The sovereign enterprise data management platform
DATAshaper measures the quality of your enterprise data and helps your teams fix what it finds, so the data behind your AI plans can be trusted. You decide where the platform runs and which AI model works on your business data.
Runs where you decide
Worldwide SAP migrations and acquisition integrations
Read the Atlas Copco story
One global ERP on Microsoft Dynamics
Read the mateco story
SAP data quality ahead of S/4HANA
Read the Bruker storyOther organisations DATAshaper has supported include
Measure and improve data quality, prepare your information for AI, and run enterprise migrations on a platform built from real project experience.
Configure quality rules, run scheduled checks and follow quality scores over time. Every issue goes to the business owner who can resolve it.
Find out whether the datasets behind your AI plans can be trusted, measure the quality dimensions each use case depends on, and keep business data on the model you choose.
Extract, map, transform, validate and migrate in structured, repeatable workflows, proven in global SAP and Microsoft Dynamics programs.
Shared quality rules, named ownership and review workflows give master data a dependable foundation to govern.
This area of the platform is still growing.
Quality work in DATAshaper follows one loop, from the first check to the next one. Follow it step by step, or pick a step to see what happens there.
Step 1 of 8
Scheduled checks run against your datasets and track a quality score over time, overall and per domain.
Product screenshot to be supplied
Data quality dashboard with scores and trend
Short screen recordings of each step will follow.
Five reasons teams choose DATAshaper, each one grounded in how the platform is used today.
Run DATAshaper on your own servers, inside your own Azure or other cloud tenant, or as a managed SaaS. For work on business data, connect the LLM you choose, including models you host yourself. Where data is stored and processed follows from the deployment and providers you pick.
AI suggests corrected values for issues your rules have found. People review and approve them, or you set up controlled automation where you trust it. Corrections are staged, never written straight into your ERP.
After a one-week training, Bruker's team configures rules and runs measurements largely on its own. Renson carried out a Material Master migration into SAP mostly by itself.
Quality measurement, cleansing, integration and migration share one platform, with master data and governance capabilities growing on the same foundation.
The technology behind DATAshaper has been shaped since 2013 by international SAP migration and integration work, often involving hundreds of thousands to millions of records per project.
Four very different programs, each described as it stands today.
Since 2013, the technology behind DATAshaper has supported Atlas Copco's worldwide migrations of customer centers onto SAP, alongside HR migrations to SAP SuccessFactors and CRM migrations to SAP C4C.
In a handful of acquisitions, the acquired company’s data became available on a Friday and the business was operating in SAP on Monday.
Today DATAshaper loads into SAP Migration Cockpit for the worldwide S/4HANA rollout to product companies, where bills of materials are a central challenge.
Various projects ongoing since 2013
Who does what. DATAshaper consultants handle platform configuration and technical migration. Atlas Copco leads the project with its business teams, and an implementation partner performs the SAP loads.
mateco is rolling out one global ERP across dozens of countries, consolidating many legacy systems into Microsoft Dynamics: hundreds of data objects and millions of records.
Structured, repeatable migrations with automated validations make data quality visible before go-live and reduce manual correction work.
Rollout in progress
Read the mateco storyAhead of a future S/4HANA migration, Bruker uses DATAshaper to measure the quality of its existing SAP data. After a one-week training, its team configures rules and runs measurements largely on its own.
Rules configured, measurements underway
Read the Bruker storyRenson used DATAshaper for a focused migration of its material master data into SAP. Its own team carried out the migration largely by itself, with DATAshaper as the platform for preparing, validating and delivering the data.
Migration completed
Before you build on your data, find out where it will let you down. We evaluate selected datasets, identify the quality risks that matter for your AI plans, and help you prioritize what to fix first.
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A paid engagement, priced on request.
Scope is agreed up front. The assessment interprets measured data quality against your goals; it does not certify your organization’s overall AI readiness.
Whether you're preparing for AI, improving data quality or planning an ERP transformation, start with a conversation.
Bring a specific challenge and discuss it with someone who has solved it before. Book a time or send a message.
See the platform in a guided session, usually with representative sample data. Book a time or send a message.