Data Consultant UK explains practical Digital Adaption support for UK SMEs, with a focus on data quality, ERP migration readiness, reporting trust, automation or delivery where it fits the page.
Data Consultant UK explains practical Digital Adaption support for UK SMEs, with a focus on data quality, ERP migration readiness, reporting trust, automation or delivery where it fits the page.
I am a UK data consultant for manufacturing and operations-led SMEs that have outgrown spreadsheets but cannot yet trust their own numbers. The work is practical, not theoretical: clean the data, own the definitions, build the reporting, govern the migration, and make the figures leadership relies on actually stand up. Fifteen years of ERP transformation, the £4.5m Infor LN programme, aerospace-grade discipline.
Book a data risk call View all servicesPractical data consulting for UK SMEs: turn fragmented, unowned, unreliable data into governed reporting the business can make decisions on. Not a data scientist selling algorithms. A consultant who has actually delivered the data work.
Digital Adaption Control Room / Data consultancy review Live data modelLeadership view
No one owns the data High Reporting no one trusts 11 Definitions conflict 8 Migration risk Open Data quality unknown 6Sources involved
If leadership argues about the figures every month, the data is the problem, not the people.
Manufacturing SME Data consulting Data confidence score 42 before review Unowned fields 36 Conflicting defs 8 Reports at risk 11 Duplicate records 2.8k Consultancy review flow Practical, not theoretical 01 Find the gapsOwnership, quality, trust 02 Own the definitionsOne agreed meaning 03 Fix the dataClean, deduplicate, govern 04 Build trustReporting that matches Pain points diagnosed Finance and operations quote different figuresHigh No one owns the customer or item master dataMed Power BI does not match the ERPOpen Data domain health CUST ITEM SUPP LEDG BOM ORD PRICE STK EDI BI FX CRMData review 5 to 10 days
Output owned definitions
Risk reduced
Ownership Who owns each field
Definitions One meaning per measure
Quality Rules and checks
Critical data list XLSX
Data definitions DOCX
Quality rules XLSX
You do not need a data scientist. You need someone who has actually cleaned, migrated, governed and reported on data in a real manufacturing SME, and made the numbers trustworthy. That is a different job, and it is the job I do.
Most SMEs that call me have the same picture. Data exists everywhere, in the ERP, in finance, in a dozen spreadsheets, 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 arguments repeat every month, and nobody is actually wrong, because both sides pulled a valid figure from a different source using a different definition.
The fix is not a tool. It is not a dashboard. It is ownership, definitions, quality rules and reporting built on governed data. Practical, unglamorous, and the difference between a business that trusts its numbers and one that argues about them.
The practical work that turns unreliable data into trusted reporting.
A data consultant for an SME is not building machine learning models or designing data lakes. The work is simpler and harder: finding the critical data the business runs on, agreeing what each piece means in plain English, naming an owner for it, putting quality checks in place, and building reporting the business can finally rely on.
That means profiling the ERP and Power BI to find where the data breaks. Writing short data definitions that finance, operations and leadership all sign off. Assigning owners so a field without an owner stops being dirty forever. Deduplicating master data so customers, items and suppliers exist once, not three times. And connecting Power BI to clean, governed source data so the dashboard matches the ERP, every time.
Real credibility. Fifteen years of ERP transformation for manufacturers, including Infor LN and Baan. The flagship was a three-year, £4.5m consolidation of four legacy ERP systems onto one Infor LN cloud instance for a 220-user manufacturing group, delivering over £1m in additional revenue and £300k in annual savings. Sectors span engineering, fabrication, industrial products and aerospace. MSc Digital Transformation and IT Strategy, Microsoft PL-200, accredited ISO 9001 Lead Auditor. Read the full background.A different definition of every measure in every team. Quality checked by eye at month-end. The ERP treated as a system of record it was never set up to be. Power BI dashboards that look impressive and show the wrong number. Knowledge of what each field means living in one person's head.
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. Master data cleaned and standardised. Reporting that matches the ERP because it is built on the same governed definitions.
A practical method for SMEs, built on fifteen years of manufacturing ERP and data work.
1The twenty or thirty records, fields and measures your decisions actually depend on. Not every field in the ERP.
2For each critical element, a short, plain-English definition that finance, operations and leadership all sign off.
3Every critical data set gets a named owner and a deputy. Owners are accountable for quality.
4Simple, automated checks that catch duplicates, missing values and broken references before they reach your reports.
5Power BI and operational reporting built on governed definitions, so the numbers finally match and the arguing stops.
The practical differences once ownership, definitions and quality are in place.
Month-end stops being a reconciliation battle because finance, operations and the board share the same definitions.
Customers, items and suppliers deduplicated and standardised, so the ERP and Power BI sit on one clean set of records.
ERP migrations, upgrades and Power BI builds stop being derailed by dirty data, because the critical data is owned before the project starts.
When an auditor or ISO 9001 assessor asks where a number came from, you can show the definition, the owner and the lineage.
What UK SMEs usually want to know before bringing in data consulting help.
Finds the data your decisions depend on, agrees what each piece means, names an owner, puts quality checks in place, and builds reporting the business can trust. Day to day that is a mix of workshops with finance and operations, writing short data definitions, profiling the ERP and Power BI, and putting simple controls in to keep the data clean.
For most SMEs, a data consultant. A data scientist builds predictive models and algorithms. A data consultant cleans, governs, owns and reports on the data you already have. If your numbers do not reconcile, your master data is messy, or your Power BI does not match the ERP, you need a consultant, not a scientist.
Data consulting is the umbrella. Governance is the ownership and definitions layer. Quality is the profiling and cleansing layer. Management is the broader operating model. In practice I do all of it for SMEs, because at this scale they cannot be separated. The governance and quality pages go deeper on each.
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.
Most engagements start with a short, fixed-scope review of the data behind your key reports, then move to a phased plan. A thirty-minute call is usually enough to scope it.
Data consulting is the umbrella. These are the areas it most often breaks down into.
Data governance consultant Ownership and definitions behind the numbers Data quality consultant Profiling, cleansing rules, duplicate handling Data management consultant The wider operating model Business intelligence consultant Reporting the business trustsBased 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 consulting is the missing piece.
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