There is a quiet commercial revolution happening inside some of the UK’s most data-rich businesses, and most people outside those firms have no idea it’s occurring. Consultancies, accountancies, law firms, and agencies have always sat on extraordinary volumes of operational information: billing patterns, project timelines, sector benchmarks, pricing trends, hiring cycles, contractual terms. For years, that data sat in spreadsheets, CRM systems, and practice management software, doing nothing beyond its original administrative purpose. A growing number of firms are now asking a sharper question: what would it be worth if we packaged it properly?
Monetising internal data in professional services is not a new idea in principle, but the mechanics of doing it compliantly, commercially, and at scale are becoming genuinely accessible for mid-sized firms rather than just the large consultancies with dedicated data science teams. I’ve spoken with partners at several UK advisory businesses who are now treating their anonymised operational data as a product line, and the results are more varied and instructive than the headlines tend to suggest.
What kinds of internal data are professional services firms actually sitting on?
The instinct is to think of “client data” as the only data a firm holds. In reality, most firms accumulate at least three distinct categories. First, there is operational data: how long specific types of engagements take, where cost overruns occur, which service lines are growing. Second, there is sector-aggregated data: the patterns that emerge when you anonymise and pool activity across dozens or hundreds of engagements in the same industry. Third, there is market-signal data: the questions clients are asking, the compliance changes driving enquiries, the hiring decisions that precede growth phases.
Each of these categories has genuine commercial value if handled correctly. A mid-sized accountancy firm with 300 SME clients in manufacturing has, without realising it, built a reasonably rich picture of what margins look like across the sector, what the typical working capital cycle is, and which cost pressures are hitting hardest right now. That picture, properly anonymised and structured, is worth something to trade bodies, lenders, insurers, and market research buyers. The firm is not selling anyone’s confidential information. It is surfacing aggregate insight that no single client could produce alone.
Staying inside UK GDPR: the rules that matter here
The legal framework is less prohibitive than many partners initially assume, but it does require careful thought. Under UK GDPR, genuinely anonymised data falls outside the regulation’s scope entirely. The key word is “genuinely”: the ICO’s anonymisation guidance is clear that data is only truly anonymous if re-identification is not reasonably likely given all the means available. For small cohorts or niche sectors, that bar is harder to clear than it looks on paper.
In practice, firms doing this well are applying a combination of aggregation thresholds (never publishing figures based on fewer than a specified number of contributing records), suppression of outlier data points that could act as identifiers, and formal anonymisation reviews before any data product goes external. Some are also leaning on legitimate interests assessments where data remains pseudonymous rather than fully anonymous. The ICO has published updated anonymisation guidance that any compliance lead should read before a firm commits to this commercially. Getting this wrong is not a hypothetical risk; the reputational damage from a client finding their confidential situation reflected in a published report would be severe, even without a formal regulatory finding.
The commercial formats that are actually working
Benchmarking reports are the most obvious product and the one I see most commonly. A law firm publishes an annual commercial contracts benchmarking study, drawing on anonymised data from its own transactions. An accountancy practice releases quarterly insight on SME cash flow patterns by sector. A consulting firm produces a salary and day-rate benchmarking tool for a specific professional category. These all have natural audiences, and firms are monetising them in two ways: direct sale, or as gated lead generation assets that convert at a meaningfully higher rate than generic white papers.
Thought leadership is a related but distinct play. Several agencies I know of have moved from producing opinion-based content to producing data-backed insight pieces, because the latter performs better in search, attracts better press coverage, and converts prospects more efficiently. The data backing the insight comes from their own project history. Positioning this correctly matters: the firm is not positioning itself as a data vendor; it is positioning itself as an authority whose experience at scale gives it something to say that a smaller competitor cannot.
A third commercial format worth mentioning is the subscription intelligence product. A specialist professional services firm with deep sector focus can, over time, build a subscriber base for regular data releases. The subscription model is not appropriate for every firm, but for those with genuine depth in a narrow sector, it creates recurring revenue that is completely separate from billable hours. For firms thinking about how to manage the technology stack involved in building and distributing these products, dijitul.ai is one resource worth exploring as part of a broader tooling review.
The internal infrastructure question
None of this works without a minimum viable data infrastructure. Most mid-sized professional services firms have their data in several places that don’t talk to each other cleanly: a time-recording system, a separate billing platform, a CRM, and possibly a project management tool. The first practical step is usually a data audit, not to identify commercial opportunities immediately, but to understand what actually exists, where it lives, and how consistently it has been captured.
Firms that are serious about this tend to appoint an internal data steward, not a data scientist necessarily, but someone who understands both the commercial opportunity and the compliance obligations. That combination is rarer than it sounds. Without it, the anonymisation process tends to be either too conservative (producing insight too vague to be useful) or not conservative enough (creating compliance risk). I’d argue this role is as important as the commercial packaging decision itself.
It’s also worth considering how this connects to existing knowledge management practices. Firms that have already built structured internal knowledge bases, as explored in our piece on using internal knowledge bases to reduce dependency on key staff, often find they’re closer to a viable data product than they thought, because the discipline of capturing and structuring knowledge transfers directly to the discipline of capturing and structuring data.
Pricing and positioning data products realistically
One error I see is firms underpricing or giving away data products because they feel uncomfortable treating insight as inventory. That discomfort is worth examining. If a benchmarking report takes 40 hours of analyst time to produce and provides a buyer with information they’d otherwise spend £5,000 commissioning from a market research firm, charging £500 for it is not aggressive; it’s underselling. Firms that have moved to treating their data products commercially, complete with pricing tiers, licensing terms, and renewal cycles, report that buyers take the product more seriously as a result.
Positioning matters too. The most successful examples I’ve seen do not lead with “we analysed our client data”. They lead with what the buyer learns. A manufacturing sector margin benchmark sells on the usefulness of the benchmark, not on the methodology behind it. The methodology is important for credibility, but it belongs in the methodology appendix, not the headline value proposition.
For firms already thinking carefully about AI-generated proposals and how to win work faster, as covered in our article on AI-generated proposals in UK professional services, proprietary data is the natural complement: proposals backed by your own benchmarks carry a different weight than those built on publicly available statistics.
The firms getting ahead of this are treating internal data as a strategic asset rather than an administrative byproduct. That framing shift is, in my experience, the hardest part. The compliance framework is navigable. The technology is accessible. The harder job is convincing a partnership that the data they’ve accumulated over years of client work has standalone value. It does.
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