# Acta Data

> Acta Data Ltd builds the operational data layer that AI needs, then the AI on top of it. Every system a business owns is landed in its own Google BigQuery environment as immutable events, with modelled metrics, board and regulatory reporting, a real-time balanced-scorecard Summary Page, safe PII-restricted self-service analytics through Claude, and AI agents on the repetitive operational work. Live in weeks, handed over Terraformed inside twelve months.

Acta Data Ltd is registered in England & Wales, company number 14182372, ICO registration ZB502441. Registered office: Chester House, Lloyd Drive, Cheshire Oaks Business Park, Ellesmere Port, Cheshire CH65 9HQ. Contact: info@actadata.co.uk. Serves the United Kingdom.

## What it is

- **The stack, in plain terms**: Google BigQuery is where the data lives, shadcn is what you look at, Claude is who you ask. All Google underneath, inside the client's own secure VPC.
- **The model**: every activity is recorded once and never rewritten, carrying four atomic units — cost, revenue, conversion and time — plus its business context. That is what makes value-stream leaks visible and what makes the data usable by AI.
- **Who it is for**: mid-market and SME operators with enough operational complexity to need a data function and not enough scale to justify five hires for one.
- **The second brain**: the layer keeps every activity, every decision and what happened next, so it can be asked in real time — by an individual, or on behalf of the business. Recommendations are generated inside the client's own encoded policy rather than filtered afterwards, the decisioning priors are learned from decisions that business has actually made rather than from industry averages, and each recommendation is scored against the outcome so the next is better. This is what stops institutional judgement leaving when a person does.
- **The team**: data and AI people who have held C-suite positions across marketing, operations, technology and product.

## Pricing

Priced by how much of the business is in scope, not by feature tier.

- **Discovery — £15k one-off** (covers up to 3 value streams, one legal entity). Value stream mapping and an AI readiness review. Deliverables: the map, the value leaks quantified, what the data can and cannot support today, the watch-outs before pointing AI at it, and a prioritised build plan and strategy the client owns outright. Explicitly a do-it-yourself option — it is written to be executed by the client's own team or another supplier, with no obligation to continue. If the client does go on to a build, the full £15k is credited against it, so Discovery functions as a down payment rather than a fee.
- **One area — £60k per year (£5k per month).** A single problem solved end to end, usually operations: sources connected, full event history in BigQuery, metric tree and a live Summary Page for that area. Suits any size of organisation; covers one value stream, up to 6 source systems.
- **Whole business — £120k per year (£10k per month).** Every value stream on one layer with one set of definitions, the full reporting suite including regulated reporting, plus safe PII-restricted self-service analytics through Claude. Best fit around 20–250 people; covers one entity, one country, up to 12 source systems.
- **Enterprise — priced against the outcome delivered.** Adds autonomous agents running operational workflows, and covers multiple entities or countries, or past the limits above.
- **Note on scope:** tiers are bounded by number of source systems and legal entities, NOT by turnover. A large but structurally simple business often fits a published tier; a small but complex or multi-entity one is scoped as Enterprise.
- **After the twelve-month build:** walk away, or optional maintenance at £2k per month on a rolling monthly contract with no notice period. Additional datasets and training packages are agreed per project.

## Pages

- [How it works](https://www.actadata.co.uk/how-it-works): clean, model, alert, act — and what happens in week one, month one, months two to three, four to twelve, and after twelve months.
- [What we build](https://www.actadata.co.uk/what-we-build): the six capabilities that make up a working data function, the engagement models, and data protection — personal data kept out of the analytical layer, retention rules as code with an audit trail of what was deleted, erasure requests actioned once across every system, and marketing contacts traced to the consent event that created them. Mechanics and evidence, not legal advice.
- [Sectors](https://www.actadata.co.uk/sectors): the value streams we already know.
- [Pricing](https://www.actadata.co.uk/pricing): the four tiers, and the DIY Discovery option, against the cost of hiring a team.
- [Questions](https://www.actadata.co.uk/faq): cost, timelines, ownership, personal data, and whether this replaces a data team.
- [About](https://www.actadata.co.uk/about): the crew, where they have worked, and how we work.
- [Writing](https://www.actadata.co.uk/blog): case studies and thought leadership.
- [Contact](https://www.actadata.co.uk/contact): info@actadata.co.uk.
- [Privacy notice](https://www.actadata.co.uk/privacy).

