Data Services
Your database, minus the duplicates, decay and chaos
Dirty data taxes everything downstream: bounced campaigns, embarrassed sales calls, analytics nobody trusts. Cleaning it is unglamorous, rule-driven work — precisely what a disciplined desk does best.
Overview
Data cleaning at Aadhya
Data cleaning is four operations done rigorously: deduplication (fuzzy-matched, human-adjudicated), standardisation (names, addresses, phones, company formats), validation (email/phone verification, dead-company checks) and completion flagging. We run them on CRMs — HubSpot, Salesforce, Pipedrive, Zoho — and raw spreadsheets alike.
CRM data cleaning is the high-stakes version: merge rules that respect activity history, ownership and pipeline objects, tested on a sandbox export before anything touches production. Cleaned lists flow naturally into enrichment — fix, then fill.
At a glance
- Operations
- Dedupe, standardise, validate, flag gaps
- CRMs
- HubSpot, Salesforce, Pipedrive, Zoho
- Method
- Fuzzy match + human adjudication, sandbox first
- Verification
- Email + phone validity checks included
- Proof
- Free 500-row sample clean with error report
- Pricing
- Per 1,000 records, fixed from sample
What you get
Built for accountability, not activity
- Merge rules you approve: which record survives, which fields win, how activity history consolidates — written before the first merge.
- Fuzzy matching + human eyes: "Jon Smith / J. Smith / Jonathan Smith @ same domain" is a judgment call; algorithms shortlist, humans decide.
- Sandbox-first CRM work: the full clean runs on an export copy; you approve the diff before production sync.
- Before/after report: duplicates removed, records standardised, invalid contacts flagged — receipts, not vibes.
- Decay prevention: optional monthly hygiene retainer so the entropy never rebuilds.
How it starts
- Sample
- Send 500 rows; we return them cleaned, with an error-taxonomy report and a fixed quote for the full set.
- Rules
- Merge and standardisation rules documented, approved by you.
- Clean
- Full run in sandbox/copy; diff report for approval; production apply.
- Maintain
- Optional monthly retainer keeps it clean.
Pricing shape
How this service is priced
Exact rates live on the pricing page — published, because serious buyers filter on it.
- Per 1,000 records
- Fixed from the sample; most cleans land here.
- CRM project
- Fixed quote for full-instance cleans incl. sandbox + apply.
- Hygiene retainer
- Monthly upkeep from ~$300/month.
Fit check
Built for some teams. Wrong for others.
Honest scoping saves both sides a month. This desk fits when:
- CRMs after years of imports, mergers and salespeople in a hurry
- Marketing teams whose bounce rates embarrass their sender reputation
- Ops teams prepping data for migrations, audits or AI projects
Probably the wrong desk if: Databases where nobody can say which duplicate should win — decide the rules, we execute them; Real-time deduplication needs (that is engineering, not operations); Compliance-driven purges needing legal sign-off we cannot give.
The Aadhya way
Cleaning is surgery on a living system: the merge that deletes a deal history is worse than the duplicate it removed. Hence the discipline — rules in writing, sandbox first, diffs approved, production last. Slow is smooth, and smooth is the only acceptable speed inside your CRM. The clients who trust us with production data are the ones who watched us refuse to touch it prematurely.
Questions buyers ask
Typically $15–$60 per 1,000 records depending on state and rules complexity; full CRM projects quoted fixed after the free 500-row sample. Verification (email/phone) is included, not an upsell. See pricing.
No — because nothing touches production until you have approved a diff from the sandbox run. Merge rules respect activity history, deal objects and ownership; the scary part is exactly why the process is sandbox-first.
By rules we write with you: most-recent-activity wins, or most-complete record wins, field-by-field precedence for conflicts. Algorithms shortlist candidates; humans adjudicate the ambiguous ones.
Yes — SMTP-level email verification and phone format/carrier checks are part of the standard clean, with invalid contacts flagged (not silently deleted; you decide their fate).
Cleaning inside the CRM’s object model: contacts, companies, deals and their relationships — dedupe that merges histories rather than orphaning them, standardisation that respects picklists, and sync-safe apply. Spreadsheet cleaning is checkers; CRM cleaning is chess.
Spreadsheets up to ~50k rows: usually under a week. Full CRM instances: 2–3 weeks including sandbox and your approval loop. The sample tells us — and you — precisely.
The hygiene retainer: monthly dedupe passes, validation refresh on aging records, and standardisation of the month’s new imports. Cheaper than annual re-surgery.
Yes — SKU dedupe, attribute normalisation, category re-mapping and spec-sheet reconciliation, same method, via the data desk.
For spreadsheets: the file. For CRMs: an export, or a least-privilege sandbox user. Production credentials only at the apply step, and revocable the moment it completes.
Every production apply is preceded by a full export snapshot, retained until you sign off — so rollback is a restore, not a prayer. In practice the sandbox-diff step catches problems before production ever sees them, which is why the snapshot has never been needed in anger.
Use one — inside our process. Tools find candidate matches; they cannot decide whether two similar companies are branches or strangers, or which mangled address is right. The judgment layer is what you are buying.
Next step
Start with a pilot, not a contract.
Describe the queue, the list or the workload. You get a written pilot plan and a fixed quote within 48 hours — and the pilot itself proves us before you commit to anything longer.