When a team stops trusting a dataset, they rebuild it by hand, and the same errors return the next quarter. Relevant corrects the records at the source, deduplicating, standardizing, and verifying them, then hands you the ruleset that holds.
Relevant finds and merges duplicate records across your systems, so each customer, product, or account appears once with the most complete details. Cleansing is the corrective layer inside the wider lifecycle our data management services cover, from ingestion to analytics.
Names, addresses, dates, and codes come into one consistent format, so every system reads each field the same way.
Each record gets checked against format rules and trusted sources, so errors surface before the data reaches a system that relies on it.
Relevant fills gaps and adds context to thin records, so your teams work from a fuller picture of each customer and account.
Customer records in your CRM come out deduplicated, verified, and complete, so sales and marketing act on contacts they trust. Relevant keeps the cleansed records flowing through CRM data integration services, so the fields stay consistent across the tools that share them.
Before data moves to a new platform, Relevant cleanses it so the target system starts from accurate, deduplicated records on its first day.
Your dataset gets profiled for duplicates, gaps, and format issues, so the scale of the work is clear before it starts, with a profiled report and error rate inside the first days.
Matching, standardization, and validation rules take shape around how your business defines a correct record, and you approve the rules before any record changes.
Duplicates merge, formats align, and errors resolve, with a full log of what changed and why, so you get a before-and-after report you can audit line by line.
Recurring cleansing rules move into the pipelines that carry your data, where our data engineering team builds and automates them, so new records arrive clean.
The ruleset, logs, and monitoring hooks stay with your team so records keep meeting standard, and we hand a repeatable process over documented in full.
Cleansing runs against rules you approve first, on a copy of your data, so you stay in control of every correction.
GDPR, HIPAA, and ISO 27001 practices govern the work, and we cleanse on secure copies so regulated data stays inside its boundary.
The specialists who build your ruleset stay to refine it.
Up to 50% faster delivery and up to 20% fewer bugs with our AI-assisted development, where AI accelerates matching and transformation and senior engineers approve every rule that ships.
A full before-and-after change log ships with the work, so each merge, format fix, and enrichment stays traceable and easy to audit.
The ruleset, logs, and monitoring hooks ship documented, so your team repeats the cleanse on new data long after the engagement ends.
Data cleansing corrects a specific dataset once, deduplicating, standardizing, verifying, and enriching the records until they meet standard. Data quality management is the ongoing work that keeps data correct after that, monitoring every new load and alerting an owner when something drifts.
Most teams need cleansing first to clear the existing backlog, then monitoring to keep it from building up again, so Relevant sequences the two, cleansing the dataset and then handing it to our data quality management services to keep clean.
Every change runs against rules you approve before the first record moves, and each edit lands in a log you can audit line by line. Relevant works on a copy, shows you a before-and-after sample, and applies changes to the full set only after you sign off. To gauge your comfort here, pick ten records you know well and check whether you could spot a wrong merge among them; if you can, you have a ready review set, and if you cannot, our team builds a validation sample with you first. You keep the log, so every merge and correction stays traceable to the rule that made it.
Yes, we can cleanse data from a legacy system or a file another vendor handed over. Relevant profiles the export first, maps its fields to how your current systems define them, then applies matching and validation to bring it up to standard. To gauge the effort yourself, open the file and check one key field, such as country or status, for how many spellings it holds; a handful means light standardization, and dozens mean the profiling pass earns its keep. If you cannot open the file easily, our team profiles a sample with you and reports what it finds.
Clean data stays clean when the rules that cleaned it keep running, so Relevant hands you the full ruleset and can wire it into your pipelines for every new load. Where you want a continuous watch on top, ongoing monitoring flags drift before it spreads. To see whether you need that watch, ask how often new records enter the dataset; a steady inflow rewards automated cleansing at the source, and a static dataset may need only the one pass. If you are unsure of the inflow, our team measures it during the assessment.
The strongest start is the dataset your revenue teams touch daily, such as your CRM contacts or product catalog, so clean data shows up where the business feels it. Relevant profiles that set, agrees the rules, and cleanses it before moving to the next. To choose it yourself, ask which dataset causes the most rework or bounced outreach; that one repays cleansing fastest. If several compete, our team ranks them with you by impact and effort.
Send us your messiest dataset, and you’ll leave the first call with its duplicate rate, its error rate, and what a clean, ready-to-use version is worth.
Product label
AI
Agriculture
