Datatarve

Datatarve is a term used in data management that describes the data requirements needed to support a business process, decision, or project. It encompasses the types of data, the level of detail (granularity), frequency of updates, data sources, and acceptable quality and accessibility. It distinguishes what is needed from what is already available.

The datatarve is typically defined during requirements gathering and is used to align stakeholders such as business owners, data owners, data stewards, analysts, and IT. The outcome is a data requirements specification or data need statement that informs data sourcing, integration, and governance.

A typical datatarve includes data elements and attributes, required data quality characteristics (accuracy, completeness, timeliness, consistency), the desired granularity and aggregation level, any necessary data lineage and provenance, and metadata to describe source and context. It also covers who can access the data and under what privacy and security constraints, as well as retention and lifecycle considerations.

In practice, datatarve guides the design of data architecture, data models, data catalogs, and master data management. It supports reporting, dashboards, analytics, forecasting, risk assessment, and regulatory compliance by ensuring the right data is available in the right form.

Challenges include resolving conflicting needs across departments, dealing with data silos, changes in business requirements, data quality issues, and compliance with privacy laws. Effective management relies on documentation, ongoing governance, stakeholder involvement, and traceability from data sources to outputs.