I help UK SMEs that have outgrown their spreadsheets but cannot yet trust their own numbers. My work puts one owned definition behind every figure leadership relies on: who owns each critical data set, what correct actually means, how quality is measured, and where the number came from. The result is reporting the business stops arguing about, and an ERP or Power BI estate that finally has solid ground underneath it.
I deliver practical data governance for UK SMEs that need one version of the truth: ownership, plain-English definitions, data quality rules and trusted reporting across ERP, finance and Power BI, built around how your teams actually work rather than a two-hundred-page policy nobody reads.
Finance, operations and Power BI each report a different figure because nobody owns the definition behind them.
Most SMEs do not have a data problem. They have an ownership problem. The figures exist. They sit in the ERP, in finance, in a dozen spreadsheets and in Power BI. What is missing is anyone whose job it is to say what each figure means, whether it is right, and who is accountable when it is not.
So the same conversation repeats every month. Operations quote one number, finance quote another, and nobody is actually wrong, because both pulled a valid figure from a different source using a different definition. A customer record in Infor LN means one thing to the warehouse and something else to credit control. The Power BI dashboard on the wall shows ninety-seven percent on-time dispatch, but the operations director knows the real figure is nearer eighty. None of this is a tooling failure. It is a governance gap.
I work with manufacturing and operations-led SMEs across the UK to close that gap. Not by writing a data governance policy and walking away, but by putting ownership, definitions and quality checks on the small number of data elements that actually drive your decisions. Usually that means customers, items, suppliers, ledgers and the measures on your key reports. Get those right and most of the noise disappears.
If you are weighing a data governance consultant against another data quality audit or a new reporting tool, this page sets out the practical route: senior discovery, owned definitions, and reporting the business can finally rely on.
Most SMEs do not need more tools. They need to decide which of these they are actually running.
A different definition of an active customer in every team. Quality checked by eye, at month-end, under time pressure. The ERP treated as a system of record it was never set up to be. The meaning of each field living in one person's head, with no documentation and no backup.
A named owner for every critical data set. A short, agreed definition behind every measure on the board report. Quality rules that run automatically and flag problems early. Documentation clear enough that a new starter, or an auditor, can follow it.
Good governance is mostly about people and a little about process, with technology last. The first job is to name the critical data elements, the twenty or thirty records, fields and measures your decisions genuinely depend on. Then we agree, in plain English, what each one means and who owns it. That single step removes most of the disputes, because an active customer or an on-time dispatch finally has one definition rather than six.
Ownership is what makes governance stick. A field without an owner stays dirty forever. A field with a named owner, a clear definition and a simple quality rule gets cleaner every month, because somebody is accountable for it and knows what good looks like.
A deliberately lightweight method for SMEs, not an enterprise framework.
We identify the data your business actually runs on: the records, fields and measures behind your key reports and decisions. Not every field in the ERP, just the ones that matter.
For each critical element we write a short, plain-English definition that finance, operations and leadership all sign off. This is where most of the arguing gets done, and where most of the value is created.
Every critical data set gets a named owner and a deputy. Owners are accountable for quality, not expected to fix every record themselves.
Simple, automated checks that catch duplicates, missing values, broken references and entries that break the definition, before they reach your reports.
Customers, items and suppliers deduplicated and standardised, so the ERP and Power BI sit on one clean set of master records rather than three overlapping ones.
Lightweight controls and a short recurring review, so governance survives staff changes and the next system implementation rather than fading out.
The practical differences clients notice once ownership and definitions are in place.
Month-end stops being a reconciliation battle. Finance, operations and the board read from the same figures because they share the same definitions.
Migrations, upgrades and Power BI projects stop being derailed by dirty data, because the critical data is owned and clean before the project begins.
When a customer, an auditor or an ISO 9001 assessor asks where a number came from, you can show the definition, the owner and the lineage without a scramble.
Fewer rebuilt reports, fewer final-version-three spreadsheets, fewer hours lost to arguing about whose number is right.
Governance matters most where the stakes are highest, and for most of my clients that means the ERP. Infor LN, Baan, Dynamics and similar systems hold years of transactional history across sales, operations and finance, and they only stay trustworthy if somebody owns the master data and the definitions sitting behind them. I work inside Infor LN and Baan environments regularly, so the governance work is grounded in how these systems actually behave rather than in theory.
It connects upstream and downstream too. Before an ERP data migration, governance is what decides what is clean enough to move. After go-live, it is what stops the new Power BI estate from reproducing every disagreement you had in the old spreadsheets. For the reporting layer itself, the practical Power BI guides on fiscal year setup, fiscal YTD measures and sorting months correctly cover the modelling details that often make finance dashboards feel wrong even when the data is clean.
The things SMEs usually want to know before they bring governance help in.
Works with your team to identify the data your decisions depend on, agree what each piece means, name an owner for it, and put lightweight quality checks in place. Day to day that is a mix of workshops with finance and operations, writing short data definitions, profiling the ERP and Power BI data to find where it breaks the rules, and putting simple controls in to keep it clean. The deliverable is not a policy document. It is governed, owned data and reports you can trust.
Data management is the broader discipline of looking after data day to day: storage, access, master data and quality. Data governance is the layer above it, the ownership, accountability and agreed definitions that decide what good looks like. In practice I do both for SMEs, because at this scale they cannot really be separated. You can read more on the data management consultant page.
Before, ideally. The single biggest cause of painful ERP go-lives is dirty, undefined data moving from one system to another. A few weeks of governance work ahead of a migration decides what is clean enough to move, what needs fixing first, and who owns it in the new system. Doing it after go-live is possible but more expensive, because you are then fixing live data while the business runs on it.
Most clients see the first result inside four to six weeks, usually the end of one recurring argument about a number that never reconciled. Full ownership and quality controls across the critical data sets typically takes three to six months for an SME, embedded around normal operations rather than run as a separate project.
Usually the opposite. Smaller businesses feel bad data more sharply, because there is no spare capacity to absorb the rework. Governance for an SME is deliberately lightweight: a handful of critical data sets, named owners and simple checks. It is not an enterprise framework. It is the minimum needed so the numbers you already have are actually usable.
It depends on how many critical data sets are in scope and how far the existing data has drifted, but most SME engagements start with a short, fixed-scope review of the data behind your key reports, then move to a phased plan. The best first step is a thirty-minute call to look at where your numbers do not reconcile today.
Governance is usually part of a wider piece of work. These are the services it most often sits alongside.
Based in the Wirral, supporting manufacturing and operations-led SMEs across Merseyside, the North West and the UK.
If leadership is tired of arguing about whose number is right, let's look at the critical data behind it. A thirty-minute call is enough to see whether governance is the missing piece.
Book a Data Governance CallTell me what needs to migrate, what no longer reconciles, or which report the business no longer trusts. If there is a fit, we start with a 5 to 10 day ERP Data Readiness Review.