Slideshow 17 BI & Analytics Requirements: Gartner Magic Quadrant

  • March 01 2015, 6:06pm EST
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17 BI & Analytics Requirements: Gartner Magic Quadrant

How can you choose the most ideal business intelligence and analytics platform? Gartner Magic Quadrant research recommends that you closely examine BI platforms that offer these 17 technology capabilities.

1. Business Analytics Reporting

Provides the ability to create highly formatted, print-ready and interactive reports – with or without parameters. Source: Gartner. Image: iStock.

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2. Business Intelligence Dashboards

A style of reporting that graphically depicts performances measures. It includes the ability to publish multi-object, linked reports and parameters with intuitive and interactive displays; dashboards often employ visualization components such as gauges, sliders, checkboxes and maps, and are often used to show the actual value of the measure compared to a goal or target value. Dashboards can represent operational or strategic information.Source: Gartner. Image: iStock.

3. Ad Hoc Report/Query

Enables users to ask their own questions of the data, without relying on IT to create a report. In particular, the tools must have a reusable semantic layer to enable users to navigate available data sources, predefined metrics, hierarchies and so on.Source: Gartner. Image: iStock.

4. Microsoft Office Integration

Sometimes, Microsoft Office (particularly Excel) acts as the reporting or analytics client. In these cases, it is vital that your BI tool provides integration with Microsoft Office, including support for native document and presentation formats, formulas, charts, data “refreshes” and pivot tables. Advanced integration includes cell locking and write-back. Source: Gartner. Image: iStock.

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5. Mobile BI

Enables organizations to develop and deliver content to mobile devices in a publishing and/or interactive mode, and takes advantage of mobile devices’ native capabilities, such as touchscreen, camera, location awareness and natural-language query. Source: Gartner. Image: iStock.

6. Interactive Visualization

Enables the exploration of data via the manipulation of chart images, with color, brightness, size, shape and motion of vision objects representing aspects of the dataset being analyzed. This includes an array of visualization options that go beyond those of pie, bar and line charts, including heat and tree maps, geographic maps, scatter plots and other special-purpose visuals. These tools enable users to analyze the data by interacting directly with a visual representation of it. Source: Gartner. Image: iStock.

7. Search-based Data Discovery

Applies a search index to structured and unstructured data sources and maps them into a classification structure of dimensions and measures that users can easily navigate and explore using a search interface. This is not the ability to search for reports and metadata objects. This would be a basic feature of a BI platform.Source: Gartner. Image: iStock.

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8. Geospatial and Location Intelligence

Specialized analytics and visualizations that provide a geographic, spatial and time context. Enables the ability to depict physical features and geography referenced data and relationships by combining geographic and location-related data from multiple sources – including aerial maps, GISs, and consumer demographics, with enterprise and other data. Basic relationships are displayed by overlaying data on interactive maps. More advanced capabilities support specialized geospatial algorithms (for example, for distance and map calculations), as well as layering of geospatial data on to custom base maps, markers, heat maps and temporal maps – supporting clustering, geofencing and 3D visualizations. Source: Gartner. Image: iStock.

9. Embedded Advanced Analytics

Enables users to leverage a statistical functions library embedded in a BI server. Included are the abilities to consume common analytics methods such as Predictive Model Markup Language (PMML) and R-based models in the metadata layer and/or in a report object or analysis to create advanced analytic visualizations (of correlations or clusters in a data set, for example). Also included are forecasting algorithms and the ability to conduct “what if?” analysis. Source: Gartner. Image: iStock.

10. Online Analytical Processing (OLAP)

Enables users to analyze data with fast query and calculation performance – offering a style of analysis known as “slicing and dicing.” Users can navigate multidimensional drill paths. They also have the ability to write-back values to the database for planning and “what if?” modeling. This capability could span a variety of data architectures (such as relational, multidimensional or hybrid) and storage architectures (such as disk-based or in-memory). Source: Gartner. Image: iStock.

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11. BI Infrastructure and Administration

Enables all tools in the platform to use the same security, metadata, administration, object model and query engine, and scheduling and distribution engine. All tools should share the same look and feel. The platform should be multitenancy. Source: Gartner. Image: iStock.

12. Metadata Management

Tools for enabling users to leverage the same systems-of-record semantic model and metadata. They should provide a robust and centralized way for administrators to search, capture, reuse and publish metadata objects, such as dimensions, hierarchies, measures, performance metrics, key performance indicators (KPIs), and report layout objects, parameters and so on. Administrators should have the ability to promote a business-user-defined data mashup and metadata to the systems-of-record metadata. Source: Gartner. Image: iStock.

13. Business User Data Mashup and Modeling

Code-free, drag and drop, user-driven data combination of different sources and the creation of analytic models, such as user-defined measures, sets, groups and hierarchies. Advanced capabilities include semantic autodiscovery, intelligent joins, intelligent profiling, hierarchy generation, data lineage, and data blending on varied data sources, including multistructured data. Source: Gartner. Image: iStock.

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14. Development Tools

The platform should provide a set of programmatic and visual tools and a development workbench for building reports, dashboards, queries and analysis. It should enable scalable and personalized distribution, scheduling and alerts of BI and analytics content via email, to a portal and to mobile devices. Source: Gartner. Image: iStock.

15. Embeddable Analytics

Tools including a software developer’s kit with APIs for creating and modifying analytic content, visualizations and applications, embedding them into a business process, and/or an application portal. These capabilities can reside outside the application, reusing the analytic infrastructure, but must be easily and seamlessly accessible from inside the application, without forcing users to switch between systems. The capabilities for integrating BI and analytics with the application architecture will enable users to choose where in the business process the analytics should be embedded. Source: Gartner. Image: iStock.

16. Collaboration

Enables users to share and discuss information, analysis, analytic content and decisions via discussion threads, chat and annotations. Source: Gartner. Image: iStock.

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17. Support for Big Data Sources

The ability to support and query hybrid, columnar and array-based data sources, such as MapReduce and other NoSQL databases (graph databases, for example). Support could include direct Hadoop Distributed File System (HDFS) query or access to MapReduce through Hive. Source: Gartner. Image: iStock.

Thank You & More

Special thanks to Gartner for the info. For additional Information Management slide shows please visit here. All images courtesy of iStock.