Deduplication, standardisation, validation rule design, enrichment APIs, and data profiling — turning messy source data into accurate, structured records your systems can actually trust.
Dirty data is the hidden cost in almost every data project. Duplicate customer records, inconsistent address formats, missing values in required fields, outliers from data entry errors, and unmapped reference codes - all of these cause downstream failures in analytics, ERP migrations, and machine learning pipelines.
At DynamicUnit, we approach data cleansing systematically: profile first, define rules, cleanse with code (not manual edits), validate against business rules, and enrich where source data is incomplete. We build repeatable cleansing pipelines - not one-time fixes - so data quality is maintained as new records enter the system. We also integrate enrichment APIs for address validation, company data appends, and contact verification.
Data cleansing is a critical prerequisite for successful data warehouse builds and data lake architectures. Garbage in, garbage out applies regardless of how well the schema is designed. We frequently run cleansing engagements alongside warehouse projects to ensure the curated layer starts clean.
For organisations extracting data from external sources, our data scraping pipelines include inline cleansing and deduplication - so extracted data arrives clean without a separate processing step. And when cleansing is part of an ERP go-live, our implementation teams coordinate directly with the cleansing engineers to hit cutover deadlines.
Patient records, provider directories, and clinical data cleansed for duplicate resolution, format standardisation, and regulatory compliance before system migrations.
Customer KYC data, account records, and transaction histories deduplicated and standardised for regulatory reporting and analytics warehouse loading.
Vendor masters, BOMs, and inventory data cleansed before ERP implementation - resolving duplicates and inconsistent part numbering.
Product catalogues, customer databases, and scraped market data cleansed for pricing accuracy, category consistency, and duplicate-free CRM records.
From initial profiling and deduplication through to enrichment APIs and ongoing quality monitoring - here's what we deliver.
Statistical analysis of your dataset - completeness rates, format distribution, cardinality, null patterns, and outlier identification - before any cleansing begins.
Identify and merge duplicate records using fuzzy matching, phonetic algorithms, and business-key logic - preserving the best version of each entity rather than randomly deleting.
Normalise phone numbers, dates, addresses, postcodes, currency values, and reference codes into consistent formats aligned to target system requirements.
Address missing data with context-appropriate strategies - conditional imputation, lookup defaults, predictive filling, or flagging for manual review where imputation isn't valid.
Design and apply validation rules that reflect actual business logic - referential integrity, cross-field dependencies, valid value ranges, and mandatory field requirements.
Append missing data via enrichment APIs - address validation (Loqate, Google Maps), company firmographics (Companies House, D&B), and contact verification services.
Statistical and rule-based outlier identification to surface data entry errors, unit mismatches, and corrupt values - reviewed with business owners before removal or correction.
Build automated cleansing pipelines that run on new data as it enters your system - maintaining quality continuously rather than requiring periodic manual intervention.
One-time manual data cleaning produces clean data that gets dirty again. Automated, rule-based cleansing pipelines maintain quality as data flows in. Here's how we make the difference.
We profile the full dataset before writing a single cleansing rule - understanding the actual distribution of problems, not guessing from a sample.
Cleansing logic is implemented in code, version-controlled, and documented - not in spreadsheet formulas that break and can't be reproduced or audited.
Validation rules are reviewed and approved by business stakeholders before execution - ensuring the rules reflect actual business logic, not assumptions.
Records that can't be cleansed automatically are flagged in an exception report with the reason - not silently dropped or left dirty in the output.
We build pipelines that apply cleansing rules to incoming data automatically - preventing dirty data from accumulating rather than waiting for the next cleanup project.
Cleansing activities involving personal data are conducted with appropriate legal basis, data minimisation, and retention controls - documented for compliance purposes.
We profile the full dataset - completeness, format consistency, duplicates, outliers, and referential integrity. You get a data quality scorecard across six dimensions with specific issues identified.
We define cleansing rules, survivorship logic for duplicates, and enrichment requirements. Every rule is reviewed by business stakeholders before execution - no silent assumptions.
We execute the code-based cleansing pipeline, validate output against business rules, and produce exception reports for records that need manual review. Before-and-after quality scores confirm improvement.
We deploy the cleansing pipeline to run automatically on new data - quarantining records that fail validation and feeding clean data into your warehouse or target system continuously.
Tell us where your data quality pain is - duplicate records, missing values, inconsistent formats - and we'll show you what a systematic cleansing programme looks like.
