SUGI 28 Data Warehousing and Enterprise Solutions Abstract

Many organizations either have built or are trying to build a

SUGI 28 Data Warehousing and Enterprise Solutions Paper 163-28 “How Do I Love Thee? Let Me Count The Ways.” SAS Software as a Part of the Corporate Information Factory John E. Bentley, Wachovia Bank, Charlotte, North Carolina Abstract Many organizations either have built or are trying to build a Corporate Information Factory (CIF), and SAS Software is often an integral part of it. A limitation of the CIF framework, however, is that it suggests hardware and database architectures and shows the flow of data and information through the system but it doesn’t provide guidance about the software needed to run the factory. As a result, it’s common for a CIF to take a “best of breed” approach and use different software for each process and task: ETCL, data QA, metadata management, ad hoc querying, production analysis, and reporting, OLAP, data mining, ad nausium. This approach complicates the CIF system, increases costs, and reduces ROI. In this best-of-breed solution, SAS Software is often deployed only in an analytical role that clearly fails to take advantage of the power of software. If SAS is leveraged to the full extent of its capabilities, then the CIF can dramatically reduce its software complexity, thereby reducing costs and increasing flexibility and ROI. After reviewing the CIF concept, this paper will discuss how specific SAS products should be integral parts of the Corporate Information Factory. The paper is generic with regard to operating systems and appropriate for all skill levels. Disclaimer: The views and opinions expressed here are those of the author and not his employer. Wachovia Bank does not endorse or necessarily use any of the models, frameworks, approaches, processes, architectures, hardware, or software discussed here. Introduction As a concept, the Corporate Information Factory (CIF) has been around since the early 1980s when W. H. Inmon used it to describe the information ecosystem. The CIF is well suited for this because its generic structure can be used to identify the ecosystem in totally different corporations. At the same time, it’s flexible enough to handle the impacts of the numerous forces that affect each corporation—business and economic, culture and politics, technology—and therefore affect it. Just as important as its descriptive usefulness, the CIF framework provides broad direction by telling us what we should be doing and what our goal should be in building our information systems. Organizations have increasingly complex business systems that include data warehouses, data marts, operational data stores (ODS) and multiple decision support systems (DSS) used by hundreds or thousands of information consumers and decision makers. Even smaller organizations’ information systems are increasingly complex. For all organizations, then, the CIF concept provides a strategic view or picture of the integrated IT system that is needed. In a nutshell, the Corporate Information Factory provides a proven way to organize our corporate information systems for maximum effectiveness. Without this strategic framework, investments in IT will produce a balkanized set of information systems that fail in terms of Systems integration Ability to change Infrastructure and support costs Performance and efficiency End-user satisfaction Contributions to organizational success Where does SAS Software fit into the CIF framework? Almost everywhere! SAS provides a platform-neutral, integrated suite of data management, analysis, and presentation software to generate and deliver intelligence about customers, suppliers, IT systems, and the organization itself. In the sections that follow, we look at the individual parts of the CIF and then describe which SAS products and solutions can be used in each particular location. An Overview of Corporate Information Factory To gain competitive advantage in recent years, organizations have been implementing new technological capabilities that promise to deliver best-of-breed data management, business intelligence, and information delivery solutions. The result is that most organizations have built or implemented many of the following technologies: Data warehouse Data repository Operational data store Data marts Exploratory data processing Data mining Online Analytical Processing Multidimensional Online Analytical Processing Internet and intranet capability Multidimensional and relational databases Star schema and snowflake relational database designs Data extract, transform, clean, load processes High-performance computing platforms (SMP and MPP) Data warehouse administration Metadata management Each of these technologies has great promise, but they’re all “point systems” designed to address a specific problem or need. Implemented without a guiding strategic vision and plan, they will not completely deliver on their promise but instead the combined result will be a confusing, intimidating, and wasteful hodgepodge of systems that don’t cooperate. In fact, in many cases the systems will compete with one another. Admittedly, each system may adequately perform the task for which it was designed, but without an overarching framework to guide implementation few synergies will be gained from the systems as a whole. At best, there will be a dysfunctional information ecosystem. At worst, there will be no ecosystem. An information ecosystem is a system of different components cooperating to produce a cohesive, balanced information environment. (Inmon, 2001.) The ecosystem is adaptable, changing as the needs of the “inhabitants” change. As in nature, when needs change the ecosystem adapts, changes, and rebalances itself. For example, a data warehouse that feeds a series of data marts delivering business intelligence is an environment common in marketing IT. When the need emerges to manage customer interactions, an operational data store (ODS) is added to the system to provide near real-time access to cu

rrent customer data. Data may no longer be loaded directly into the data warehouse but may instead flow into the warehouse through the ODS. 1

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