Anzo Smart Data Integration (Demo)

Anzo Smart Data Integration abstracts and automates data integration projects, yielding higher-quality data and dramatically faster customer, partner, or data on-boarding.

Want to try out Anzo Smart Data Integration? Sign up for our Beta Program today and help shape a new, more sensible way to integrate data.

The Problem with Traditional Data Integration

Data integration and data on-boarding are time-consuming, manual, costly & error-prone processes.

  • Complex integrations require developing a large number of point-to-point source-target mappings.
  • Each mapping must be jointly developed by experts in all involved systems before being handed off to a team of ETL developers.
  • Each hand-off increases both the time it takes to complete the integration and also the risk of errors as requirements are misunderstood or not fully validated.
  • The lineage and meaning of data are often lost in the process, limiting the trustworthiness and utility of the data.

A Smarter Approach to Data Integration

Anzo Smart Data Integration replaces the point-to-point mappings of traditional data integration projects with mappings to and from common, conceptual models. (To learn more about how conceptual models can lead to significantly improved data quality and time-to-revenue, read more about Smart Enterprise Data Management).

Customers report that Anzo offers them:

  • Up to 10-times faster time-to-revenue when on-boarding new customers or partners, or when completing other data integration projects
  • High quality ETL jobs that accurately implement requirements as specified by business experts
  • Greater visibility into the lineage of data flowing across system and database boundaries

Key Capabilities

  • An Excel-based data mapper that lets business experts map source and target systems to conceptual models
  • Automatic generation of ETL jobs that run on popular 3rd party ETL engines such as Informatica or Pentaho
  • Reuse and combine mappings to quickly target different environments (e.g. development, QA, and production) or to populate new data warehouses with an existing schema
  • Self-service ability for business analysts to create their own data marts and data feeds
  • Dashboard access to data provenance and transformation lineage