When reading the latest 2013 Gartner BI and Analytics Magic Quadrant I noticed that they have included some definitions of the various capabilities in each of the BI categories. I wil no doubt end up cutting and pasting them into a few BI Strategy documents this year, so to make it easier for me I have cut and pasted them here.
Gartners context around the definitions (and also this years change in name of the magic quadrant) was:
“Gartner changed the name of this Magic Quadrant from “Business Intelligence Platforms” to “Business Intelligence and Analytics Platforms” to emphasize the growing importance of analysis capabilities to the information systems that organizations are now building. Gartner defines the business intelligence (BI) and analytics platform market as a software platform that delivers 15 capabilities across three categories: integration, information delivery and analysis.”
So the Gartner definitions were:
- BI infrastructure:
All tools in the platform use the same security, metadata, administration, portal integration, object model and query engine, and should share the same look and feel.
- Metadata management:
Tools should leverage the same metadata, and the tools should provide a robust way to search, capture, store, reuse and publish metadata objects, such as dimensions, hierarchies, measures, performance metrics and report layout objects.
- Development tools:
The platform should provide a set of programmatic and visual tools, coupled with a software developer’s kit for creating analytic applications, integrating them into a business process, and/or embedding them in another application.
Enables users to share and discuss information and analytic content, and/or to manage hierarchies and metrics via discussion threads, chat and annotations.
Provides the ability to create formatted and interactive reports, with or without parameters, with highly scalable distribution and scheduling capabilities.
Includes the ability to publish Web based or mobile reports with intuitive interactive displays that indicate the state of a performance metric compared with a goal or target value. Increasingly, dashboards are used to disseminate realtime data from operational applications, or in conjunction with a complex event processing engine.
- Ad hoc 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 robust semantic layer to enable users to navigate available data sources.
- Microsoft Office integration:
Sometimes, Microsoft Office (particularly Excel) acts as the reporting or analytics client. In these cases, it is vital that the tool provides integration with Microsoft Office, including support for document and presentation formats, formulas, data “refreshes” and pivot tables. Advanced integration includes cell locking and writeback.
- Search based BI:
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.
- Mobile BI:
Enables organizations to deliver analytic content to mobile devices in a publishing and/or interactive mode, and takes advantage of the mobile client’s location awareness.
- Online analytical processing (OLAP):
Enables users to analyze data with fast query and calculation performance, enabling a style of analysis known as “slicing and dicing.” Users are able to navigate multidimensional drill paths. They also have the ability to write back values to a proprietary database for planning and “what if” modeling purposes. This capability could span a variety of data architectures (such as relational or multidimensional) and storage architectures (such as disk based or in-memory).
- Interactive visualization:
Gives users the ability to display numerous aspects of the data more efficiently by using interactive pictures and charts, instead of rows and columns.
- Predictive modeling and data mining:
Enables organizations to classify categorical variables, and to estimate continuous variables using mathematical algorithms.
These take the metrics displayed in a dashboard a step further by applying them to a strategy map that aligns key performance indicators (KPIs) with a strategic objective.
- Prescriptive modeling, simulation and optimization:
Supports decision making by enabling organizations to select the correct value of a variable based on a set of constraints for deterministic processes, and by modeling outcomes for stochastic processes.