BI Governance Metadata Management

Eliminate Analytic Silos with BI Governance Metadata Management

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This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Birst.

The 2017 Gartner Critical Capabilities report asserts that the business intelligence (BI) and analytics market has reached a line-in-the-sand moment, moving away from IT-centric, reporting-based platforms and moving toward more modern BI and analytics solutions.

IT leaders must be prepared to deliver analytics across the enterprise to a wide range of users who demand self-service capabilities, but while avoiding the proliferation of analytic silos that undermine the decision-making process. As a leader, how do you go about managing BI governance metadata in the middle of all your IT demands? We have a solution!

In the current 2017 Gartner Critical Capabilities report, Birst received high rankings among the majority of components in the governance and metadata management critical capability — including data lineage and impact analysis, data modeling, metadata layer capabilities, promotability, and reuse. Birst’s Networked BI architecture, allows organizations to balance governance and agility within a single platform.

Birst’s Networked BI architecture includes the following advantages:

  • Two-tier architecture — comprised of an enterprise data tier and a user data tier — enables customers to identify, plus manage a semantic layer for governance climate.
  • A multi-tenant cloud platform — offering strong support for every facet of the cloud BI critical capability platform.
  • Prepackaged applications called Solution Accelerators that bundle pre-built connectors to cloud data sources.
  • Powerful capabilities in the area of data management, specifically self-contained ETL and data source connectivity.
  • Access to a diverse range of data source options, including OLAP sources, personal and Web data, relational database, cloud applications, Hadoop, and NoSQL sources.
  • Automated data refinement (ADR), which ingests data, automatically construes relationships, and then loads merged data into a central data store.