Data standardization services that make your systems agree Damco sets one format and one code list for every record you send
Data normalization for the fields that never match The fields where every system writes the same thing differently
How our data standardization works Rules first, agents for the volume, people for the exceptions
- Week 1Write the standardWe agree each field's format and code list with you, in writing.
- Week 1Test on a sampleWe standardize a sample, so you can see every change.
- Each batchAgents apply the rulesAgents parse and convert each field, and flag values outside the standard.
- Each batchPeople decide the exceptionsA reviewer sets each flagged value and adds it to the rules.
- DeliveryThe owner signs offA named batch owner checks a sample and answers for the batch.
When data standardization services are the right fit And when your data needs a wider cleanup or a new home
A good fit when
- The same supplier appears five ways across your ERP and spreadsheets
- Reports split one country into "US", "USA", and "United States"
- You're merging systems and need one product schema first
Look elsewhere when
- You also need duplicates merged and gaps filled: see data cleansing
- You need data moved into a new system: see data conversion
- You want standards enforced at entry in your own apps: see data engineering
Our end-to-end data standardization services What we standardize, field by field, and how
Why data standardization matters
Data reaches you from forms, partners, old systems, and spreadsheets, each in its own format. Until it's standardized, a report can't add up sales by country, and a buyer can't see total spend with one supplier.
Standardization identifies each data element, parses it into parts, and sets it to one agreed form. It's the step that lets matching, deduplication, and analysis work.
Names, addresses, and contact numbers
We parse people's names into prefix, first, middle, and last name, and set proper case. Addresses are set to the postal format of each country, and state and country names become standard codes. Phone numbers are validated and written in international format.
- Proper case formatting and prefix values
- State and country names as ISO codes
- Phone numbers validated and in international format
Company names, job titles, and identifiers
Company names lose stray punctuation and get one form for suffixes such as Inc., Ltd, and GmbH. Job titles map to the short list of roles your sales team uses, so "VP Ops" and "Vice President, Operations" count as one.
We check identifiers such as D-U-N-S and TIN numbers for the right length and format, and flag the ones that fail.
Supplier and manufacturing data standardization
Procurement and plant teams work from supplier and part records that grew over decades. We set one name per supplier, one unit per measure, and one description pattern per part family, so spend reports and inventory counts line up.
Product, business, and financial data normalization
For product catalogs, we map attributes to one schema: color, size, material, and unit written the same way across every product. For business and financial data, we normalize currencies, date formats, and account codes, so figures from two systems can be added together.
Questions about data standardization services What buyers ask us before a standardization project starts
What is data standardization?
It's the work of writing the same thing the same way everywhere. Names, addresses, codes, and units are set to one agreed format, so records from different systems can be matched, counted, and compared.
What's the difference between data standardization and data normalization?
In everyday business use the terms overlap. Standardization usually means one format for each field, and normalization means one scale or unit for values, such as one currency or one unit of measure. We do both.
Which file formats can you standardize?
Spreadsheets, database extracts, CSV and XML files, and data captured from paper forms and images. We return the data in the format your system loads.
Is it a one-time project or ongoing?
Either. Many clients start with a one-time cleanup of their master data, then send new records each week so they meet the same standard before they reach the system.
How do you price data standardization?
We price per record. The price depends on how many fields we standardize and how messy they are, and we quote it after a sample.
Send us the field that never matches
We'll reply with the standard we'd propose for it and the price per record.