Editor's Note: This article appeared in the Analytic Applications Supplement in the April 2001 issue of DM Review.
IDC created the analytic applications category in 1997 to call attention to a growing class of packaged software designed to understand and improve business performance. Analytic applications now represent over $2 billion in total software license and maintenance revenues, projected to grow to $6.7 billion by 2004. This represents a compound annual growth rate of 28 percent, significantly higher than the overall growth rate for business applications.
Analytic Application: Definition and Forecast
An analytic application must meet each of the following three conditions:
- Process support. This condition relates to packaged applications software that structures and automates a group of tasks pertaining to the review and optimization of business operations (i.e., control) or the discovery and development of new business (i.e., opportunity).
- Separation of function. This condition means that the application can function independently of an organization's core transactional applications, yet it can be dependent on such applications for data and might send results back to these applications.
- Time-oriented, integrated data from multiple sources. The application extracts, transforms and integrates data from multiple sources (internal or external to the business), supporting a time- based dimension for analysis of past and future trends, or it accesses such a database.
There are three major types of analytic applications that meet these criteria. The differences between these major categories relate to the business subjects or processes that they cover:
- Financial/Business Performance Management: analytic applications designed to measure and optimize financial performance (e.g., budgeting) and/or to establish and evaluate an enterprise business strategy (e.g., balanced scorecard).
- Operations/Production: analytic applications designed to measure and optimize the production and delivery of a business's products and services (e.g., demand planning, workforce optimization, healthcare outcomes analysis).
- Customer Relationship Management (CRM): analytic applications designed to measure and optimize customer relationships (e.g., customer profitability, retention, marketing analysis).
Figure 1 shows the growth of each of these major types of applications since IDC began reporting of these markets in 1997. Financial analytic applications were the first to be packaged and are the largest single category. CRM analytic applications are the fastest growing, nearly doubling year over year. Operations/production analytic applications are highly verticalized. The growth in business-to-business (B2B) e-commerce, especially supply chain exchanges, will spur growth in these types of analytic applications. (More detail on the segments of each of these major categories is provided in IDC's recent report "Analytic Applications Market Forecast and Analysis: 2000-2004").
Figure 1: Growth in Packaged Analytic Applications: 1997- 2004
Analytic Applications: Major Trends
The following major trends should expand the scope and the depth of analytic applications:
- Growing importance of planning and simulation
- Incorporating rich data types as data sources
- Competing models of suites incorporating analytics
Growing Importance of Planning and Simulation
In the near term, planning and simulation should increasingly emerge as a component of analytic applications. Simulation models can be used in the decision-making process in order to evaluate alternative courses of action.
Business issues such as the following can be addressed:
- Should a utilities company acquire another plant or sell one of its existing plants?
- Which policies should a retailer adopt in handling markdowns for seasonal goods?
Technical specialists build the simulation model using sophisticated tools. Decision-makers then apply the model, gauging the probable impact of a planned course of action. A decision is made as to the optimal set of rules (as determined by human decision-makers or fully automated agents). These rules then govern future operations.
Planning and simulation can add value to all levels of analytic applications: strategic, process-specific and foundational.
- Strategic analytic applications. The goal of strategic analytic applications such as balanced scorecard is to develop an overall plan governing the full range of business operations. These objectives are disseminated to operational groups (finance, customer service, etc.) where they become targets to be achieved in a particular business function or process. This sets the bar for accountability.
- Process-specific analytic applications. Process-specific analytic applications such as customer retention or quality analysis work to optimize business performance according to specific targets set within the strategic plan. Planning and simulation test alternative policies to be applied to a specific process, aiming to meet the targets or objectives. The difference is that the time horizon is typically shorter than the time horizon for strategic applications.
- Foundational analytic applications. Foundational applications such as activity-based costing provide consistency in calculating metrics used in strategic and process- specific analytic applications. Planning and simulation has a role here as well. Predictive activity-based costing could be deployed to support applications such as predicting customer profitability under particular scenarios.
