Every hour your analysts spend reconciling numbers is an hour they are not acting on them. Relevant sets the profiling, rules, and monitoring that keep data correct at the source, so accuracy holds as volumes grow and the manual reconciliation disappears.
Relevant profiles your critical datasets to show where accuracy, completeness, and consistency stand today, and where the biggest gains sit. Quality is the measurement layer inside the wider lifecycle our data management services cover, from ingestion to warehousing.
Clear, testable rules define what good data looks like for each field, so checks catch problems the moment records arrive. The ownership and policies behind those rules come from your data governance program, which decides who sets the standard for each domain.
Continuous monitoring watches freshness, volume, and distribution on every load and alerts the right owner before a wrong number reaches a dashboard, checks Relevant builds into the pipelines our data pipeline development team maintains.
Executive scorecards turn quality into numbers leadership acts on, and the trusted metrics they surface feed the dashboards behind our data analytics work.
When monitoring flags an issue, a clear workflow routes it to the owner, tracks the fix, and confirms the data meets standard again. For large-scale correction across whole datasets, our data cleansing services handle deduplication, standardization, and enrichment in one pass.
Every new source and pipeline clears quality gates before it reaches production, so fresh data meets standard from its first load.
Your priority datasets get profiled and scored, so the biggest gaps surface with a number attached, delivered as a scored baseline inside the first two weeks.
Fixes, owners, and quick wins arrive as a sequenced plan tied to the data your business relies on most, ready to fund and staff.
Validation rules and thresholds take shape with the owners who set the standard for each domain, so your teams share one definition of good data.
Quality checks and alerts move into the platforms that carry your data, where our data engineering team builds and automates them, so issues surface before dashboards do.
Scorecards, alerts, and remediation workflows keep accuracy high as your data grows, and we hand the program over documented in full.
Every engagement starts with a phased roadmap, each phase priced and dated, and no phase starts without your sign-off.
Recognized security and compliance practices are built into how we handle your data.
The specialists who build your monitoring stay to keep it tuned.
Our AI-assisted development framework speeds rule and pipeline work with up to 20% fewer bugs, and every release still passes senior code review.
Most clients continue with another dataset or domain after the first engagement.
Each rule, monitor, and scorecard ships documented, so your team runs the program on its own.
Relevant builds the profiling, rules, and monitoring behind data your teams can trust.
Data governance sets the rules and names the owners: who decides what good data means for each domain, and what the policies are. Data quality management runs the day-to-day work those rules describe, profiling the data, checking it on every load, and fixing what falls below standard. A quick way to tell which you need first is to ask whether your teams already agree on the definition of a clean customer record; if they do, quality monitoring puts that definition to work, and if they do not, governance sets the definition first. If you are unsure where your gap sits, our team maps both layers with you, nothing to prepare beforehand.
Relevant scores data against six dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness. Each dimension becomes a testable rule, and the results roll up into a scorecard by domain, so leadership sees quality as a trend. To gauge your own starting point, pick one core table and estimate what share of rows carry a missing or malformed value in a key field; a low share shows healthy footing, and a high one shows where profiling pays off first. If you cannot estimate it yet, our team profiles a sample dataset with you to produce the first number.
Cost tracks with scope, and the biggest driver is how many datasets and pipelines you bring under monitoring. Count the tables your reports and models depend on most, and the pipelines that feed them; that count sizes the engagement more than any other factor. Most clients reach a funded quality roadmap within 14 days of kickoff, then phase monitoring by domain so trusted data arrives early. When you have that list of datasets and pipelines, our team sizes the work with you on a short call, so contact us once it is ready.
The strongest start is the data behind your most-watched report or model, ahead of a company-wide cleanup, so trust returns where people feel it first. Relevant profiles that dataset, sets rules, and stands up monitoring there before scaling to the next domain. To choose it yourself, ask which dashboard your leaders question most; the data feeding it is where quality work earns trust quickest. If several feel equally shaky, our team scores them with you against business impact and effort.
A failed check raises an alert to the owner of that data, opens a tracked issue, and holds the number back from certified dashboards until the fix is confirmed. The workflow records the root cause so the same problem earns a permanent fix and stops recurring. To see how ready you are for this, ask who receives the alert today when a nightly load doubles in size; a named owner shows a healthy setup, and an empty answer shows where to start. If no one owns that alert yet, our team helps you assign owners as part of the rollout.
