Care to produce their most accurate reports to date – starting from November this year. These enhanced reports will empower Ministers and MPs to deliver data-driven insights and decisions about the care sector. Leveraging modern data principles and AI, Skills for Care has significantly improved the quality and impact of their reporting.

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Forecasting weather requires deep scientific knowledge and vast amounts of computing power. World-leading experts, the Met Office are constantly improving their forecasting process – to help us stay safe and thrive. But how can they measure the success of a new weather model? It’s one thing to know that your model is more scientifically accurate. But when it comes to data-driven insights on how users respond to and understand that information – it’s a whole different ball game.

By using data and AI to automate the sentiment analysis of vast amounts of user feedback, we allowed the Met Office to extract patterns and trends with an efficiency that was previously impossible.

We knew we could then combine this with traditional methods of user research that provide in-depth, qualitative insights into user behaviours.

Our data science team helped the Met Office interrogate hundreds of thousands of individual pieces of user feedback. These came from from millions of daily users of the Met Office public website and mobile apps. Our challenge lay in extracting a needle of insight from this haystack of information. Together we set out to demonstrate if a new weather model was more trusted than previous iterations.

Our approach

When we joined, a sole team member was reading hundreds of comments every day on top of their day job. They were trying to extract insight from multiple spreadsheets and data sources. Our data scientists quickly recognised this tiresome task was a perfect opportunity for AI.

To begin, there were conversations around which technology to use. Should we jump on the large language model (LLM) bandwagon, or use a simpler, more tailored solution?

Despite the hype in the industry, we knew an LLM was not appropriate for this use case. Due to the complexity of these systems, their unpredictable behaviour and the large cost associated with the compute. We recommended in this early stage to use well-established natural language processing (NLP).

Following the advice of our Made Tech colleagues who are experts in this field, we opted for a tried and tested NLP model. As this would be best to categorise the thousands of items of accuracy feedback we receive. The efficiency of this system and the amount of time it has saved extracting insight from our feedback has been invaluable.

Fleur Layzell
User Journey Manager

To get started, we broke this task into 3 problems:

• how do we feed this insight back to stakeholders?
• how do we collect and process so much data?
• what insight can we extract from user comments?

In order to process the hundreds of thousands of pieces of user feedback, we built an automated analytics pipeline. This pipeline had to be easy to maintain and robust enough to support computationally heavy machine learning (ML) tasks. It also had to safely strip any personal information that users might have submitted. 

Building this platform centralised a single source of truth for all user feedback. We were then able to begin sifting through comments and developing analytics processes. It was in this space that the majority of data cleaning, enriching and preparing processes were carried out.

This central analytics environment instantly began closing the feedback loop between stakeholders and users, empowering the Met Office to begin asking for bespoke pieces of analysis and digging into feedback following feature releases. We presented their user feedback in a completely new light. We helped visualise user distributions, target specific use cases and report on different weather scenarios as they happened. Something that had not been possible before. 

We brought in junior developers to the team to provide valuable experience on at greenfield project. By aligning to the Met Office’s strong culture of excellent people, culture and developing capabilities, the team were able to provide a supportive space to learn, upskill and share knowledge across the team and wider Consumer Digital Programme.

Through their hard work and dedication, our colleagues at Made Tech have created a robust data pipeline. And in doing so, a more efficient way for the Met Office to not only monitor accuracy and cost reporting, but open the door to other areas where accuracy and potentially more general feedback can be monitored. This increasing our validity as an organisation where user-centred experience is at the heart of everything we do.

Fleur Layzell
User Journey Manager

Results: Data-driven insights at the Met Office

Improving the response to user feedback

Thanks to the development of the feedback analytics platform, the Met Office can now actively monitor user feedback and measure the perceived success of new weather models in real time. Stakeholders and product owners can track user sentiment throughout the rollout and precisely quantify the impact of any changes they make to the service.

They’re able to monitor the impact of different weather systems, check model performance across different regions and environments and rapidly respond and iterate on issues as they arise – reducing the feedback-to-development loop from weeks to days. All focused on providing the best service possible for users.

User feedback analytics platform

The new analytics platform empowers the Met Office to conduct more types of data analysis. We’re now able to use powerful NLP techniques to categorise comments, allowing a shift from anecdotal to actual evidence. We can visualise and monitor feedback as it lands, and slice it however needed. For example, if the weather science team needs to know how users in Region X responded to forecast Y,  this is now achievable with the click of just a few buttons.

