Solutions · Data management
Data Quality
Data quality is a measure of the state of your data: whether it is correct, complete and consistent. We put the controls in place that keep it that way, and make the exceptions visible.
What we do
- Profile critical data and agree quality rules with the business
- Automate quality checks, exception reporting and remediation workflow
- Apply tooling where it earns its place, including Experian Aperture Data Studio
- Build quality into migrations and reporting processes
Outcomes
- Quality measured continuously, not sampled once a year
- Issues caught in flight, before they reach a report or a regulator
- Reporting and analytics people are willing to rely on
- Evidence that quality controls are operating
Where quality is won and lost
Data quality measures the state of your data against characteristics such as correctness, completeness and consistency. Quality is decided at every stage: how data is collected, how it is treated while in use, how it is held at rest, and how it was structured in the first place.
Few organisations can stay on top of all their data, let alone keep it close to perfect. The work is to make quality measurable, then make the exceptions visible to the people who can fix them.
What we put in place
- Rules agreed with the business. Quality is meaningless in the abstract. We profile critical data and agree what “good” means for each field, with the people who rely on it.
- Automated checks. Rules run continuously rather than during an annual review, with exception reporting and a remediation path.
- Tooling where it earns its place. We use Experian’s Aperture Data Studio where it fits: a data management suite that lets ordinary users build sophisticated processes, including machine learning for automatic data tagging, and enrich data against curated reference sets.
- Monitoring that lasts. Controls that keep reporting on quality after the project finishes.
Quality and migration are the same problem
The success of any migration depends heavily on data quality, which is why a data-driven approach migrates early and often rather than once at the end. Treat migration as a process with no value beyond the new system and the quality problems simply move house.
Why it matters downstream
Reporting, analytics and AI all inherit the quality of what feeds them. Reconciled, traceable data is what lets a finance team stand behind a number, a risk team explain an exposure, and an executive approve an AI system on evidence. Quality is the cheapest place to fix a problem that becomes expensive everywhere else.
Common questions
Why should I care about my data?
W. Edwards Deming put it best: without data, you are just another person with an opinion. We believe decisions should be made from data, and we help organisations gain an advantage by becoming data-driven through stronger data management.
Ready to move AI into production with proof?
Leave your details and we will arrange a conversation.