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Examining the Birst BI and predictive analytics tool

Centralized BI and IT teams can use Birst to connect with decentralized teams on the business side. Learn more about the BI and predictive analytics tool.

Birst is a cloud-based BI and predictive analytics tool that automates the process of unifying a company's centrally managed enterprise data -- including data warehouses, data lakes and enterprise resource planning systems -- with user-generated data.

Birst enables centralized teams (BI and IT organizations) to collaborate with decentralized teams (business units). End users can work with data on their own through a variety of analytical styles on top of a unified business model, and BI teams can manage enterprise data assets while shifting the task of creating reports to the users.

The product includes a WYSIWYG dashboard editor, a key performance indicator creation wizard with visual indicators, automatic layouts, redesigned filters with advanced filter options, and dashboard themes for full white labeling capabilities. Visual discovery enhancements include performance optimizations, enhanced formatting capabilities, predefined color palettes, chart properties, exporting, localization and embedding visualizations in Web applications.

A new iOS native application supports offline analysis, and new data can be loaded without interrupting access to reports and dashboards.

The Birst BI and predictive analytics tool connects to on-premises or cloud data sources or applications, and can extract data or issue live queries via a set of prebuilt connectors to popular applications such as Salesforce, SAP and Eloqua. Its automated data refinement (ADR) engine unifies, refines and organizes data from multiple sources into a user-ready data store (URDS). All ADR workflows are developed from a single Web browser without requiring any work in a different application or desktop-based tool.

The URDS is designed and optimized for relational online analytical processing-style analytics, providing a Kimball-style star schema with a multidimensional view of all data. In addition, Birst supports Type 1 and 2 slowly changing dimensions and conformed dimensions, and automatically manages snapshots and time-based transformations.

A two-tier analytics engine provides full ad-hoc analysis capabilities across multiple sources. Birst supports query federation and Live Access (real-time queries), enabling customers to leverage their existing investments in data warehouses, data marts and XML for Analysis cubes. On-premises analytical data stores are queried in real time, without having to first extract and load the data into the cloud.

A single set of business rules and definitions enable data governance and allow users to create their own custom measures and attributes. Managed data mashups enable administrators to create sandboxes (or virtual spaces) for business users to use and work in, while the administrator maintains the parent space on which the enterprise is run.

Birst subscriptions and support

The Birst predictive analytics tool is sold as a subscription that includes an annual platform fee, plus per-user fees. Additional options such as application-specific connectors, Solution Accelerators and language packs are sold separately. Although Birst pricing isn't published, the company provides flexible pricing terms for embedded customers to match their business model. It can also operate on premises, via a virtual appliance running the Open Virtualization Format.

Birst is sold directly by Birst Inc. or via technology partners and worldwide resellers. A 30-day limited trial is available.

Birst offers 24/7 customer support for critical issues for both cloud and appliance deployments and around-the-clock support during the week for all other issues. Premium support is offered through Birst's Business Analytics Competency Center offering. Support is included in subscription license agreements and isn't negotiated separately.

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This was last published in November 2015

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Birst also partners with Altiscale for Big Data (Hadoop and Spark) so that you can combine classic enterprise data (revenue, operational data) with large volume, unstructured data (log files, social media, unstructured text data).
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