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Data modeling and collaboration: Insights for business and data professionals

Business stakeholders make decisions with misunderstood or incorrect data.

75% use wrong data

  • 12% I do not know
  • 13% never use wrong data
  • 81% interpret data incorrectly.

  • 10% I do not know
  • 9% never interpret data incorrectly

Communication gaps affect data quality.

  • Only 45% include data modeling team in time to influence development.
  • 28% document the data model after applications are complete.
  • 25% data modeling team finds out too late.
  • 3% After decisions have been made that cannot be changed.

Better understanding data models means better data.

Application and data quality would increase if developers understood data modeling better. Yes 95%. No 5%.

Agile data modeling enables:

  • 95% see benefits in agile data modeling.
  • 52% increase data accuracy and integrity.
  • 51% better collaboration between data modelers and developers
  • 49% Improved consistency across applications using the same data models as a basis
  • 45% Better communication with business stakeholders and alignment to business objectives
  • 43% Improve alignment throughout the organization by increasing sharing of models and metadata
  • 38% Better solution delivery performance
  • 37% Increased productivity be creating and tracking data modeling tasks
  • 34% Better visibility into backlog of change requests

Survey by: dimensional research

Topics : Data Modeling,Enterprise Architecture,Metadata,

Products : ER/Studio Data Architect,ER/Studio Enterprise Team Edition,

Infographic : ER/Studio Enterprise Team Edition

Data Modeling and Collaboration

IDERA ER/Studio Enterprise Team Edition is a powerful modeling and metadata collaboration solution that helps you create an enterprise data architecture foundation for your data governance initiatives. With ER/Studio you can document your enterprise architecture with business processes, entities, and data flows that accurately represent your organization and its priorities. These can serve as the basis for enabling data quality and data governance programs.

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