Lake On Rails: data governance software that runs on the people you already have
Your platform holds the data. Your department heads hold the decisions. Lake On Rails puts each one (who owns this dataset, which number is trusted, how long it is kept) to the person who can answer it, records it against their name, and shows you whether it is still holding a year on. It never moves or reads the contents of your data. The rules arrive already drafted, so your department heads approve an operating model rather than writing one.
Four signs it is already costing you
None of these is a technology problem, which is why buying more technology does not fix them. Each one is a decision nobody was asked to make: who owns a number, which source is current, what happens when the person who knows is away.
Two reports, two numbers
Revenue in the sales dashboard does not match revenue in the finance pack. Both are "right". They were built from different extracts with different rules, and none of those rules is written down anywhere but inside the files. Nobody owns the definition.
One person's leave delays month-end
One analyst knows which source is current, why each report exists and where the spreadsheet that corrects the dashboard lives. When they are away, reporting slips. When they resign, the notice period becomes the deadline for writing it all down.
Fabric or AWS, and a partner pushing one
Three implementation partners will give you a platform opinion for free, and each is confident. None of them will tell you who should own each dataset once it lands, or what "trusted enough to report on" means for your business.
Someone asks you to prove a number
A lender, an auditor or a customer's due-diligence team wants to see how a figure was produced and who approved access to their data. You can probably reconstruct the answer. You cannot show it.
Your cloud platform answers half of this
Microsoft Fabric and AWS give you a good catalogue: schemas, lineage, permissions, refresh times. All of it is written for the two or three people in your business who work with data for a living. A catalogue can record the shape of a table. It cannot record a judgement nobody has made yet. Lake On Rails adds nothing to the rows the platform already covers, so you do not pay twice.
What Fabric and AWS answer
- Where your data lives and how it is stored
- Pipelines and processing
- Permissions and identity
- A technical catalogue: schemas, lineage, refresh times
Solved. Nothing here to pay for twice.
What still needs a person to decide
- Who is accountable for each dataset and report
- Which version is the trusted one, and the rule for promoting it
- What counts as sensitive, and how long it is kept
- The written procedure, and whether it was actually followed
Recorded against a name and a date, so you can show it was decided.
What a data operating model records, and why you do not start from a blank system
Every tool in this category ships you an empty template. Here, your one analyst does not author an operating model. They approve one, and change the bits that are wrong for you. Every screenshot below is the real product with sample data.
A register of every dataset and report, with a name against each
Owner and steward by name. Where it comes from, which department it belongs to, how sensitive it is, how long you keep it, who uses it and which procedures apply. Filter by coverage gaps to see what still has nobody against it, or bulk-import what you already hold in a spreadsheet.
Each record carries a six-point readiness check: owner assigned, steward assigned, classification set, retention defined, description written, consumers identified. Six ticks means the analyst's knowledge about that dataset is now the business's. Trust tiers sit alongside, with written criteria and a promotion rule, so "the version you report from" is a fact rather than an opinion.
Seven procedures, written and ready to edit
Lake On Rails ships the procedures rather than a template: onboarding a new dataset, classifying data, granting and reviewing access, promoting between tiers, handling a schema change, retiring a dataset, and running the periodic review. Each has a purpose, a scope, a named role per step and a version history.
They are yours to edit, and editing an approved procedure withdraws its approval until someone signs it off again. A pre-filled responsibility matrix says who is responsible, accountable, consulted and informed for each activity, so cover is a decision made in advance rather than a scramble. Read one procedure and you will know inside a minute whether it fits your business.
One score, six parts, and a page for the board pack
Ownership coverage, procedure sign-off, responsibility matrix completeness, classification coverage, platform gap closure and training completion, averaged into one number from 0 to 100. Snapshots are taken weekly, so the trend is real rather than remembered. It goes down when something slips, which is the point.
Alerts fire when a dataset lands without an owner, a procedure's review date passes or a role goes unstaffed. The executive summary turns the same data into one page for the board: a score, five measures and three plain-English talking points generated from current state.
Above Fabric, above AWS, and useful before either
The capability map shows, for each platform, what it covers natively and what still needs a human decision recorded. Switch the tab from Fabric to AWS and the right-hand column is the same: the gaps are about your business, not your cloud.
