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Embedding Intelligence Throughout the Enterprise
A InforSense White Paper

by David Menninger, VP Global Marketing & Product Management , InforSenseMonday, November 12, 2007

EXECUTIVE SUMMARY

Historically, business intelligence (BI) has been about bringing data to the analyst. Analysis could only be performed with specialized business intelligence tools, specialized data sources called data warehouses and specialized skills to be able to design and use business intelligence systems. Instead of bringing the data to the analysts, InforSense provides software that enables enterprises to bring the analysis to the data. The solution is simple: Data originates and resides in your existing business systems and processes. That’s where the analysis belongs.

By embedding analytics in business processes you can ensure that the entire organization benefits from business intelligence technology and you can ensure that your organization is applying its decision-making policies in a consistent and efficient manner.

This document presents the following key technologies of the InforSense platform and describes how these enable your entire organization to leverage the information generated in your existing business processes to make better business decisions.

• Dynamic data and application integration: Existing applications such as customer relationship management (CRM), supply chain management, and customer service/customer support must all be tied together to create a single horizontal analytical
platform. The InforSense platform can combine and analyze data in a flexible way to support analyses ranging from high data volume, single point analyses to high throughput real-time scoring applications.
• In-database processing: The ability to perform data processing and analytics within standard commercial databases such as Oracle, IBM DB2 and Microsoft SQL Server is critical for data warehouse creation and searching. Data integrity is maintained by
avoiding the need to extract data unnecessarily for analysis. High performance data processing and analytics are both secure and scalable.
• Interactive analytical workflows: Adopting a workflow paradigm enables domain experts to rapidly and interactively create and optimize streamlined analytic applications that are specific to your organization’s needs.
• Predictive Analytics, Data Mining and Text Mining: Business processes may require very specific types of analyses ranging from simple analytics to the most advanced statistical algorithms or data mining techniques. Embedded analytics must support whatever types of analyses are needed by the business process.
• Enterprise deployment and reporting: The custom workflow applications incorporate standard reporting as well as advanced visualization techniques. These workflow applications can be deployed to any user community from a centrally-maintained portal
as fully-interactive applications enabling any type of decision making while optimizing standard data processing steps.

EMBEDDED ANALYTICS

Embedded analytics is a paradigm pioneered by InforSense that addresses the challenges faced by IT teams and management across all industries. This approach is based on the use of analytical workflows as a means for rapidly developing and deploying applications that integrate data from multiple sources and coordinate the invocation of various analytical tools. The approach enables domain experts to construct their own analytical workflows within a user-friendly environment, to conduct analysis, and to easily deploy their analyses as interactive applications for use by colleagues. The approach reduces the long cycle times required for developing and deploying analytical processes, while providing IT personnel with the control and flexibility they require to maintain, integrate and deliver the underlying data sources and software tools.

Analytical Workflows

The Embedded Analytics approach uses analytical workflows as the enabling technology for crossdomain data and application integration. Informally, a workflow (see Figure 2) is a high-level description of the steps required for executing a particular real-world process and the flow of information between these tasks. Work passes through the flow from start to finish and the activities are executed by people or by system functions. Visually, a workflow is often best represented as a directed graph where tasks are represented as nodes (boxes) and information flow represented as arcs (arrows).

In InforSense’s Embedded Analytics approach, workflows are used to specify the data processing and analysis steps using data integrated from distributed data sources. The workflows are authored through a visual interface where users can drag-and-drop nodes representing available data sources and processing tools. The workflows are then submitted for execution by a workflow engine that controls the access and data transfer between the distributed applications that implement the processing steps.

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