Understanding analytic applications

The term ''analytic application'' has emerged as an important new business intelligence trend. Yet many business intelligence professionals wonder if this term truly signifies something new, or whether it simply repackages existing analytic technologies and processes under a new banner.

The truth is that the market for analytic applications is both new and old.

What's old?
For the past 10 years, business intelligence professionals have employed analytic technologies and products -- including data warehouses, query and reporting tools, OLAP products, data mining tools and algorithms, and visualization techniques -- to deliver information and insight to business users. In most cases, organizations use these tools to create access methods and reports for specific functional areas, such as sales, marketing, finance and manufacturing.

Many business intelligence professionals may consider the results of these projects to be analytic applications. And they are right -- to a degree. These handcrafted solutions apply analytic technologies to business needs in specific functional areas.

So, what's new about an ''analytic application'' in the year 2002?

What's new?
Four factors differentiate today's analytic applications from previous generations of analytic tools and applications. TDWI calls these the four ''P's'' of analytic applications:

1. Perspective. A true analytic application contains some level of domain knowledge about a functional area, such as sales, marketing or manufacturing, in a particular industry. The best analytic applications embody industry best practices represented as key performance indicators or metrics within a set of pre-defined reports or report templates.

For example, a procurement analytic application might provide a set of reports that help purchasing managers optimize spending on materials for a manufacturing plant. The spending optimization reports will highlight best-practice metrics, such as aggregate spending per supplier on a global basis, average order value and percentage of order volume purchased outside existing contracts.

''The real benefit of an analytic application is the domain knowledge or intellectual capital it contains about a functional area,'' said Bill Schmarzo, vice president of analytic applications at DecisionWorks Consulting and a TDWI faculty member who instructs on analytic applications at TDWI conferences.

2. Process. Another trademark feature of an analytic application is that it is ... well ... an application. Just as operational applications walk users through a pre-defined business process step by step, analytic applications do the same for decision-making processes.

An analytic application is not just a bunch of reports, or an analytic tool to perform ad hoc queries or create reports. It is a real application that provides transparent support for the analytic processes that individuals and groups use to analyze data, make decisions and act on plans. These analytic processes can be procedural, contextual, collaborative, event-driven, transactional or evaluative.

In the past, organizations gave users an analytic tool, provided some training and hoped for the best. The result was usually failure or lots of underutilized software. A report by Nigel Pendse and found that organizations never deploy an astonishing 39% of the OLAP licenses they purchase. (See for ''The OLAP Survey,'' by Nigel Pendse, July 2001.)

Analytic applications promise to reverse this trend by embedding analytics into the fabric of users' daily business processes and tasks. Users don't have to wrestle with a tool to access, analyze and act on information. They simply use an analytic application that supports their decision-making style, business processes and collaborative activities in a seamless, intuitive fashion.

3. Packages. In the past, organizations had to stitch together multiple products and components using hand-written code to create an analytic application. Today, many vendors offer packaged analytic applications that pre-integrate analytic and data warehousing components, including data models, ETL tools, meta data, analytic tools, reports and portals.

These packaged applications typically provide 65% to 85% of a complete solution, greatly accelerating and simplifying the deployment process. Most analytic packages are tailored to specific functional areas, such as sales or marketing, or to applications in vertical industries, such as retail merchandising and assortment analysis. These packages embed domain knowledge of specific functional or vertical applications in data models and reports.

4. Platforms. Contrary to public opinion, analytic applications are not just packaged solutions. Today, organizations can just as easily build an analytic application as buy one. That's because an emerging class of vendors offers specialized tools to rapidly build custom analytic applications on top of an existing data warehouse or data mart.

These tools -- which TDWI calls analytic development platforms -- enable developers or savvy business users to build custom analytic applications using pre-defined components, services and starter kits in a graphical environment that minimizes coding and facilitates rapid prototyping and deployment. (There are also data warehouse development platforms, sometimes called packaged data warehouses, that automate the design, deployment and maintenance of a data warehouse.)

In contrast, most analytic tools (a.k.a. decision support tools or business intelligence tools) are hard-wired monoliths with a vendor-supplied look and feel -- what you see is what you get. Administrators cannot easily modify the vendor's GUI, add new functions, modify or extend existing functions, or dynamically personalize the end-user environment (GUI, functionality or views, for example,) to fit the user or group.

The complete solution
In summary, an analytic application is a domain-specific analytic solution that integrates a diverse set of data warehousing and analytic tools that organizations previously had to painstakingly stitch together.

New packages and development tools enable organizations to rapidly deploy analytic solutions that address the unique information needs of knowledge workers in specific departments or lines of business. As apps, these new products provide built-in support for decision-making processes that knowledge workers use to access, analyze, collaborate and act on information.

Analytic application:
An analytic application enables business users to access, analyze and act on information in the context of the business processes and tasks they manage. It embeds domain knowledge that supports the unique information requirements of users in a specific department or functional area. An analytic application is a complete solution that usually leverages a data warehousing environment, embeds analytic tools and employs business process logic. You can either build or buy an analytic application.
What it's not: An analytic tool.

Packaged analytic application: The ''buy'' option -- a vendor-supplied package that provides domain-specific analytics. It contains an integrated set of analytic tools, data models, ETL mappings, business metrics, pre-defined reports, and ''best practice'' processes that accelerate the deployment of an analytic application in a given domain or across multiple domains.
What it's not: An analytic tool.

Custom analytic application: The ''build'' option -- an analytic application that is primarily built using tools, code or customizable templates to provide the exact look, feel and functionality desired by an organization for its analytic environment.
What it's not: An analytic tool.

Analytic Development Platform (ADP): A development environment that enables developers or savvy business users to build custom analytic applications using predefined components, services or starter kits in a graphical environment that minimizes coding and facilitates rapid prototyping and deployment.
What it's not: An analytic tool.

Business analytic tool (or analytic tool): A tool that provides query, reporting, OLAP or data mining functionality for end users, but offers little or no ability to extend or substantially customize functionality or the end-user environment.
What it's often called: A business intelligence tool or decision support tool.

Business intelligence: The processes, technologies and tools needed to turn data into information, information into knowledge, and knowledge into plans that drive profitable business action. Business intelligence encompasses data warehousing and business analytic tools, as well as content and knowledge management.
What it's not: An analytic tool.

About the Author

Wayne W. Eckerson is director of education and research for The Data Warehousing Institute, where he oversees TDWI's educational curriculum, member publications, and various research and consulting services. He has published and spoken extensively on data warehousing and business intelligence subjects since 1994.


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