Data modelling and structure
Define the core entities your business runs on — customers, products, orders, accounts — with clear keys and relationships, so records can be joined reliably instead of matched on a name field.
Data Management
Make your business data something you can actually rely on
Ufanzo brings structure, quality and ownership to business data — so reporting stops contradicting itself and the numbers in a meeting no longer need to be defended.
Why it matters
The problem before the solution.
Analytics and automation both fail for the same reason: the underlying data cannot be trusted. Customer records exist in three systems with three different spellings. Nobody can say authoritatively what counts as an active account. A field means one thing in the sales tool and something else in the finance export. Ufanzo works on that foundation — defining what the business's core entities are, where each one is mastered, how quality is measured, and who owns a decision when two sources disagree. It is unglamorous work, and it is what makes everything built on top of it hold up.
Problems addressed
Signs this is the work you need.
If several of these describe your situation, there is usually a structural cause worth addressing rather than a series of coincidences.
- The same report produces different numbers depending on who runs it
- Customer or product records duplicated across disconnected systems
- Key business terms that mean different things to different departments
- No clear owner for a data set, so nobody fixes it when it drifts
- Spreadsheets acting as the real system of record
- Analysts spending most of their time cleaning data instead of analysing it
What Ufanzo does
Data Management, in practice.
Data quality rules and monitoring
Make quality measurable: completeness, validity, uniqueness, timeliness and consistency checks that run continuously and alert when a source starts degrading, rather than being discovered during a board meeting.
Governance and ownership
Establish who owns each data domain, who may change a definition, and how a conflict is resolved. Governance here means a short, enforceable set of decisions — not a document nobody reads.
Integration and consolidation
Bring data together from the systems it currently lives in, with a documented system of record for each entity so there is a defensible answer to which source wins.
Business glossary and documentation
Write down what each metric and field actually means, how it is calculated, and where it comes from — so a new analyst can be productive without interviewing three people.
Data preparation for analytics and AI
Shape data into the clean, well-described structures that reporting, dashboards and AI workflows need, rather than leaving every downstream consumer to clean it themselves.
What is inside
The disciplines this covers.
Data Management is not one activity. These are the sub-practices it breaks into, and an engagement usually draws on several rather than all of them.
Data modelling
Defining the entities the business runs on and the relationships between them, with stable surrogate keys rather than joins on mutable fields like email or company name. Includes how history is kept, so a customer who changed segment last quarter does not silently rewrite last quarter's numbers.
Master and reference data
Resolving the same real-world thing appearing in several systems into one identity, with documented survivorship rules for which source wins on which field. Reference data - statuses, categories, codes - gets a single owned list instead of four divergent ones.
Data quality management
Making quality measurable rather than anecdotal: completeness, validity, uniqueness, referential integrity and timeliness, expressed as rules that run on every load and alert when a source starts degrading.
Governance and stewardship
Who owns each domain, who may change a definition, and how a disagreement is settled. Deliberately a short, enforceable set of decisions rather than a policy document nobody opens.
Integration and pipelines
Moving data between systems on a schedule or as changes occur, with a documented system of record per entity. Transformations are versioned, reviewable code with tests, not manual steps someone performs each month.
Access, classification and retention
Knowing which fields are personal or sensitive, who can read them, and how long they are kept. Far easier to build in at modelling time than to retrofit the week a deletion request arrives.
Business benefits
What changes for the business.
Stated as directions rather than figures. The size of the change depends on your starting point, and anyone quoting a percentage before looking at your environment is quoting a sales number.
- Reporting that reconciles across departments
- One defensible definition for each core business metric
- Duplicate and conflicting records reduced at the source
- Data quality problems surfaced early instead of discovered late
- Clear ownership when something needs to be corrected
- Analysts spending their time on analysis rather than cleanup
Where it fits
Typical engagements.
Consolidating customer records
Customer data spread across a CRM, a billing platform and a support tool is modelled, de-duplicated and given a single authoritative identity, so a customer can be viewed as one relationship.
Establishing metric definitions
Contested metrics — active customer, qualified lead, recognised revenue — are defined once, documented, and implemented consistently everywhere they are reported.
Preparing for a reporting rebuild
Before dashboards are rebuilt, the underlying structures and quality rules are put in place so the new reporting does not inherit the old contradictions.
Retiring spreadsheet systems of record
Business-critical spreadsheets are migrated into governed, structured storage with validation and access control, without losing the flexibility the team relied on.
How it works
How the data foundation is built
For readers evaluating whether this is done properly.
Work begins with a data inventory: what sources exist, what each one is authoritative for, how fresh it is, and who depends on it. That inventory is what makes it possible to say which system wins when two disagree — the question that most reporting disputes actually reduce to.
Structures are modelled deliberately. Entities get stable surrogate keys rather than being joined on mutable natural keys like email address or company name. History is handled explicitly, so a customer who changed segment last quarter does not silently rewrite last quarter's reporting. Transformations are expressed as versioned, reviewable code with tests, not as manual steps someone performs each month.
Quality checks run as part of the pipeline rather than alongside it. A load that violates a uniqueness or referential constraint fails loudly and is quarantined instead of being published, because publishing bad data quietly is more expensive than a delayed refresh. Definitions, lineage and ownership are documented in the same repository as the transformations, so the documentation moves when the code does.
Related work
What this connects to.
These areas overlap in practice, and work in one often depends on work in another.
This is the layer almost everything else rests on. Analytics inherits whatever definitions and quality exist here, which is why contradictory dashboards are usually a data management problem wearing an analytics costume. Automation depends on records resolving to one entity. Where data moves between platforms, the storage and access decisions sit alongside cloud solutions.
Data Analytics
KPI definition, dashboards and analysis that answer real business questions instead of displaying every available number.
Read moreAI Automation
Workflow automation and AI-assisted processes that cut repetitive manual work and keep people in control of decisions.
Read moreCloud Solutions
Cloud architecture, migration and operations built for reliability and scale, with cost and security considered from the start.
Read more
Let's talk
Do your reports agree with each other?
If the same question produces different answers depending on the source, the problem is structural. Tell us where the numbers diverge and we will look at why.