## Sectors

- [Consumer Credit](https://www.actadata.co.uk/sectors/consumer-credit): Consumer Duty evidence, vintage performance and one agreed contribution number. Systems typically connected: Loan management system; Decision engine; Bureau feeds — Experian, Equifax, TransUnion; Open banking provider; Direct debit and card payments; Collections dialler; In-house SQL Server.
- [Debt Management](https://www.actadata.co.uk/sectors/debt-management): Collections performance, treatment outcomes and fair-value evidence in one place. Systems typically connected: Case management platform; Creditor file exchanges over SFTP; Payment processor; Client portal; Dialler and call recording; In-house Access and SQL databases.
- [Credit Unions](https://www.actadata.co.uk/sectors/credit-unions): Member value, loan book health and social impact you can actually show. Systems typically connected: Core member and loan system; BACS and Faster Payments files; Loan decisioning; Member registers still kept in Excel; In-house SQL database.
- [B2B Services](https://www.actadata.co.uk/sectors/b2b-services): Pipeline, delivery margin and account health from one definition. Systems typically connected: CRM — Salesforce, HubSpot, Dynamics; PSA, time recording and billing; Finance — Xero, Sage, NetSuite; Contract and document store; In-house SQL database.
- [Wholesale & Distribution](https://www.actadata.co.uk/sectors/wholesale): Customer profitability, stock turn and true landed margin. Systems typically connected: ERP — Kerridge K8, Sage 200, Access Dimensions, SAP Business One; Warehouse management; Trade counter EPOS; Telesales and quoting; Carrier and haulier portals; Supplier EDI and price files; In-house SQL Server; The rebate and margin spreadsheets.
- [Manufacturing](https://www.actadata.co.uk/sectors/manufacturing): Cost to make, yield and OEE joined to what you actually sold. Systems typically connected: ERP and MRP; Shop-floor MES; SCADA and PLC historians; Quality and non-conformance; Maintenance — CMMS; In-house SQL and Access databases.
- [Omni-channel Retail](https://www.actadata.co.uk/sectors/omni-channel-retail): True margin by SKU, channel and customer, without the spreadsheet. Systems typically connected: EPOS; E-commerce — Shopify, Magento, BigCommerce; Stock and merchandising; Marketplace feeds; Payments; Ad platforms; Loyalty.
- [Legal Services](https://www.actadata.co.uk/sectors/legal-services): Case economics, WIP and cost per acquired case, settled. Systems typically connected: Practice management — Proclaim, LEAP, Osprey, Clio; Time recording; Legal accounts; Document management; In-house SQL database.
- [Customer Service](https://www.actadata.co.uk/sectors/customer-service): Cost to serve, repeat contacts and the root causes behind both. Systems typically connected: Contact centre — Genesys, Five9, Amazon Connect; Ticketing — Zendesk, Freshdesk; Telephony and IVR; Workforce management; QA and call scoring.
- [Recruitment & Training](https://www.actadata.co.uk/sectors/recruitment-training): Desk margin, time-to-fill and cohort outcomes on one definition. Systems typically connected: ATS and CRM — Bullhorn, Vincere, JobAdder; Learning management system; Timesheets and payroll; Job board and aggregator feeds; In-house candidate databases.
- [SaaS & Startups](https://www.actadata.co.uk/sectors/saas-startups): Activation, retention and CAC payback that survive diligence. Systems typically connected: Application database — Postgres, MySQL; Stripe; Product analytics; CRM; Support desk; Ad platforms.

## Writing

- [Your data team is a folder on a shared drive](https://www.actadata.co.uk/blog/your-data-team-is-a-folder) — Insight, 2026-08-10. It is called Reports, it has 240 files in it, and every one of them is a question the business asks every week. That folder is not a failure — it is a specification.
- [Nobody is drowning in data. They're drowning in reconciliation.](https://www.actadata.co.uk/blog/drowning-in-reconciliation) — Insight, 2026-08-08. Month-end isn't slow because the numbers are hard. It's slow because five people are proving to each other that their versions agree.
- [Quality data first. Then the agents.](https://www.actadata.co.uk/blog/quality-data-before-agents) — Insight, 2026-08-08. An AI output is only ever as good as what sits underneath it. Most failed AI projects are data projects that got skipped.
- [Cost, revenue, conversion, time](https://www.actadata.co.uk/blog/cost-revenue-conversion-time) — Insight, 2026-08-08. Four atomic units are enough to describe almost any operation — and to show you where the value is leaking out of it.

## Questions and answers

### What does Acta Data actually do?

We build the data layer a business needs to run on, and then the AI on top of it. Every system you own gets connected and landed in your own Google BigQuery environment as events that are recorded once and never rewritten. On top of that we build the modelled metrics, the board and regulatory reporting, and a Summary Page that shows the whole business against target. Then we wire in Claude so your team can ask their own questions, and put agents onto the repetitive operational work.

### How much does it cost?

Four options, priced by how much of the business is in scope. Discovery is a £15k one-off: the value stream map, the AI readiness review, the watch-outs and a build plan you own and can hand to anyone — there is no obligation to use us afterwards — and if you do go ahead with a build, the whole £15k is credited against it. Solving one area, usually operations, is £60k a year, billed monthly at £5k. Mapping the whole business, with safe PII-restricted self-service analytics through Claude, is £120k a year at £10k a month. Enterprise adds autonomous agents and is priced against the outcome it delivers rather than from a list. After the twelve-month build you can walk away, or keep us on for £2k a month on a rolling monthly contract so nothing falls over. For scale, one area a year is less than half a fully-loaded senior data hire at around £143k. Each tier covers a stated number of source systems and legal entities rather than a revenue band, because a large simple business is a cheaper build than a small complex one: Whole business covers one entity, one country, up to 12 source systems, and anything past that — group structures, several countries, more integration — is scoped as Enterprise.

