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Elements of a business analytics framework for IT

Moving beyond operational mode to discovery mode requires the ability to support the various styles of decision making that are used throughout the enterprise.

Ad Hoc Decision Making and Classic Business Intelligence

Any business analytics environment contains elements that today would be considered normal for most business intelligence (BI) applications. The classic BI environment has operational elements, including applications that implement business processes and their associated databases. The data warehouse often acts as the interface between the operational world and the BI world.

Figure 1: Achieving the discovery mode requires support for a variety of decision-making styles

Data warehouse data is usually not structured to support a wide range of analytical and reporting applications. It is often necessary to create managed data marts tailored to specific business analytics applications such as anti-money laundering, fraud detection or social network analysis. It is important to note that these are not simply random extracts from the data warehouse but are created and maintained by well-defined IT processes.

The classic BI approach does not address the full range of business needs that can be met through business analytics.

Exploratory and Transformational Decision Making

A key differentiating requirement of business analytics environments is the ability to move beyond reactive, BI-based ad hoc decisions to proactive exploration, which leads to transformational decisions. By using more advanced quantitative methods, business analysts can challenge fundamental business models and evolve them into new models that support top-line organizational growth.

Automated and Real-Time Decisions

Business analytics stretches beyond the simple ad hoc decision process by providing the key input to and a seamless mechanism for feeding results back into operational processes. Automation is suitable for cyclically running processes, such as planning, but not for applications that enable decisions to be made on the fly, with intelligence, in response to an external event (such as the swipe of a credit card or the arrival of a phone call). In such cases, the operational process will need to “call out” for help.

As a result of this call, the scoring or recommendation engine needs to execute very quickly and return a result in the form of a recommendation, such as a go/no-go decision for a transaction that may be fraudulent or a “next offer” suggestion based on a customer rating. Together, these styles of decision making require a business analytics framework that provides:

  • Packaged operational analytical applications, such as real-time fraud detection and marketing solutions.
  • A robust platform for business analytics that supplies core infrastructure services.

To effectively deliver business analytics, IT groups will need to rebuild for the future.

Figure 2: The logical business analytics processes (a SAS Best Practice Blueprint).

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