Profiling, cleansing, matching, deduplication and continuous monitoring.
Agilus delivers enterprise data quality as both a service and a platform: we profile your data, fix what is broken — cleansing, standardising, matching, deduplicating — and then keep it clean with automated monitoring through our DM360 platform. Data Quality is a dedicated knowledge area of DAMA-DMBOK2, and the one with the most immediate, measurable payback.
What poor data quality actually costs
Research published in MIT Sloan Management Review puts the cost of poor data quality at 15–25% of revenue for a typical enterprise — in duplicated effort, missed discounts, failed campaigns, regulatory exposure and decisions made on wrong numbers. But the largest cost is quieter: when people stop trusting the data, they stop using it, and every report gets rebuilt by hand in a spreadsheet.
The six dimensions we measure
Agilus assesses data quality against the six dimensions defined in the DAMA framework:
| Dimension | The question it answers |
|---|---|
| Completeness | Is the data all there? |
| Uniqueness | Does each real-world thing exist exactly once? |
| Timeliness | Is it current enough for the decision it feeds? |
| Validity | Does it conform to the required format and rules? |
| Accuracy | Does it reflect reality? |
| Consistency | Does it agree with itself across systems? |
Every engagement starts by scoring your critical data against these six — so improvement is a measured trend, not a feeling.
What our data quality engagement delivers
Profiling and baseline
Automated profiling of your critical data sets: duplicates, gaps, format violations, referential breaks — quantified and ranked by business impact.
Cleansing and standardisation
Names, addresses, identifiers, product codes and reference data cleansed and standardised against defined rules, with every change auditable.
Matching and deduplication
Deterministic and machine-learning matching to find the duplicates exact-match logic misses, with survivorship rules that build the golden record. More on MDM.
Ruleless data quality & anomaly detection
Ruleless data quality is DM360’s machine-learning approach that detects anomalies and quality issues without pre-written validation rules — the system learns what normal looks like in your data and flags what deviates from it. This catches the errors nobody thought to write a rule for.
Continuous monitoring
Quality dashboards and alerts through DM360, so quality becomes an operational metric — watched weekly, not rediscovered annually.
Common questions
What is data quality?
Data quality is the degree to which data is fit for its intended use — measured across dimensions such as completeness, uniqueness, timeliness, validity, accuracy and consistency. It is a dedicated knowledge area of the DAMA-DMBOK2 framework, and it is managed as an ongoing operational discipline, not a one-off cleanup.
What is data cleansing?
Data cleansing is the process of detecting and correcting errors in data: fixing formats, completing gaps, standardising values, removing duplicates and resolving contradictions. Cleansing fixes the stock of existing errors; data quality management stops the flow of new ones — a serious programme does both.
What is ruleless data quality?
Ruleless data quality is an approach, pioneered in Agilus’s DM360 platform, where machine learning learns the normal patterns in a data set and flags anomalies automatically — without analysts having to write validation rules in advance. It complements rule-based checking by catching the unknown unknowns.
What is the difference between data quality and data governance?
Data quality measures and improves the data itself; data governance sets the authority and rules under which that happens — who owns the data, what standards apply, who fixes what. Quality without governance decays; governance without quality measurement is theatre. They are separate DMBOK knowledge areas that only work together.
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Related: DM360 platform · Master Data Management · Consulting overview