Migrate your data once. Prove it’s right.

DataPrep 360 is Data Semantics’ governed migration workspace. It profiles your source data, suggests mappings with AI, uncovers the business rules hidden in legacy systems, records who decided what, generates and fixes the migration code, and reconciles source to target, so every go-live comes with evidence, not just a load log.

Reconciliation proves the data in the new system matches the old one: record counts, balances and open items, checked at every mock load and at cutover. With DataPrep 360, opening-balance variance at cutover is within 0.5%, and every remaining difference is either fixed as a defect or explained by a recorded, approved decision.

Matching totals tells you the numbers agree. It doesn’t tell you why a difference was accepted, which legacy rule was deliberately changed, or who approved it. DataPrep 360 links every mismatch to either a defect to fix or a recorded decision, so auditors and business owners can see the reasoning.

Yes. DataPrep 360 profiles, maps, cleanses and reconciles customers, suppliers, items, open receivables and payables, general ledger balances and history from Oracle E-Business Suite to Oracle Fusion or Microsoft Dynamics 365, starting from our mapping library rather than a blank sheet.

Yes. DataPrep 360 prepares, maps and reconciles master and transactional data for SAP moves, including ECC to S/4HANA, working alongside your SAP implementation partner.

Yes. DataPrep 360 merges CRM orgs and migrates CRM data using merge rules agreed with the data owners, for example matching customers on tax number and trade license, so the target holds one record per customer.

Yes. DataPrep 360 covers core banking migrations across Temenos T24 and Temenos Transact, Path Solutions iMAL, Infosys Finacle and Oracle FLEXCUBE Universal Banking, older Temenos T24 releases, Finastra Equation and Midas, and ICS BANKS and in-house systems, plus cards, lending, treasury and regulatory reporting data. A Gulf bank’s iMAL to Temenos T24 migration is a named reference.

Yes. DataPrep 360 supports moves from Teradata, Netezza, Oracle, SQL Server, Azure Synapse and other warehouses to Microsoft Fabric, Databricks or Snowflake, with old and new reconciled before the switch-off. Our Data Platform Modernization service runs these programs end to end.

Yes. Closed history is archived to Microsoft Fabric with its keys and lineage preserved, retrieval is tested, and the legacy system can then be retired while the history stays reportable.

At every step: profiling and spotting duplicates and gaps, suggesting source-to-target mappings from our mapping library, reading legacy code and reports to propose business rules, generating SQL, PySpark and data quality checks, fixing failed transformations straight away, and explaining reconciliation differences. People approve the mappings, rules and sign-offs.

Mapping suggestions, migration code generation and data quality checks run in the DataPrep 360 workspace. Rule discovery and reconciliation are run by our migration specialists with AI-assisted tooling, and every result is recorded in the workspace.

A data quality phase, typically 4 to 6 weeks, profiles the source for duplicates, gaps and invalid references. Issues are ranked with Pareto charts and an urgent-versus-important (Eisenhower) matrix, so business owners fix the few issues that matter most first, and we support their corrections and track progress until go-live.

No. DataPrep 360 works alongside the platforms you already use. Generated pipelines and checks are deployed into your own environment, such as your Microsoft Fabric tenant, under version control. DataPrep 360 holds the mappings, rule decisions and reconciliation evidence.

Yes, and that is the most common setup. Your implementation partner configures the new system; we own the data: profiling, mapping, cleansing, mock loads and reconciliation evidence, on a plan aligned to the program’s test cycles and cutover.

It is reused. The agreed definitions, merge rules and mappings become the business context for reporting, data quality monitoring and AI agents such as Serina and Zebo, instead of being left in project spreadsheets.

From 6 weeks for a focused migration, and longer for larger programs, because the plan is aligned to your implementation program, its mock loads and its cutover. A data quality phase on its own typically takes 4 to 6 weeks.

With a Migration Readiness Check. We profile a sample of your source data and show, in plain numbers, how ready it really is: duplicates, gaps, unmapped fields and open decisions. For data platform programs, the fixed-price Data Platform Assessment covers your current platform and a plan to move, including to Microsoft Fabric.

As a fixed-bid project, priced on the number of data objects in scope, such as customers, suppliers, items, open balances and history. You know the cost before the work starts.

Data Semantics holds SOC 2 Type II and ISO 27001 and handles data in line with GDPR. Work can run inside your own cloud tenant, access is role-based and every decision and change is logged. Details are in the DS Trust Center.