Living descriptive study · DM-MCS-001
MCS Certified Installers UK: Dataset Structure, Technologies and Geography
Abstract
This living descriptive study examines the structure of Data Menu’s UK MCS installer dataset. It combines counts of non-empty records, recognised technology categories, recorded geographic classifications and populated fields. The results describe the analysed file rather than national installation activity or the number of distinct operating businesses. An integrated view makes it possible to read technology and location summaries alongside the completeness of their supporting fields. Numerical findings are calculated when the analysis loads; each result set identifies its source update and analysis time.
Research questionWhat does the available MCS installer dataset describe, and which conclusions can its structure support?
1. Background and scope
Installer datasets can support supplier research, market mapping and the design of further studies. Their usefulness depends on what a row represents and how the available variables were recorded. An installer record is not an installation, a measure of annual output or a direct estimate of workforce capacity. Government guidance distinguishes joining the installer database from recording individual installations in the MCS Installations Database [2].
This report treats the file as an observed collection of records. It brings together three measurements that answer different questions: technology membership describes recorded categories; geography describes the selected location field; and completeness describes whether columns contain values. Reading these measurements together reduces the risk of interpreting a well-populated spreadsheet as independently verified market intelligence.
2. Findings and visualisations
The results below are calculated from the latest available source analysis. The methodology and discussion describe how to interpret them; numerical claims appear only after the results have loaded.
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Preparing the latest available aggregate findings. Larger files may take a few minutes.
3. Methods
- Exclude rows whose available cells are all empty after conversion to text. Count each remaining row once. Retain every source column as a separate field; do not merge records by company name, telephone number or certification number.
- Identify supported technology indicator headings and categorical technology values. Match recognised labels and aliases; count a row at most once in each technology category. Retain a combined ground/water heat-pump category as combined rather than allocating its rows to two separate technologies.
- Select one supported geography field in this order: region, county, country, then local authority. Count accepted labels after trimming and collapsing whitespace. Do not reconcile spelling variants, geocode addresses or substitute service-area indicators.
- For each field, divide populated cells by all analysed rows. For category shares, divide category records by all analysed rows. Round displayed percentages to two decimal places.
- Publish technology categories with at least 15 rows. For geography, apply the same threshold and display at most the 20 largest eligible labels. Category suppression and display limits do not remove rows from the denominator.
4. Interpretation
The record and field totals establish the size of the file being analysed. The technology results describe recorded capabilities or classifications, while the geographic results describe the selected source labels. These are complementary views of the same rows, not separate datasets that can be added together.
Technology shares may sum to more than 100% because one row can belong to several categories. Geographic shares may sum to less than 100% because of missing or rejected labels, the minimum category threshold and the display limit. These two behaviours have different causes.
The completeness table is a guide to which columns may need closer inspection before reuse. A populated cell can still be incorrect, outdated, a placeholder or unsuitable for a particular purpose. Completeness is only one dimension of data quality [3].
5. Limitations
- Rows are the unit of analysis. The calculation does not deduplicate legal entities, establish whether a business is currently trading, or independently re-check its certification.
- The source is a commercial dataset rather than a probability sample. No sampling weights, population estimates, confidence intervals or causal claims are produced.
- This is a living analysis of the latest available source file. Later refreshes may change the figures. Comparisons require retained, dated snapshots and consistent definitions.
- Technology recognition is rule-based and depends on source labels. Unrecognised descriptions are not assigned to a category. The geographic breakdown is not a map of installation service areas.
- No historical trend, regional shortage, company-level specialisation or relationship between technologies and locations can be inferred from these marginal summaries alone.
6. Implications and further research
The combined report provides a reproducible starting point for describing the available file. Its value is the ability to inspect category distributions together with the fields and counting rules that produced them.
Further work should be driven by distinct questions and suitable evidence. Historical change needs preserved source versions; regional availability needs service-area or operational information; company-level analysis needs a documented entity-resolution method.
7. Data access and disclosure
Data Menu produces this analysis and sells access to the underlying commercial dataset. This commercial relationship should be considered when using the study. The aggregate downloads are available with the findings; individual records and the source workbook are not published here.
These studies examine the same source dataset and are not independent replications. They are descriptive analyses, have not undergone external peer review, and are not official MCS statistics. Data Menu is independent of MCS and does not claim its endorsement.
View the source dataset →8. References
- Data Menu Limited. MCS Certified Installers UK. Commercial source dataset; current version identified with each analysis. Source
- Department for Energy Security and Net Zero. Next steps: getting started as a heat pump installer. GOV.UK. Source
- Government Data Quality Hub. The Government Data Quality Framework. 3 December 2020. GOV.UK. Source
9. How to cite this study
Data Menu Research. MCS Certified Installers UK: Dataset Structure, Technologies and Geography. DM-MCS-001, methodology 2.0. Data Menu Limited. Living study, n.d. https://www.datamenu.co.uk/research/mcs-certified-installers-uk.
When citing numerical findings, use the snapshot citation supplied with the loaded results. Retain the source update, analysis time, analysis version and aggregate download so readers can identify the figures you used. A live page is not a permanent archive of a historical result set.