Mark Twain once wrote that a report of his death was an exaggeration. This is also true of claims that the mainframe is a near-death technology in the mission-critical world of today's robust business intelligence (BI) applications. Conventional wisdom says the mainframe - the "powerhouse" of corporate computing - is simply too costly, too complex and incapable of supporting a comprehensive BI system. Not so.
A critical look at the true cost, complexity and capability of the mainframe - home to 70 percent of the world's critical transactional data - reveals a competitive BI platform. Countless innovations make the mainframe a reliable, cost-effective, large-scale platform capable of satisfying core applications and BI needs. Far from being dead, the mainframe is a valuable asset to a company wanting to implement BI and analytic applications.
Yet myths persist about the mainframe's BI capability. The intent of this three-part series is to debunk the top 10 mainframe myths and illustrate why current BI solutions can be successfully deployed on a mainframe. After all, when choosing where to place BI applications, companies should weigh all their options, as well as consider applications requirements two and three years down the road. If they make decisions based on misconceptions concerning the relative cost, complexity and capability of mainframe computing environments, they might overlook the solution that best fits their needs.
BI has come a long way from the early days of simple reports and back-office statisticians. Today's BI is in the boardroom and spread throughout the organization as every part of corporations demand decision support. This new BI has ramifications not only in BI architectures, but also in the technologies used to support these environments. There's tremendous pressure on the BI environment as terabytes of data are accessible, response times mimic operations and globally dispersed users - both sophisticated and novice - produce simple queries and complex models.
Beginning in the 1990s, distributed servers were used increasingly throughout the enterprise to handle departmental and other workloads. Distributed systems were particularly attractive for BI workloads as the server architectures matured and the software environment developed, including improved operating systems, database management systems and data access and delivery tools. In time, server technology evolved across all platforms. As distributed systems continue to emulate more of the mainframe's unique functions, such as partitioning capabilities, virtualization technologies and workload management controls, the mainframe also has matured, supporting more of the software vendors and offerings that drive the BI market today.
Why does the reliable mainframe have such a bad reputation for BI capability? Like Twain, it's misrepresented.
Myth I
Mainframe total cost of ownership (TCO) is too high. Deploying any new environment can be expensive, regardless of platform choice. Hardware and software expenses are only part of a solution's overall costs. When using price as a criterion for selecting a BI platform, a true comparison requires considering the TCO. For any application environment, total cost has many components, including labor, hardware, software and electricity. The most expensive component of any solution is the staff required to support the system.
In a distributed server environment, costs go up linearly with additional workload. Adding capacity means adding servers. Each additional server increases the human resources needed to manage and maintain the environment. In the mixed workload mainframe environment, initial hardware costs are higher, but the per-unit cost of incremental capacity decreases as the total workload grows. With the mainframe, incremental capacity can often be added without increased staffing to manage and maintain the environment. In an existing mainframe environment, many of the initial costs for deploying a new solution already have been paid. This makes incremental costs associated with adding BI capabilities much lower than those for a new environment. Creating a data warehouse from data that may already be housed on the mainframe and adding a user tool gives users immediate access to valuable BI capabilities.
Further, by offering dedicated specialty processors, the mainframe has recognized the need to target capacity to address specific workloads. These provide a high-speed engine that reduces overall processing costs when data is centralized on the mainframe. The economy of this solution helps break down the walls between transactional data stores and BI, enterprise resource planning (ERP) and customer relationship management (CRM) applications. This also minimizes the need to maintain duplicate copies of data across a pool of discrete systems while providing high levels of security for critical corporate data. By reducing the need for multiple databases and consolidating applications onto the mainframe, the platform's inherent strengths are leveraged to manage the concurrent sharing of data by batch, online transactional processing (OLTP) and online analytical processing (OLAP) applications.
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