By presenting this analysis in responsive, live dashboards, Made Tech has enabled an interaction with Met Office user data like never before. 

Moving forward

AI technology continues to progress at a lightning pace. Smaller, open-source generative AI solutions are now far more available, with performance and reliability nipping at the heels of the larger closed-source models. The next steps for the Met Office will be to further enhance this analysis platform by generating automated, summary reports, condensing a never-ending river of user feedback into easy-to-understand, direct points of action. 

This is an excellent example of delivering value to our public sector clients. By working in partnership with us, we have led the Met Office to the adoption of new technologies that harness the power of their own data.

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Hackney Council wanted to better harness the power behind its data to modernise and improve services for residents. 

Hackney’s data was in multiple sources, including hidden in legacy technology and modern SaaS applications. They were finding it challenging to use it for analysis. Working with their team we delivered a cloud platform. Designed to help the council maximise its data-driven insights and deliver the best results for residents.

Creating a modern data platform allowed the council to store and analyse its data all in one place. Through this new holistic view of its residents, Hackney could help them live healthier and happier lives.

Our approach

The council wanted to focus on collaboration, sharing information and awareness of the available data. We worked with them to build a data lake, sorting captured data into 4 tiers: landing, raw, refined and trusted.

We didn’t want Hackney’s engineers to spend all their time maintaining the platform after we left. So we avoided doing anything overly complicated or bespoke.

Hackney, like any local authority, is responsible for delivering a wide range of services. It uses a range of applications to run them. This means it has many different data sources: databases, APIs, CSV files and Google Sheets, amongst others. 

The main challenge was the sheer volume, with some services holding huge amounts of data. For example, Hackney’s parking system alone holds millions of rows of data and is updated daily.

Alongside this modern data platform, we consolidated their housing repair data. This allowed further insight and analysis and was used by Hackney’s Link Work team to reach out to support residents at higher risk.

The council wanted the new platform to encourage re-use across the public sector. Hackney’s strong vision of helping other authorities use their own data in better ways was key. We therefore built the platform using AWS infrastructure and open-source technologies. Other councils could then easily view and aim to replicate Hackney’s insights.

Results we achieved together

For the data platform to be sustainable we knew that Hackney’s staff needed to be confident using it. That’s why, as well as the infrastructure of the data platform, we focused on training and documentation. When we left, Hackney’s analysts were able to work independently, adding data to the platform and processing it according to their needs. This was key to make sure it didn’t become another piece of legacy technology.

Wanting to build a culture that recognises the value of data, we trained Hackney’s analysts in Spark, a data processing tool used by the core team. One of our engineers spent time working with the analysts to develop machine learning solutions to some sticky issues. This included Entity Resolution (for example, detecting that “10 Downing Street” is the same as “10 DS”), which we hope will have a huge impact. We also created an in-depth playbook that captured how to use the new platform which they could use for future projects.

Stepping away from legacy technology, the new system helps the team to make sure all data is accessible. This gives them the best chance of reaching residents using more than one service. We combined this with robust data governance to make sure that data can only be accessed by the people that need it – and for the right purpose.

One large benefit of this new data-driven design is cross-collaboration. For example, by combining data on property repairs with a vulnerability index built during the pandemic, Hackney could predict cases where vulnerable people needed repairs. They could then contact elderly residents who hadn’t been heard from in properties that likely had repairs due. A third of the residents contacted by the team were found to have unmet needs. They were offered support, advice and referrals to other vital services including adult social care, financial inclusion and voluntary organisations.

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The Data Hub is a self-built customer relationship management system (CRM) used by The Department for Business and Trade (DBT). As a data platform it provides:

• the department with a single view of all the companies they work with
• help for UK businesses find new export markets and expand existing ones
• support for businesses from across the globe looking to invest in the UK

As a learning organisation DBT wanted to review how the Data Hub meets its changing needs. With a goal to provide the best possible service, DBT also wanted to explore future design requirements for the data platform.

Our approach

Together with the Digital Data and Technology and Implementation Unit team we set out to create a report on the Data Hub. This paper narrowed the direction of travel down to 3 options, including:

• continuing with Data Hub as it is
• moving to a Salesforce or Dynamics alternative
• developing a new approach that brings together the best features of the Data Hub, Salesforce and other CRMs

We knew we wanted to first find out what was working well with the current data platform and what was performing not so well. Supported by user and market research, we set out to interview DBT teams worldwide using different CRM systems. Next up we spoke to other government departments, agencies and local authorities. This helped us understand their experiences of running a CRM system like Salesforce or Dynamics – the good, the bad and the ugly.