### We are a large or multi-entity group — which tier applies?

Enterprise. The published tiers are bounded by scope rather than by turnover: Whole business covers one entity, one country, up to 12 source systems, and One area covers one value stream, up to 6 source systems. Past those limits — several legal entities, more than one country, or more integration than that — it is scoped and priced properly instead of squeezed into a tier. That cuts both ways: a large, structurally simple business often lands inside a published tier, because what drives the cost is the number of systems and the number of people who have to agree a definition, not revenue.

### Can we just buy the strategy and build it ourselves?

Yes, and the Discovery tier exists for exactly that. For £15k you get your value streams mapped, the leaks quantified, an honest read on whether your data can support AI yet, the watch-outs, and a prioritised build plan. It is written to be acted on by somebody else — your own team, or another supplier. No proprietary format, no dependency, and nothing held back to protect a follow-on sale. And it is a down payment rather than a sunk cost: if you do come back for the build, the whole £15k comes off it.

### How long before we see something?

Within a week. We connect the first source, land the events and put a real number in front of you — not a plan for a number. The layer takes shape over the first month, and the Summary Page and reporting behind it go live in months two and three. There is no six-week discovery phase that produces a document.

### Do we own the environment and the data?

Yes. Everything is built in your own Google Cloud project, inside your own secure VPC. You own the environment, the data and the models throughout — we are working in your account, not hosting you in ours. At the end there is a structured handover with everything Terraformed and documented, so you can run it without us.

### What happens to personal data?

Personal data is obscured at ingest, so it does not travel into the modelling layer or into any AI prompt. Self-service analytics through Claude is PII-restricted by design. We only ever need read access, with personal data excluded.

### Does this replace our data team?

No — it means you do not have to build one before you get value. We work alongside whoever you already have, and the point of the engagement is that your team is self-sufficient well before twelve months. Then we get out of the way and you use us where it actually matters.

### What technology do you use, and why that stack?

Google BigQuery for where the data lives, shadcn for what you look at, and Claude for who you ask. It is all Google underneath, in your own secure environment, because that stack scales, integrates with everything and lets us start immediately rather than spending a quarter on procurement and platform choices.

### Which sectors do you work in?

We work where operations are complex enough to need a real data function: Consumer Credit, Debt Management, Credit Unions, B2B Services, Wholesale & Distribution, Manufacturing, Omni-channel Retail, Legal Services, Customer Service, Recruitment & Training, SaaS & Startups. The team has held C-suite positions across marketing, operations, technology and product, so the value streams in those sectors are familiar rather than newly researched.

### Can you take over an existing BI or reporting setup?

Yes. Taking over reporting somebody else built is one of the most common ways engagements start — usually when the manual reconciliation behind it has become the bottleneck, or when the person who understood the workbooks has left.

### What do you mean by a second brain?

A layer that remembers everything the business has done — every activity, every decision, and what happened next — and can be asked about it in real time. For an individual it answers what they would otherwise have to ask the colleague who has been there fifteen years. For the business it means that judgement stops living in a handful of heads: when somebody resigns the reasoning stays behind, and when somebody joins they start with the whole history. Recommendations are generated inside your own policy rather than filtered afterwards, the priors come from decisions your business has actually made rather than from industry averages, and every recommendation is scored against what happened next so the following one is better.

### Can you prove our data retention policy is actually being followed?

That is the work. Most retention policies exist as a document and nothing else, because nobody knows which of a dozen systems hold a given record and deleting it from the CRM does nothing about the copy in the warehouse, the reporting database or a spreadsheet on somebody's desktop. We map where personal data actually lives across every connected system, express your retention rules as code against that map, run them on a schedule, and keep an audit trail of what was deleted, when and under which rule. You set the policy — we make it happen and produce the evidence that it did.

### Can you support data destruction and right-to-erasure requests?

Yes. An erasure request is the same problem as retention, one record at a time: the difficulty is not deleting, it is knowing everywhere the record exists. Once personal data is mapped across your systems, a request can be actioned once and evidenced across all of them, with a record of what was removed and when. That record is usually what an auditor or the ICO actually asks for.

### Our marketing list is old — can you tell which contacts we can lawfully email?

We can show you which contacts have an evidenced lawful basis and which do not. Every contact gets traced back to the event that created it — the form submission, its timestamp and the wording that was on the page at the time — or to nothing at all, which is the common case for anything more than a few years old. The list then splits into contacts you can stand behind and contacts to suppress. The decision about what basis you rely on is yours and your counsel's; we produce the evidence to make it with.

### What is the Summary Page?

One page showing the whole business in real time: a balanced scorecard across finance, customer, operations and people with every measure against target and RAG derived from the metric tree rather than typed in, the quarter's objectives tracked against pace, and the three things that most need attention today. Clear one and the next moves up.