In the knowledge society the value-creating strategies and long-term viability of a firm depends on sustaining its competitive advantage. The resource-based view of competitive advantage suggests that organizations with valuable, unique and non-substitutable resources gain sustainable competitive advantage and superior performance (Moustaghfir, 2008; Zhang, 2007). Such firms can achieve these capabilities by developing new products and services that satisfy customers, and by restructuring and improvement their operational and strategic business processes. Tremendous amount of corporate data, information and knowledge that are related to operational and strategic levels in a firm are flooding into business (Lau, Ning, Ip, & Choy, 2004). The optimal use of corporate knowledge assets can improve the performance of decision makers and process workers (Moustaghfir; McGuff & Kador, 1998) and is a fuel that drives a firm’s engine of innovation (Henrad & McFadyen, 2008). Online transaction processing (OLTP) technologies and online analytical processing (OLAP) applications are useful for addressing the operational data needs (Sen & Sinha, 2005) and supporting strategic decision making (Jones, 2005) of a firm. The purpose of this essay is twofold. Fist, an identification of the categories of knowledge workers who could benefit from OLTP and OLAP systems is provided. Second, the relevance of these tools to the workers activities is discussed.
OLTP and OLAP
Online transaction processing (OLTP) systems are web-based transaction processing systems (TPS). TPSs are databases that process transactions within an organization (Haag, Cummings, & McCubbrey, 2005). Payroll, inventory, and sales systems are few examples of TPSs. OLTP tools are useful for addressing the operational data needs of a firm (Sen & Sinha). OLTP applications, with their online nature, decentralize computing power in an organization by placing that power in the hands of customers; hence they are characterized as customer self-service systems. The latter are integrated to databases and database management systems (Post & Kagan, 2001) to support the operational role of the knowledge worker. Various functionalities of the integrated system assist in the gathering of input, processing, and updating existing knowledge according to specific business rules. However, OLTP systems are not well suited for supporting decision-support queries or business questions that managers typically need to address. Such questions involve analytics that include aggregation, drilldown, slicing or dicing of data, which are best supported by online analytical processing systems.
The term OLAP was coined by Codd et al. in the 1990s (Davenport & Sena, 2004; Hedelin & Allwood, 2002). The OLAP system helps to manipule information to support strategic decision making. Jones (2005) defined decision making as the process of selecting from a set of options the alternative(s) that are most likely to lead to desired outcomes. He added that the decision making process is a knowledge-intensive activity which is subdivided into four phases: intelligence, design, choice and implementation. The knowledge needs of decision makers drive the knowledge derivation process (Davenport & Sena) and are supported by categories of technologies such as executive information systems, expert systems, agent-based modeling, data mining, and decision support systems (McNurlin & Sprague, 2006).
OLAP applications are decision support systems that enable the knowledge worker to make better and faster daily business. These applications are supported by data warehouses. The concept of data warehouse was developed to circumvent the limitations of operational databases for decision support systems. Lau et al. posited that data warehouse has excellent capabilities to integrate data from multiple transactional systems and improve their quality. OLAP enables various types of analyses of data organized as a star design (central fact table and dimensional tables) with average response times of OLAP that are lower than 20 seconds (Lau, Chan, Fun, & Wong, 2004).
Although OLAP technologies are powerful, Hedelin and Allwood argued that they also have some limitations. OLAP allows the knowledge worker to elaborate and test perceptive hypotheses about associations in the data. However, the existing information in the database remains unexploited if appropriate queries are not executed. Data-mining tools circumvent this shortcoming by looking for significant patterns without requiring the formulation of a specific hypothesis. This technology enables, as Hedelin and Allwood noted, knowledge discovery in addition to knowledge verification and provides sophisticated analyses to support decision making.
Process Workers and OLTP
The OLTP and OLAP applications users’ needs are different from one another (McGuff & Kador, 1998). An OLTP is useful for addressing the operational data needs of a firm (Haag, Cummings & McCubbrey, 2005). Some examples of OLTP include worldwide airline customer reservation systems, online banking systems, or financial applications including ledger, accounts payable and accounts receivable, payroll, manufacturing, inventory and human resources. These operational systems demand procedural specificity and support corporate mission-critical processes. Process workers (McGuff & Kador) using these systems perform day-to-day operations following specific business operational needs.
Although they cover a wide business functions, ERP systems are typical examples of operational systems. Sumner’s (2005) defined ERPs as software tools used to manage enterprise data as they are built with an integrated systems approach that establishes a common set of applications supporting business operations. An ERP system provides an enterprise database where all business transactions are entered, processed, monitored, and reported. Examples of ERP systems include SAP, PeopleSoft and Oracle. These systems cover different corporate functional areas such supply chain, receiving, inventory management, customer order management, production planning, shipping, accounting, and human resource management.
Decision Makers and OLAP
Contrary to OLTP technologies, OLAP applications have different set of characteristics that include generalization, aggregation, adaptability and long retention period (McGuff & Kador). Database content and structure tend to be more generalized and usually include several operational applications. The level of detail may be at a much higher level of aggregation. This aggregation creates a level of difficulty that the operation systems do not have to deal with. The data retention period for decision maker’s use is much longer, often measured in years. If the business strategies or structures change, then all the data must be modified. By using an OLAP system, the knowledge worker can manipulate enterprise dimensional data models to understand changes that are occurring in the firm and take appropriate business decisions (Lau et al.).
Good decision making is imperative for the survival of firms (Jones, 2005). The process of identification of critical alternative courses of action and development of a decision-making used multiple criteria decision approaches (Figueira, Greco, & Ehrgott, 2005). According to Jones, this process forges the decision through a choice made from among available alternatives. The main goal of OLAPs is to improve the quality of a decision. The quality of data determines both decision quality and the quality of the OLAP. The integration of OLAP and OLTP technologies through a common data warehouse technology provides decision support to firms (Gorla, 2003). Data warehouses extract data from different operational databases to facilitate decision-making by management people by employing a set of user-friendly tools, like data mining and presentation techniques (Chowdhury, 2007). Traditional decision making phases take the decision maker through knowledge gathering, alternative formulation, and finally a selection of the alternative. The purpose of data warehouses is to enforce a centralized store-house representing a single source of truth for the entire organization. This centralization allows managers to access analytical databases through OLAP interface and analyze corporate data on various dimensions (Hedelin & Allwood). OLAP technologies allow decision makers to evaluate corporate changes over time, obtain an overview of the business operations and perform various analyses. A data warehouse project is usually business-driven and will work to improve the direction of the organization. The first priority of the business-driven data warehouse approach, as Chowdhury noted, is the formulation of a lit of questions. This list is a set of analytical problems that managers consider as critical success factors for the future of the business. It evolves to a dimensional model which is combined to the data model OLAP cubes to build reports that answer managers’ questions.
In this post, OLTP and online OLAP were identified as two examples of front-end support tools that knowledge workers can employ to support organizational goals. OLTP tools are useful for addressing the operational data needs of a firm. They decentralize computing power in an organization by placing that power in the hands of customers. OLTP applications are useful for process workers in their day-to-day operations. However, OLTP systems are not well suited for supporting decision-support queries or business questions that managers typically need to address. By using an OLAP system, decision makers can manipulate enterprise dimensional data models to understand changes that are occurring in the firm and take appropriate business decisions. The integration of OLAP and OLTP technologies through a common data warehouse technology provides decision support to firms. This integration enforces a centralized store-house representing a single source of truth for the entire organization and managers to access knowledge bases and analyze corporate multidimensional data.