When creating the report it was important we balanced both buy and build options. So all this research helped us run a comparative analysis that included examples of startup and run costs for different options. However, we also made sure to include a modular build option that brings together different CRM systems into the existing Data Hub.

This is a complex issue. Your team’s paper and all the underpinning work enabled the committee to have a productive discussion and move this issue forward. Many executive committee members commented on the quality of the work – do keep it up!

James Bowler
Permanent Secretary, the Department for International Trade

Results we achieved together

Supported by Digital Data and Technology leaders and the Implementation Unit strategy team we developed an in-depth report that included:

• a strategic and economic analysis
• workshops with senior civil servants
• policy recommendations and insights into CRM options
• detailed assessments of key strategic benefits or costs of each option

We’re delighted that the report was presented to DBT’s executive committee, setting out the options for a way forward that would improve all user experiences.

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The UK Health Security Agency (UKHSA) plays an important role in society. They help to keep us all safe from the impact of infectious diseases. UKHSA covers everything from chemical to radiological, biological to nuclear and other health threats. One of the main ways they do this is by monitoring and responding to changes in new and known diseases using data. This includes laboratory surveillance data. By using data-driven insights they stay up to date on patterns of infection and new outbreaks – keeping us all safe.

To continue to get the most out of the data they collect, UKHSA wanted to strengthen their laboratory surveillance data platform. We worked with the team to run a discovery with a goal of finding areas for improvement. Together we set out to help the UKHSA make even better use of their data for more informed decisions using data-driven insights.

Our approach

Working with the team we ran user research sessions to understand challenges users face – across the whole system. To make sure we understand a range of different needs, we interviewed people in various roles and teams. This included all levels, from service owners and surveillance specialists to data processors and laboratory managers.

We wanted to understand each person, the process and systems they use and any requirements they need to do their job well. Over 60 minute interviews we used discussion guides to steer conversation and allowed time for team members to ask us questions. Depending on their preferences, we also followed up with another interview to sense-check the process maps we created.

The final step was to review the data collected in each component of the system they were currently using. From this we created a consolidated data model. One that’s flexible enough to allow for future changes. It’s also designed to allow for new data sources or new types of testing to keep it modern and robust.

Results we achieved together

Throughout the project we worked closely with the UKHSA team to:

• map out their existing process to help pinpoint areas for improvement
• create a single data model that allows for a range of data analysis and sharing opportunities
• recommend improvements that align with other healthcare bodies and programmes

This data platform discovery and its findings has helped UKHSA to make better use of their data. It has also helped in improving efficiency and reducing silos across the organisation. By strengthening their laboratory surveillance data system in this way, UKHSA can continue to do what they do best – keep us all safe.

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Since 2006 Skills for Care has been collecting and analysing data on the thousands of adult social care workforce across England. Providing data to the Department of Health and Social Care helps better inform the decisions made by government when it comes to the care sector.

Many of their data analysis processes were manual, taking considerable time and not maximising data-driven insights. We worked with the team to transform their data processes to better provide intelligence quickly and accurately.

Our approach

We implemented the Amazon Web Services (AWS) data ecosystem. This helped us modernise Skills for Care’s data architecture and processing. The goal was to streamline processes, automate routine analysis and free up valuable time.

Working with the analytics team we helped put in place best practices in data engineering. We also worked with them to apply machine learning to improve their workforce estimates.

The next stage was to help Skills for Care identify what was needed to continue to develop this new data pipeline long after we left. They’re now fully prepared to continue to build their pipeline as new data becomes available.

The data engineering prototype developed in this project is the first step toward a new way of working. It will help us to report more frequently and accurately to support faster and better decision-making.

Will Fenton
Analytics Manager

Results we achieved together

Crucially the team can now import data from many data sources automatically. This has helped enrich their data, fill data gaps and review at a more granular level – improving data quality. Now that there’s no need to manually process data that team are saving time and effort. Analysis that used to take 2 days can be done in 15 minutes and the datasets available for review are also extended from one to 10 years. Data visualisations can now also better inform analysis, making it much easier to see trends over time.

For Skills for Care it’s now increasingly quick and easy to test new ideas. They are better prepared to identify and fix issues earlier when data changes outside of the organisation happen.

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