Case study: Performance analysis of UK businesses in high growth technology sectors, HMRC and DSIT
Case study details
| Metadata item | Details |
|---|---|
| Owner: | Analysis Function Central Team |
| Who this is for: | All government analysts |
| Contact: | Sam Caton and Nathan Anderson, HMRC (CrossGovernmentAnalysis@hmrc.gov.uk) Michael Watson (Michael.Watson@dsit.gov.uk) and Paddy Vanderpant (Paddy.Vanderpant@dsit.gov.uk), DSIT |
Team / Department
- Cross-government Business Analysis, HM Revenue & Customs (HMRC)
- Technologies, Growth & Securities Analysis, Department for Science, Innovation and Technology (DSIT)
Situation / Action
The Department for Science, Innovation and Technology’s (DSIT) Technologies, Growth and Security team is responsible for supporting high growth technology sectors in the UK. However, these are not measured well by ‘Standard Industry Classification’ codes, and the variable coverage can be poor on existing business datasets they have available.
To solve this problem, DSIT analysts adopted a supervised machine learning approach that could identify businesses within the priority technology sectors. HM Revenue & Custom (HMRC) cross-government business analysis team then took business identifiers and matched administrative company tax data. They performed analysis of employment and gross value added to evaluate performance.
Outcome / Impact / Results
DSIT is now able to report on performance of businesses in high growth technology sectors; without collaborative working across government this wouldn’t have been possible.
Internally, these measures form the basis of monitoring and evaluation for spending review proposals and DSIT report on the situation in these technology sectors to Ministers and senior officials via the Council for Science and Technology. This analysis provides a cost-effective route to sector analysis, reducing the need for licenses to databases for financial and employment information, for example – whilst DSIT analysts have many options for analysis at their disposal, this option using evidence internal to government is both robust and economical.
Further reading
Analysis was made publicly available via the UK Quantum Skills Taskforce report, published in May 2025.
How this work supports the Analysis Function strategy
DSIT’s work demonstrated behaviours which support the Analysis Function strategy by supporting tech sector policy (1), using machine learning for classification (2), and linking tax and business data across departments (3).
Find out more: a strategy for analysis in government 2025 to 2028