Connect a platform and Lake On Rails reads technical metadata (dataset names, schemas, owners, refresh times) from Microsoft Fabric and Purview, or from the AWS Glue Data Catalog, on a schedule. It never moves, transforms or stores the contents of your data. Or run it with no connection at all, on metadata you enter yourself. If the platform is still to be built, Scorchsoft's data lake development team works on both.
An audit trail that answers "prove it"
Who changed a classification, from what to what, when, and on whose authority. Field-level, attributed to a person, and append-only: the application has no edit or delete path, every change writes a new event and nothing is overwritten. Filter by entity, action, person or date, and export to CSV.
Approvals for access requests, dataset onboarding, schema changes and tier promotion run through the product rather than through email. The request goes to the person the responsibility matrix says should decide it, they decide in a browser in a minute, and the decision, the person and the time are recorded. On Professional and above, a REST API and MCP server let an AI assistant draft the dataset descriptions nobody has a fortnight to type, as proposals a person still approves.
Three plans, published in full
A business your size should not have to book a call to find out whether it can afford something. All prices are in pounds sterling, exclude VAT and are a 12-month commitment. Foundations and Professional can be paid monthly instead (£780 and £1,750 a month). Consulting (discovery, implementation, training workshops and a quarterly review) is optional and priced separately.
| What you get | Foundations: £8,000 a year | Professional: £18,000 a year | Enterprise: £30,000 a year |
|---|---|---|---|
| The question it answers | "Which number is right?" | "What happens when she's on holiday?" | "Can you show me?" |
| Dataset register with named owners and stewards | ✓ | ✓ | ✓ |
| Trust tiers, sensitivity and retention rules | ✓ | ✓ | ✓ |
| Roles and the responsibility matrix | ✓ | ✓ | ✓ |
| The seven written procedures, with approvals and versions | ✓ | ✓ | ✓ |
| Health scoring and maturity roadmap | Basic scoring | ✓ | ✓ |
| Approval workflows: onboarding, access, schema change, promotion | ✗ | ✓ | ✓ |
| Training courses, role guides and learning paths | ✗ | ✓ | ✓ |
| Audit trail exports, REST API and MCP server | ✗ | ✓ | ✓ |
| Single sign-on with your own identity provider | ✗ | ✗ | ✓ |
| Compliance reporting and quarterly evidence packs | ✗ | ✗ | ✓ |
| Custom workflow templates | ✗ | ✗ | ✓ |
| Platform connections (Fabric / Purview, AWS Glue) | 1 | No limit | No limit |
| People with a login | Up to 10 | Up to 50 | No limit |
| Onboarding included | One-hour call | Half-day kickstart | Two-day assessment and an account manager |
Built for businesses with one to five people who touch data
Roughly 50 to 1,000 people. Power BI or similar on application databases, perhaps some object storage and a couple of ETL jobs. Weighing Microsoft Fabric against AWS, or about to have that decision made for you. The enterprise governance platforms are built and priced for organisations with a governance team you do not have. That is the business this was built for.
It is not for you, yet, if you have three reports and one analyst (a spreadsheet is honestly still enough), or if you already have a data team and a governance lead (we would be a thin layer over work you have done). We will say so in the first conversation.
It fits you if:
- You have somewhere between twenty and a few hundred datasets and reports
- One to five people touch data, and one of them can carry half a day a week
- Somebody above that person thinks this is a real problem
- You have a date on the calendar this has to be ready for
Five steps, and the first two are free
Book a free consultation
Forty-five minutes with you and whoever runs reporting today. We show the product against your own examples and give an honest answer about whether it is worth doing at all. "Not yet" is a normal outcome.
A trial workspace, if you want one
Arranged after that conversation, on Foundations or Professional. Pre-populated and set up with you rather than self-served, so you are not staring at an empty system.
Discovery, if it is worth doing
Fixed fee, agreed before it starts. You end up with a register of what you actually have, a named owner proposed against each line and a one-page definition of your trust tiers. Yours in open formats whether or not you buy the platform.
