Data Science and Analytics Editing and Proofreading Services

Analytics work is judged almost entirely on the write-up. The query was correct, the sample was adequate, the confounders were considered — but none of that reaches the person deciding whether to change the pricing model. What reaches them is a paragraph and a chart, and if the paragraph says "engagement appears to be trending positively" they will either ignore it or act on a reading you did not intend. Analysts lose more arguments to imprecise language than to bad statistics, and they usually lose them silently.

We edit what data and analytics teams produce — analysis reports and experiment write-ups, A/B test results and readouts, dashboard documentation and metric definitions, data dictionaries and catalogue entries, data quality and lineage documentation, methodology appendices, executive summaries for non-technical stakeholders, data governance and retention policies, requests for data access and privacy impact assessments, notebooks intended to be read by others, and slide narratives for quarterly business reviews. Our editors check that every metric named in the document has one definition, that correlation is not quietly promoted to cause between the body and the summary, and that uncertainty is stated in a form a decision-maker can use rather than a hedge that transfers the risk to them.

The experiment readout is where the writing does the most damage or the most good. Most fail in the same three ways: the result is reported without the confidence interval, the primary metric is not distinguished from the metrics that were examined afterwards, and a null result is written apologetically as though the analyst had failed. We rewrite readouts so the headline states the effect size with its interval in the units the business cares about — "checkout conversion rose 0.4 points, 95% CI 0.1 to 0.7, worth roughly £180k annually at current volume" — so the pre-registered primary metric is labelled as such and any secondary findings are marked exploratory, and so a well-run experiment showing no effect is presented as the useful, money-saving result it is. Decision-makers who can see the interval start asking better questions, which is the whole point.

Everything you send is handled in confidence, including unpublished results, customer data descriptions and internal metric definitions. Whether you are an analyst whose findings keep getting misread, a team standardising how experiments are reported, or a researcher writing in English as an additional language, we can make the writing accurate and persuasive at the same time.