Data cleansing
Data cleansing: turn a dirty database into a reliable asset.
Data cleansing is the process of cleaning, standardizing, deduplicating, validating and enriching a company's master data to make it unique, accurate and reliable. 4MDG cleanses material, supplier and customer data with AI and more than 600 public sources — and installs the governance that keeps bad data from coming back.
In just a few days you see the duplicates, inconsistencies and risks hidden in your database. No commitment required.
Trusted by 160 clients
Integrations
What we cleanse
Three cleansing fronts, with individuals and legal entities in the first two.
Customer data cleansing
Individuals and legal entities
Identity validated against public registry databases, registration status normalized, address and postal code standardized, verified contacts and duplicates merged into a golden record. A database ready for billing without rejections.
Customer onboarding →Supplier data cleansing
Individuals and legal entities
Business registration status, ownership structure, tax regime, certificates, sanctions lists and verified bank details. Duplicate suppliers unified and irregular records flagged before payment.
Supplier qualification →Cleansing of materials, products and services
The entire catalog, item by item
Rule-based standardized descriptions, attributes by family, unit of measure, packaging, product classification codes and UNSPSC, translation and deduplication. Covers materials, MRO, resale products and service catalogs.
Material standardization →
You can start with a single domain: the diagnostic shows which one delivers the most return.
The invisible cost
Your dirty database is already costing you — it just isn't on the invoice.
The same customer registered three times. A supplier with no validated registration. "Screw", "scr." and "SCREW M6" as different items.
The result shows up as duplicate purchases, wrong payments, stockouts, tax fines and reports nobody trusts.
US$ 12,9M
is the average annual cost of poor-quality data per company. Gartner.
up to 25%
of annual revenue is what incorrect data can consume. 4MDG reference.
Bad data stalls ERP, CRM, BI — and now AI
A model trained on dirty data doesn't make fewer mistakes: it fails at scale, with the appearance of certainty.
A diagnostic on a sample of your database, with nothing to install.
Concept
What is data cleansing?
Data cleansing, also called data hygiene or data scrubbing, is the set of techniques that corrects and organizes a master database: it removes duplicates, standardizes descriptions and formats, fills in missing fields, validates information against official sources and eliminates invalid records.
The goal is the golden record: a single reliable version of each material, supplier and customer, published to every system that consumes that data.
Terminology
Scrubbing, deduplication and enrichment: what's the difference?
Scrubbing
Corrects and standardizes what is wrong or off-standard: formats, descriptions, units, required fields left blank.
Deduplication
Identifies and merges repeated records into a single golden record, with similarity scores and survivorship rules.
Enrichment
Completes and updates records with data from reliable sources: tax status, CNAE, address, certificates, technical attributes.
At 4MDG, all three happen together and continuously — not as a one-off cleanup that unravels the following month.
Scrubbing, deduplication and enrichment in the same flow.
PDM and PDS
There is no clean database without standard descriptions.
PDM and PDS are the descriptive standardization disciplines that make cleansing last. Deduplicating without standardizing treats the symptom: next month the same item returns under another name.
PDM
Descriptive standardization of materials
Describing each item in a unique, structured way: taxonomy by family, attribute templates, short and long descriptions generated by rule, unit of measure, packaging and tax classification. It's what makes items findable in search, prices comparable and duplicates visible before they are created.
See the PDM page →PDS
Descriptive standardization of services
The same discipline applied to the service catalog: clear scope, unit of measure, measurement criteria and tax linkage. Without it, "preventive maintenance" becomes thirty records, each with its own price and terms.
See the PDS page →Standardize before deduplicating
With structured attributes, matching compares apples to apples: similarity scores become far more precise.
A standard that protects the database
Rule-generated descriptions keep the next requester from creating the same item with different free text.
Services dirty the database too
Contracting services without scope or unit of measure creates duplicates and disputed measurements, just like materials.
The diagnostic measures how many items are missing attributes, off-standard or duplicated.
How to do it
How to cleanse your data in five steps.
STEP 01
Diagnostic
We measure the state of the database: duplicates, inconsistencies, empty critical fields, invalid records and hidden tax risks.
STEP 02
Standardization
We apply taxonomy and rules: short and long descriptions, formats, units, classifications and naming by family.
STEP 03
Deduplication
We identify and merge repeated records into a golden record, with similarity scores and attribute-by-attribute survivorship.
STEP 04
Validation and enrichment
We verify and complete records against more than 600 public sources, with AI support: registration status, CNAE, address, certificates and technical attributes.
STEP 05
Continuous governance
We install entry rules, approval workflow and indicators so bad data doesn't come back the following month.
The fifth step is what sets 4MDG apart from a common cleanup.
Your database doesn't get clean just once — it stays clean.
The fifth step is what keeps the database clean after the project.
Why 4MDG
Different from a common cleanup.
Cleansing and governance together
We solve the cause, not just the symptom. Entry rules and workflow keep the problem from coming back.
AI and more than 600 public sources
Validation and enrichment at scale, checked at the source with evidence stored per record.
All three domains
Materials, suppliers and customers — not just a marketing mailing list.
Native SAP integration
The treated database enters the ERP clean, via API, IDoc, BAPI or file.
Software and people (DaaS)
We can run your cleansing continuously for you, under an agreed SLA.
ISO/IEC 27001 and LGPD
Your data handled under a certified management system, in compliance with LGPD and GDPR.
Proof
Clean data, measurable results.
−87%
Wella Company · supplier SLA, with −78% for customers and −84% for materials, and 100% of records managed by 4MDG.
+174k
Industry (SAP) · records standardized and cleansed, with optimized e-procurement and fewer purchases from wrong descriptions.
60M
records under management, for 160 clients in 6 countries.
Cristália · indirect materials cleansing: a clean, standardized MRO and consumables database, with operational improvement and cost reduction.
Get these results on my databaseWhat you gain
What changes when your data becomes reliable.
Fewer duplicate purchases
Unique, findable items cut repeat purchases and idle inventory.
A month-end close that adds up
Spend by category, ABC curve and supplier views stop diverging between areas.
Faster onboarding and qualification
Records done right the first time shorten the path to the first purchase or sale.
Less tax and compliance risk
Registration status, certificates and tax classification checked at the source and revalidated.
Data ready for BI
Reports built on a single database stop being a debate over which number is right.
Data ready for AI
AI models and agents only deliver value on reliable, traceable master data.
BI and AI only deliver value on reliable master data.
Contact
Let's look at your database before proposing anything.
A specialist analyzes your scenario and shows the current state of records in your industry. No commitment required.
Av. Queiroz Filho, 1700, Torre E, Conjuntos 715 a 718
Vila Leopoldina, São Paulo, SP, 05319-000
FAQ