The unglamorous work everyone postpones — audits, deduplication, schema remediation, and SQL-heavy analysis — done offshore, documented, and delivered to Fortune 500 standards.
Data quality and cleanup is the work of making messy enterprise data trustworthy — auditing it, removing duplicates, fixing schema problems, and standardizing it — so downstream reports and migrations can be relied on. Thinklens delivers this offshore in a documented, reversible sequence: column-level audit and profiling (null rates, value domains, duplicates, orphans, and cross-system drift) ranked by business impact; deduplication with domain-tuned match rules and agreed survivorship logic, executed with full before/after lineage; schema remediation through staged migrations coordinated with downstream reports; and standardization with validation rules wired in at entry so the data doesn't decay. Senior, SQL-first analysts then run root-cause investigations, reconciliations, and ad-hoc or recurring reporting in Power BI, Tableau, or Spotfire on the cleaned data. Cleanup is scoped for what's next — SAP S/4HANA, Workday, or warehouse moves — and leaves behind the rules, gates, and monitoring that keep data clean.
A wrong number in a dashboard is rarely a dashboard problem. We work backwards from the symptom to the source — then fix it where it lives, with an audit trail of every change.
Column-level profiling across the estate — null rates, value domains, duplicates, orphans, drift between systems — summarized in a findings report ranked by business impact, not row count.
Match rules tuned to the domain (fuzzy on names, exact on tax IDs), agreed survivorship logic, and merge execution with full before/after lineage — reversible, never destructive.
Misused fields, overloaded columns, and missing constraints corrected with staged migrations — coordinated with the downstream reports and integrations that depend on them.
Reference-data alignment, unit and format normalization, and validation rules wired in at entry — so the dataset that took weeks to clean doesn't decay back in a quarter.
Once the data is trustworthy, we put senior analysts on it — SQL-first, BI-fluent, and used to presenting to stakeholders who ask "why" twice.
Root-cause analysis, cohort and trend work, and reconciliation investigations directly against the warehouse — documented queries your team keeps after the engagement ends.
One-off deep dives or scheduled reporting packs in Power BI, Tableau, or Spotfire — built on the cleaned data so the numbers agree with the systems of record.
Cleanup scoped for what's next — SAP S/4HANA moves, Workday rollouts, warehouse consolidation — so the new system goes live on data that's already been through quarantine.
Most data-quality projects fail by being framed as one-time cleanups. We leave behind the rules, gates, and monitoring that keep the data clean after we're gone.
Audit and profiling ranked by business impact, deduplication with domain-tuned match rules and survivorship logic, schema remediation via staged migrations, and standardization with validation rules wired in at entry.
Yes — merges run with full before/after lineage and agreed survivorship rules; the process is reversible, never destructive.
Yes — cleanup is scoped for what's next (SAP S/4HANA, Workday, or warehouse consolidation) so the new system goes live on data that has already been through quarantine.
Fully registered in India (GST, PAN, IEC). Transparent contracts and audit-ready processes. A reliable offshore team with proven delivery — the three things every overseas partner wants on day one.
Request our compliance packAn unfilled seat isn't neutral — every week it stays empty is a slipped deadline or a client conversation you'd rather not have. Tell us the gap before it costs you one, and we'll line up the right senior consultant, remotely, with fast ramp-up.
An overflow crunch, a long-term mandate, ongoing support, or training — tell us what you need and we'll move fast.
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