Source consolidation
We audit every data source and standardize definitions, so every department starts from the same numbers.
- Data audit and clean-up
- Shared definitions for every metric
- One source of truth across the company
Data Management
Know the number is right before you sign it off
Errors, duplicates and inconsistencies are identified before they reach a report, by rules that run on every refresh. What reaches you has already been checked.
Who this is for
Does any of this sound like your week?
Nobody buys data quality out of curiosity. It gets bought after a wrong number has cost something.
You check the report before you present it
Not because you enjoy it, but because once you didn’t. That time is a cost, and the distrust behind it is more expensive still.
You put your name on the figure
Whoever signs off a report needs to know not only what the number is, but how confident they can be in it. Today there is no way to find out.
Your team keeps fixing the same things
The same errors get corrected by hand, month after month, by the same people. Nobody has had time to ask why they keep coming back.
You have already consolidated your sources
You have one reporting base. The next question, inevitably, is how correct the data that went into it actually is.
What you’re losing now
What you’re losing to a number you cannot trust
Not the error itself. What you do to avoid it, and what you avoid doing because you cannot rule it out.
The same work, done twice
Somebody recalculates alongside the report, just to be sure. It is duplicate work that appears in no budget and in no plan.
One customer counted as three
The same account spelled three ways becomes three accounts. Every analysis built on it starts wrong, and the result still looks plausible.
The error surfaces after the decision
When it turns up in next month’s report, the decision has already been made. The correction arrives too late to matter.
What you get
What you actually receive
Six deliverables. The last one is what holds: without monitoring, quality degrades back within months.
01 Automated profiling of your sources
What data types you hold, which values occur, how complete the fields are, where the patterns break. No assumptions — we read what is there.
02 Validation rules by dimension
Completeness, uniqueness, validity, cross-system consistency and timeliness. Each rule has a threshold agreed with you, not a default.
03 A quality scorecard
A score per source and per dimension, recalculated on every run. This is how you know whether you can sign the number off — beforehand.
04 Clean-up and standardization
Duplicates are matched and merged, formats brought to one convention, missing values given an explicit rule rather than a guess.
05 A remediation plan at the source
Cleaning fixes the symptom. The plan says what has to change in the process that produces the data, so the same error does not return next month.
06 Continuous monitoring with alerts
The rules run on every refresh. When a source starts to degrade you find out then, not at the next audit.
How we work
Measure first, then fix
Nothing gets cleaned before we know how bad it is and exactly where.
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First call
Duration:
You tell us which number cost you something last time and which source it came from. That is where the list of sources to audit starts.
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Audit, rules and clean-up
Duration:
Profiling across sources, defining the rules and thresholds, then cleaning and standardizing. You end this step with the scorecard and the remediation plan.
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Monitoring
Duration: Ongoing
The rules stay active and run on every refresh. Thresholds are adjusted as the data changes.
Exact timing depends on how many sources you have and how clean they are — we agree these with you upfront.
Evidence
What changes in how you work
Before
The figure is checked by hand before every presentation, and errors are found after they have already gone into a decision.
After
The rules run on every refresh. What fails them never reaches a report, and the quality score is visible beforehand, not after.
The comparison describes what changes in how you work, not the measured outcome of any particular project.
What stops you
The questions you ask before you sign
- How much does it cost?
- The 30-minute call is free. It ends with a proposal at a fixed price for the whole project — not an hourly rate, and not an estimate that keeps moving.
- How long before we see something useful?
- First results appear in 2–4 weeks. Exact timing depends on how many sources you have and how clean they are — and we agree that before we start, not along the way.
- What happens to our data?
- It stays with you. Our tools connect to your database, read its structure and build the reporting measures there — nothing is copied or stored on our side. You get a Power BI report file with the metrics defined, which you connect to your own source; the data in it is yours and never passes through us. The exception is prediction: a model needs history, so there we keep aggregated values and model statistics, not individual records and no confidential data. Before we get any access, we sign a confidentiality agreement and a GDPR data processing agreement.
- Our systems already validate on entry. What does this add?
- Entry validation catches what is wrong inside that one system. It cannot catch what is inconsistent between two systems, or what degraded after an import — which is exactly where these rules work.
From the same stage
The rest of the foundation
Consolidation brings the data together, quality makes it trustworthy, GDPR defines what you are allowed to do with it.
GDPR & compliance
Ensure your data handling meets GDPR requirements without manual effort or external auditors.
- Personal data scanning and classification
- Data catalog and retention policies
- GDPR compliance scoring
- DSAR request handling
Next step
A 30-minute conversation, no strings attached
Tell us which decision you want to make better. We’ll tell you whether we can help, and how, concretely.