The ability to assemble and analyze information has become a powerful source of competitive advantage for many organizations. However, most companies have not harnessed this capability and are operating at a clear competitive disadvantage. The reasons? It has been excessively expensive and difficult to retrieve and analyze corporate information - until now. In particular, affordable business intelligence (BI) solutions have not been available to the small and medium-sized business (SMB) market, and while many large companies have been able to afford these solutions, they have been frustrated in their attempts to effectively implement and utilize BI capabilities within a reasonable timeframe. The BI environment is changing rapidly as new solutions are emerging that allow many more companies (and a broader group of users within these companies) to access key information and create significant business value. These next-generation solutions offer full BI capabilities, rapid implementation with many fewer demands on a companys business and technology resources;,dramatically lower cost and lower risk than traditional solutions, and the flexibility to accommodate business changes as they occur. These new solutions will have a major impact on the BI marketplace, providing companies with critical business information and a valuable competitive edge. Increasingly, many leading companies recognize that access to information and analytics is integral to successfully operating and growing their businesses. By being able to view and analyze information throughout their organization, they are able to understand the profitability of market segments and sharpen marketing approaches; improve their ability to serve their customers; better manage their supply base; more rapidly integrate acquisitions; and enhance their overall efficiency and operational performance. Companies such as Wal-Mart, Dell and Amazon have focused analytics on supply chain costs and inventory optimization; Harrahs, Capital One and Barclays have leveraged analytic and BI tools to enhance customer selection, loyalty and service; and MCI, Marriott and Verizon have focused on pricing, profitability and financial analysis.1 The abundance of data now available from both internal and external sources presents a tremendous opportunity for companies to leverage information for competitive advantage. Over the past two decades, large and small companies have implemented new enterprise software, particularly transaction systems, to help them manage their businesses. These new systems include broad enterprise resource planning (ERP) or related management capabilities such as financial management and accounting, order management, asset management, customer relationship management, marketing and sales management, supply chain management, transportation, procurement and related functionality. Transaction systems provide essential capabilities and allow companies to operate much more efficiently, but also generate large volumes of very valuable data. Paradoxically, access to this transaction data is often limited. The data that is organized and stored to optimize the transaction system has fundamentally different requirements than the data needed for rapid access, analysis and reporting. Unfortunately, end users cannot easily extract data, combine it with data from other transaction systems, analyze it or generate reports - precluding their ability to look at data in new ways or gain new insights. The compelling need to access and leverage this valuable data, which is resident but not available to many companies, has driven the growth of BI.
Historically, implementing BI solutions has not been for the weak of heart or those lacking money, resources or time. BI programs are large, lengthy, complex and expensive undertakings that demand careful management. According to industry experts, more than 50 percent of large BI programs fail to deliver to expectations, and many of these projects are abandoned as a direct result of their implementation complexities and/or their failure to deliver anticipated value. Traditional approaches to BI systems necessarily lead to multiphased and very complex projects. Providing information from multiple transaction systems to senior executives and managers for business analytics involves an extensive set of activities - data movement, data transformation, presentation layers, data analytics tools, master data management (MDM), metadata management and overarching data governance. And the lifecycle for implementing BI systems will progress through many steps, including requirements analysis, source systems analysis, gap analysis against existing capabilities, solution architecture, data model development, change management for data governance and implementation. Serving the analytic needs across all functions of the company complicates the design of data models and data transformation processes. Inevitably, companies will start with a goal of a single data management framework, but then implement multiple instances at any or all of the layers of the operational data store (ODS), dimensional data warehouse and data marts. Solution architecture and design must involve the evaluation of multiple special purpose technologies for extract, transform and load (ETL), standard and ad hoc reporting, custom analytics, MDM and metadata management. The project lifecycle is extended by the need for validation of findings at each phase of the project and the complexity of vendor technology selection for multiple best-of-breed technologies or integrated BI platforms. Further, the organization is faced with obtaining the core competencies for each of the technologies and layers of the data management process. In the best case, the typical implementation of BI entails lengthy, multiphase project lifecycles and the investment in multiple technologies. The obvious issues with this are the investment in project resources, the investment in technology, managing data quality and organizational acceptance. The more subtle risk is the ability to respond to changing business strategy, both in the initial project lifecycle as well as for system upgrades. When a company attempts to seize new opportunities, the BI infrastructure must keep pace with the need to evaluate metrics of market entry and performance management. Faced with this challenge, business analysts in both large and small companies often choose to move some or much of reporting and analysis to technologies that they can maintain and understand, most notably Excel spreadsheets and Access databases. While the data management and data governance process is severely compromised when data lands in the desktop tools environment, the more serious issue is how the data is sourced. Business users will extract data from whichever environment is available to them: source transaction systems, ODS, data warehouse or data marts. Inevitably, companies lose any attempt to achieve a single version of the truth. Data management and traditional data warehouse techniques (e.g., conjoined dimensions) are lost because there is no consistency (and no predictability) on the sourcing of data for business analysis. So while these solutions fill a gaping hole in information access, they are neither an enduring nor an ideal answer.
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