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Funding Service: Defining data standards for grant teams

Creating reusable standards to improve grant delivery

Data collection in funding is complex, with no two funds collecting the same data in the same way. This causes problems with data consistency and increases the burden on grant recipients who report data for monitoring purposes.

As part of the Ministry of Housing, Communities and Local Government (MHCLG)’s work to simplify the funding landscape, the Funding Service is creating reusable data standards for grant teams.

By establishing data standards for grant funding, our aim is to:

reduce the time taken to onboard funds onto digital services reduce the burden on grant applicants and recipients by ensuring a uniform process across grants improve the consistency and quality of data collection

We believe that standardising funding data will both benefit grant teams and improve the quality and efficiency of grant delivery across the whole department.

What we set out to do

To help us achieve consistency for grant funding data, we embarked on an 8-week project to:

document existing data standards and identify where the gaps are agree an initial set of standards to define and design explore effective ways of governing and embedding data standards across MHCLG

In this blog post, you can find out about the approach we’ve taken and our progress to date.

Understanding data needs

Before defining any data standards, we first needed to understand grant funding data needs across the department and beyond. We looked at:

what data is collected and by what system what the purpose is for collecting the data and what it is used for what data we need to support the various funding processes how data flows through the systems and processes during the funding lifecycle 

This involved speaking to business and grant funding teams in MHCLG, including 10 different teams on finance alone. We then looked at any standards already being used within those teams, wider government departments and across the subject matter in general. 

The approach allowed us to compare the data needs against standards already in use and identify any data gaps. It also allowed us to pick the most suitable starting point for the Funding Service’s data standards which could be modified to meet those needs.

Prioritising ‘risk’, ‘organisation’ and ‘financial’ data standards

A graphic showing a map of Barnet, with the following phrases listed: London Borough of Barnet, Barnet Council, Barnet, LB of Barnet and Barnet London Borough.Example of inconsistent reference data for organisation name.

We identified three standards to prioritise: risk, organisation and financial data. These were chosen deliberately:

Risk data is a mature subject matter—we have a well-defined and understood definition of it. This allows us to quickly work through the process of how we define standards and how we can replicate that process for future standards  Organisation data is used extensively across our digital services and it gave us an opportunity to produce an exemplar of how reference data is used within data standards Financial data is a subject less well researched. We wanted to understand the challenges of producing a complex data standard to test the process of defining data standards we’d already prepared

Our data standards design process

A graphic showing the cycle described in the paragraph below, with the following steps listed: assess data needs, review existing standards, adopt and extend, assure, publish, implement, evaluate and measure, iterate, and with 'data standards' in the middle.

Taking a user-centred design approach, we created a blueprint to define the data standards. The cycle include:

assessing data needs reviewing existing standards adopting and extending (to make them relevant to our user needs) assuring and validating standards with users establishing governance and publishing planning and implementing changes monitoring and evaluating use and impact iterating standards based on changes to user needs and policy

The cycle will continue to recur as we test and iterate the grant data standards once they are in use across the department.

Working on the data standards together

Defining data standards is not an individual or even a team activity. For the grant funding data standards to succeed, there needs to be an ongoing collaborative effort between business and policy teams, subject matter experts, and operational and delivery teams. These differing viewpoints help to us give a 360-degree view of data and how it is used within MHCLG. They also provide a level of assurance and confidence that the data standard is well defined.

Similarly, we’ve begun work with data colleagues throughout MHCLG, including the Deputy Prime Minister’s Data Unit and the Chief Data Office, on how we introduce ownership and governance controls into the standards.

What's next and how to get in touch

As we pass through the mid-way point of the project and progress with the definition of our three initial data standards for grant funding, we’re beginning to think about what standards we want to produce next. We will also be putting forward recommendations on how we successfully implement the standards within MHCLG’s digital services.

Subscribe to the MHCLG Digital blog for the latest updates or contact us at This email address is being protected from spambots. You need JavaScript enabled to view it. to find out more about our data standards work.

If you are working in a funding service in another government department, you can also join the #funding-services channel on the cross-government Slack. 

Note for grant funds delivered by MHCLG

All new and redesigned government funds delivered by MHCLG must use the Funding Service, unless an exemption is agreed when your business case is approved. If you are designing or delivering a fund via MHCLG and are not yet talking to the team about onboarding arrangements, please get in touch at the email address above.

(Originally posted by Wayne Busby, Technical Architect)
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