Turning scattered data into exit value for a PE-backed hospitality tech company
We took a business whose data was spread across three clouds and half a dozen systems, and built it into a single, governed, board-ready warehouse that makes the company easier to run and easier to sell.
- Offering
- Data enablement
- Sector
- Hospitality technology, PE-backed
- Company size
- 150+ people
The situation
The client is a hospitality technology business with more than 150 staff, backed by private equity. Like a lot of fast-growing companies, it had grown its systems faster than its data.
The numbers a buyer or a board would ask for lived in different places. Bookings and guest data sat in one cloud, the e-commerce platform in another, payments in a third. Getting a straight answer to "how much did we make last month, by product" meant someone exporting spreadsheets and stitching them together by hand. Different people used different definitions, so the same question could come back with two different answers.
For a PE-backed company this is more than an inconvenience. When the data is messy, every board pack takes longer, every diligence request is a fire drill, and the story the numbers tell is harder to trust. That drags on value.
What Epoch was asked to do
Build the data foundation the business should have had all along. One place the numbers live, defined once, that anyone can query, that stands up to scrutiny, and that keeps working after we hand it back.
There was a second, quieter requirement that mattered just as much. The business handles guest data, and some of that data is sensitive. Any warehouse we built had to treat that properly from day one, not bolt it on later.
How we approached it
We built the warehouse in layers, so raw data comes in one end and clean, trustworthy numbers come out the other.
Raw data lands first, exactly as the source systems send it, with nothing changed. From there we clean it, fix the quirks every source system has, and line the definitions up so they agree with each other. Then we model it into a query-ready layer that the business intelligence tools and the wider team read from. Analysts only ever touch the top, clean layer, so they are not exposed to the mess underneath.
The work fell into seven workstreams that we ran in parallel:
Ingestion & connectivity
Getting data securely out of systems spread across three clouds and into one place.
Modelling & transformation
Turning raw tables into a clean, dimensional model the business can actually use.
Data quality & testing
Automated checks so a wrong number fails loudly instead of quietly reaching a board pack.
Governance & sensitive data
Classifying every field, and protecting personal and health-related data properly.
Cataloguing & documentation
Writing down what every table and metric means, so knowledge does not live in one person's head.
Platform & delivery pipeline
The plumbing that lets changes ship safely and repeatably.
Insight & reporting
The dashboards that turn all of it into something a board can read.
The parts most people skip
Plenty of firms can move data from A to B. The difference shows up in the parts that are easy to skip and expensive to get wrong.
We defined the numbers before we reported them
Revenue, gross booking value, an active customer — each has a precise definition, signed off by someone in the business, written down, and used everywhere. That is why two people asking the same question now get the same answer.
We tested the model, not just built it
Every core table has automated checks that confirm it means what it claims to mean. If something breaks upstream, the checks catch it before the number reaches anyone — because a wrong answer that looks right is far more dangerous than an obvious error.
We wrote down how the data connects, end to end
For every reported figure you can trace it back through each step to the exact source it came from. That matters for trust, and when a source system changes.
We treated sensitive data as a first-class problem
We classified every field, separated out anything personal or health-related, and made sure that data is only ever used where there is a clear and lawful reason. That is the difference between a warehouse that passes a security review and one that becomes a liability.
Why this protects and grows exit value
Everything above adds up to a simple commercial outcome.
A buyer running diligence on this company now finds organised, documented, governed data instead of a pile of spreadsheets and caveats. That builds confidence, and confidence supports price.
The board gets numbers it can trust, sooner, so decisions are made on fact rather than on the last version of a spreadsheet someone happened to have open.
And the data itself, once it is clean and joined up, becomes something the business can build on, not just report from. The organised data is an asset in its own right.
Organised data is not a back-office nicety. For a PE-backed company it is part of the value of the business, and getting it right protects and grows what the company is worth at exit.
How we work
We build to hand back. The warehouse is documented so the client's own team can run it, the conventions are written down rather than carried in someone's head, and we design the platform to be repeatable, so bringing in the next data source or standing up the next report does not start from scratch.
The result is a business that understands itself better, and is worth more because of it.
Organised data is part of your exit value
Epoch helps PE-backed companies maximise their exit value through AI and data. We get a company's data organised and fit for purpose, so the business can make better decisions and stand up to scrutiny.
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