Continue in 2 seconds

Building the Intelligent Business

  • June 01 2002, 1:00am EDT

Business intelligence (BI) and data warehousing systems were once considered nice to have, but they are now so essential to business success that many companies cannot manage their business effectively without them. These systems are no longer standalone and separate from operational processing ­– they are becoming more and more integrated into the overall business process. This integration allows BI to be used not only for strategic and tactical planning, but also for actively driving day-to-day business operations and decisions. To support operational decision making, the BI system must provide easy-to-use and self-service enterprise analytic applications that enable real-time decision making. It must also include a methodology (either formal or informal) that helps organizations assign responsibilities to specific business users for handling BI and define actions to be taken when BI identifies business problems that require urgent attention. Such a system can be thought of as supporting integrated and actionable BI, and this will be the focus of my column. I intend to review strategies, technologies and products that enable an organization to become an intelligent business, rather than just a business with a data warehousing system.

A Historical Perspective

Software and hardware for delivering accurate business information to executives and managers for corporate decision making has evolved from early centralized batch reporting systems to data warehousing and BI processing, and more recently to enterprise analytic applications.

Users of first- generation centralized batch reporting systems had to deal with complex tools, poor data quality, an absence of data integration, inconsistent data and limited historical information. Data warehousing was introduced to help improve the quality, integration and consistency of data, and to store historical data that helped business users track business trends. The problem with early data warehousing implementations was that the development group felt that the job was finished once the data warehouse was built, and they were surprised when the data warehouse did not provide the expected ROI. The problem was that business users did not have the tools to fully exploit the value of the data in the warehouse. Vendors then introduced BI tools that extended the use of data warehousing from simple reporting to advanced analytical processing and data mining.

Figure 1: BI and Enterprise Analytics Integration Framework

Although the introduction of BI tools allowed organizations to improve the quality and depth of the reports and analyses from the data warehousing systems, several problems still remained. These systems were still passive in that value could only be obtained from the BI produced if business users constantly monitored the results for business problems and then took action to fix them. Even if business users identified a problem, they often did not know how to analyze the problem in more detail or what actions should be taken to resolve it. Although very powerful, many of the new BI tools were complex and time-consuming to use, and business users had to rely on power users and the IT group to help them develop new reports and analyses. Complexity also prevented many grass-roots employees from having access to the BI they needed to do their jobs more effectively. For example, when handling a customer problem, call center staff either did not have access to BI about their customers or they had to use several different systems to find it.

A New Generation of Enterprise Analytic Solutions

To solve the complexity problems with BI tools, vendors have been introducing new software solutions and products that are at last beginning to improve the usability, and therefore the ROI, of BI and data warehousing investments by organizations.

These solutions fall into three groups. The first is aimed at faster and easier application development through the use of analytic application development tools and packaged analytic applications. These applications may support high-level business performance management (BPM) dashboards and scorecards (that may or may not support a business methodology) and/or domain-specific enterprise analytics. The second group of solutions provides personalized access to business information through the use of internal corporate and e-business portals. The third group is designed to support real-time decision making through the use of near real-time data warehousing, on-demand analytics and automated decision and recommendation engines.

It is important to emphasize that each new generation of products builds on, rather than replaces, solutions provided in earlier generations. This means that your selection of batch reporting products, data warehousing and BI tools, and enterprise analytic applications solutions must fit into an overall framework as shown in Figure 1. Otherwise, you simply end up with a bunch of nonintegrated stovepipe applications.

Register or login for access to this item and much more

All Information Management content is archived after seven days.

Community members receive:
  • All recent and archived articles
  • Conference offers and updates
  • A full menu of enewsletter options
  • Web seminars, white papers, ebooks

Don't have an account? Register for Free Unlimited Access