The platform
Foundations, Professional or Enterprise, with your operating model already in it. A written proposal first, then set-up and onboarding. Invoiced, not card-billed.
The quarterly review, if you want it
We run it with you, walk through the score and what moved it, and report to your sponsor. It is the thing that stops the drift.
See Lake On Rails on your own examples
Bring the person who would carry it. A demo to a sponsor alone tends to produce agreement; a demo with the person who runs reporting produces a decision. The first conversation is free, and a person replies, usually within one working day.
Questions your team will ask
Straight answers. If yours is not here, ask us and you will get the same kind of answer.
Lake On Rails is data governance software that records the data operating model a mid-market business runs on: who owns each dataset, what the rules are, and how you prove it. It is the organisational layer that sits above Microsoft Fabric or AWS, or ahead of choosing either.
Yes, and that is the most common situation. Most of the businesses it was built for run Power BI or similar on their application databases and are weighing Microsoft Fabric against AWS. The decisions recorded here (who owns each dataset, what counts as trusted, what is sensitive, how long you keep things) are true whichever platform you choose, and they are the input that decision needs. Agreed before a migration, they make the build smaller. Retrofitted after one, you are asking an implementation partner to guess.
No. A catalogue discovers what exists, captures lineage and classifies columns, and Purview, DataZone and the rest do that well. Lake On Rails holds what a catalogue does not: a named accountable owner per dataset, a responsibility matrix, written procedures with approvals, and a measure of whether any of it is happening. If you have a catalogue, keep it. The register can read from it.
No. It never moves, transforms or stores the contents of your data. Where you connect it to a platform it reads technical metadata only: dataset names, schemas, owners and refresh times, from Microsoft Fabric and Purview or from the AWS Glue Data Catalog. You can also run it with no connection at all, on metadata you enter yourself or import from a spreadsheet.
No. It is built for a business where one person is most of the data team and has this as a tenth of their job. What it does need is that one person carrying roughly half a day a week, and a sponsor who cares. If nobody will carry it, it will not work, and we would rather say so up front.
Whoever runs reporting: about two days a week for six weeks, then half a day a week. Each data owner: ninety minutes up front, then about an hour a month. The sponsor: an hour a month at first, then an hour a quarter. For most businesses this size that is bigger than the invoice, and it is the honest reason these programmes stall.
Foundations at £8,000 a year, Professional at £18,000 a year and Enterprise at £30,000 a year, excluding VAT, each a 12-month commitment. Foundations and Professional can be paid monthly instead, at £780 and £1,750 a month. Enterprise is annual only. There is no free tier; the first conversation is free. Consulting is optional and priced separately, with the ranges published on lakeonrails.com.
Not yet. Lake On Rails is new and has no customer references, so you would be early. Early gets you the founder on your discovery, a named line to the people building it, a say in what comes next, and your operating model in open formats whether or not you stay. Scorchsoft can give references for the custom software it has built since 2010, and will say plainly that those are references for Scorchsoft, not for this product.
One analyst. Nine definitions of "revenue".
Scorchsoft has built software for other people's businesses since 2010, and the same conversation happened at the handover of every data platform: "who owns this number now?" Nobody had written it down. Every report had its own model, because reusing someone else's was harder than starting fresh, and the definitions lived inside the files and inside one person's head.
The tools that fix that were built for companies with a governance team and a six-figure budget. Our customers had one to five people and a spreadsheet. So we turned the operating model we kept writing in Word documents into a product, with the procedures already in it, and priced it for the businesses we actually work with.
It is new, and we would rather you knew that from us than found it out in month four. Sixteen years of custom software, Cyber Essentials certification and a 4.9 Google rating are facts about Scorchsoft, not proof of this product. They do mean the company behind it will answer the phone in year four. If you write to us, a person replies, and on anything that matters that person is Andrew Ward, the founder. Read more about Scorchsoft or see the work we have delivered.
Building for teams across healthcare, logistics, motorsport and manufacturing
Need help building your ideas?
Tell us where you're headed and we'll come back with our thoughts, a realistic plan and a rough cost estimate. Scorchsoft is a UK-based team of app, portal and AI developers, working in-house from Birmingham's Jewellery Quarter.