Incorporating Rich Data Types as Data Sources
There are signs of major progress toward enhancing analytic applications by incorporating heterogeneous data types to provide a richer base of information on which to perform analysis. There are many data types (e.g., image, video and sound), but the two that are coming into their own are textual and spatial data.
Unified access to both structured and unstructured data is not yet a reality, but significant progress is being made. (See "Toward Unified Access: Three Waves of Information Portals for Knowledge Management," IDC #22631, June 2000).
One of the application domains that should be ripe to exploit this technology is CRM. The sheer volume of the available textual data (e.g., from customer communications) dwarfs the available structured data, such as sales data. The problem is that the textual data is not set up for analysis. Without providing some structure to this unstructured data (in other words, identifying categories or subjects within the text), it's not possible to aggregate or analyze the information to discern trends.
However, the advances in categorization of text should lead to the exploitation of textual data for analytic applications. The benefits of incorporating new data sources will be reflected in analytic applications with improved predictive capabilities.
- CRM-related analytic applications will leverage inbound customer communications along with sales data to build better predictive models. The resulting benefit will be the ability to drive more informed strategies for cross-selling and customer retention.
- Supply chain-related analytic applications will provide better supplier intelligence, taking advantage of reports and B2B communications to gain better intelligence on supplier performance. This is essential for the new model of supply chain exchanges to succeed, replacing proprietary supplier networks where businesses owned the data on their preferred suppliers' ability to deliver quality products on time.
- Workforce management analytic applications will result in better information on employees. Taking advantage of project reports, resumes and compensation information will drive more informed strategies for employee recruitment and employee retention.
Location information has long been part of dedicated geographic information systems (GIS). IDC has reported on the changes in this marketplace that occur as mainstream databases and business applications become spatially enabled.
It has been claimed that the Internet makes geography irrelevant to business systems. However, the more experience we have with e-business, the more we understand the dependency of the virtual world on the physical world. Products must be delivered through physical transportation systems. Many industries, such as telecommunications, are constrained by the scope of their physical infrastructure, limiting which customers it is profitable to serve. Spatially enabled CRM analytic applications should emerge to address these business issues.
Competing Models of Suites Incorporating Analytics
Analytic applications arose as departmental applications addressing the review and optimization of specific functions within a business. As a result, separate applications arose within specific functional areas. More recently, application suites began to be delivered, promising added value and reduced cost of integration over individual applications.
Demand for these departmental, process-specific applications will continue. If the market for business applications (mostly transactional) is a guide, standalone (or "best-of-breed") applications in the aggregate will have a greater share of the overall marketplace. However, the largest vendors in the applications market have been the vendors who supply application suites.
Analytic applications eventually will move to suites, following the general rule for business applications. However, there are two possible models for suites that include analytics:
- The first model, advanced by ERP suite vendors and major analytic tools specialists, is a suite of analytic applications. These suites tend to begin with financial analysis and business performance management applications, bolstered by activity-based management. Once this framework is in place, the suite can be broadened to include CRM analytics and vertical-specific operations/production analytics.
- The second model, advanced by CRM application vendors, is to provide an integrated application suite across CRM operations and CRM analytics, providing finer segmentation and personalized recommendations and policies. The most popular starting point for this is marketing (including e-marketing), but expanding to other areas such as customer service is important. Eventually, this CRM analytics-plus-operations suite will need to broaden to analyze non-CRM processes that impact CRM. For example, customer satisfaction depends in part on the efficiency of the order management process, and customer profitability depends in part on the efficiency of the billing process.
At the present time, the two suite models are distinct and appeal to different buyers. The first model appeals to financial managers and high-level executives, while the second model appeals to managers of CRM functions. The two models are on a collision course, however, as each inevitably expands to subsume the other. The competitive struggle in the analytic applications marketplace has only begun, and the eventual winner or winners have yet to be decided.
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