Professional learning path

Data Modeler

Define a customer model that engineering and analytics can use without conflicting definitions.

Prepare

Prepare a trusted reconciliation dataset

Run the reconciliation kit. Inspect identifiers, currency, and unmatched rows. Define the grain of the result and explain why a currency mismatch must remain an exception even when amounts are equal.

Start with the browser demo or download the local kit. For the workspace exercise, you need approved access to the relevant Genedata capabilities and a reviewer for your output.

Open the interactive demo Download the practice kit

Practice tasks

0 / 4 completed

Record the source key and the grain of each table.Your working notes

Explain the missing settlement and currency mismatch in the output.Your working notes

Change the amount tolerance and document which result changes.Your working notes

Publish the definition of a matched record with its limitations.Your working notes

Notes and progress are saved on this device.

Apply in your workspace

Give shared data a shared meaning.

Define a customer model that engineering and analytics can use without conflicting definitions.

Inspect existing datasets, glossary terms, and downstream consumers before proposing a new model.

Agree the business grain, identifiers, relationships, and definitions with the data owner.

Document mappings and validation rules. Review the impact of changed fields with engineering and analytics.

Publish the agreed model and connect it to the catalog entries and implementation work that use it.

Expected handoff

An agreed model, field definitions, mappings, and an impact review.

Explore your platform capabilities

Measure your progress

Time to agree a shared data definition
Downstream changes caused by incompatible schemas

Ask AI to explain a schema or suggest mappings; have domain owners confirm the business meaning.