Now more than ever, business managers are challenged to give up their old ways of decision making. They need to rely less on managing by the seat of their pants, going with a "gut feel," utilizing only anecdotal evidence and rationalizing by saying, "I just know this is the best thing to do in this situation!" Facts should lead us to better decisions. Facts provide the basis for improved decision making as well as monitoring or measuring the performance of the decisions over time. The advent of business intelligence tools made it easier for us to access business facts that can help us make better decisions. However, it's analytic applications that will make "managing by fact" mainstream.

The term analytic applications was coined in 1997 by IDC to refer to packaged application software designed to predict, measure and optimize business performance. Many types of analyses fall within the various categories of analytic applications. IDC separates analytic applications into three main categories.

The first category is business peformance management which includes financial budgeting, consolidation and forecasting. Included in this category are balanced scorecards, the top-down analysis that is the successor to executive information systems (EISs). Balanced scorecards start with high-level business strategies and translate them into quantifiable measures of business performance. The main differences between business performance management systems and EIS tools are that the former incorporates models with specific business indicators and the audience for business performance management systems is wider than that of EISs. Early business performance management applications consisted solely of financial measures regarding performance against budget and/or forecast. These measures are still important; however, now a balance of financial measures with customer-related metrics is essential for determining overall business health.

The most critical component to business performance management, including balanced scorecards, is the more bottom-up analysis of activity-based management or activity-based costing. Activity-based management and activity-based costing build a model of activities within a business process that associates cost with customers, products and channels. The resulting model can be the basis for process-specific analytic applications such as customer profitability analysis, product profitability analysis or channel profitability analysis. The reason activity-based costing is so important for an organization is that without it balanced scorecards are forced to rely on potentially misleading cost allocations from core accounting systems. Activity-based costing puts the actual costs related to a high-activity customer with that customer, rather than allocating an average cost which will not reveal a true picture of the customer's profitability. With more emphasis being placed on accuracy and facts, the true customer profitability figure can drive many decisions within the organization regarding how to treat the customer, what to offer the customer and so forth. In fact, a good way to start with analytic applications is to integrate activity-based costing with strategic applications such as balanced scorecard. This will help with the marrying of top-down and bottom-up methods of analysis.

A second category of analytic applications includes those systems designed around customer relationship management (CRM). CRM analytic applications address customer retention, customer churn analysis, cross-selling optimization and marketing optimization. The growth within this market category is projected to be high, despite the fact that recent economic trends have placed CRM vendors into a definite "wait-and-see" category. This category includes Web site analysis and multi-channel analysis, as well as vertical industry-specific CRM analysis (such as financial services, telecommunications, retail, etc.). Many CRM vendors are challenged to provide CRM analytics in conjunction with their operational or transactional applications. Yet the analytics, in many cases, provide the heart of what needs to be addressed on the operational front. Understanding what underlying customer churn issues exist is essential to defining successful customer treatment strategies. Marketing analysis is the largest sector of CRM analytics, including marketing campaign management as its key component.

The third category of analytic applications incorporates operational and production analytic applications. The whole idea is to drive efficiency into the production process based on a time-oriented analysis of data integrated from multiple sources. Types of operational systems include supplier performance analysis, demand planning, workforce optimization, skills inventory management, supply chain optimization, procurement optimization and quality control.

Analytic applications have evolved and are readily available in the marketplace from a number of vendors. Granted, it's tough to change from "gut feel" to fact-based decision making ­ it feels more comfortable the old way. In the future, however, while intuition will still be evident, facts will be more important in making ongoing business decisions that will positively impact the bottom line, customer retention and supplier performance. Before you set out to evaluate and purchase the right analytic applications for your organization, however, realize that this decision also needs to be based on facts, not myths. Beware of the following five myths of analytic applications!

Myth 1: Analytic applications and query tools are the same.

If you really believe this, I hate to burst your bubble, but they are not the same thing. Query tools facilitate ad hoc types of analysis, whereas analytic applications have identified repeating indicators that are coded into an application that runs periodically to deliver ongoing and comparative facts to decision makers. Most business intelligence reporting and online analytical processing (OLAP) tools deal with historical data intelligence and interpretation, while data mining helps develop predictive understanding about customer behavior or other business events. Both historical analysis and predictive insight can be built into analytic applications to provide point-in-time comparisons of business activity that can be used to analyze and direct business decisions regarding products, locations, suppliers and customers.

The key to analytic applications (versus query tools) is the act of ongoing measurement and comparison. Where a single query can provide a snapshot of activity (now or at some point in history), analytic applications can provide continued trending which is helpful for determining ongoing business strategies and measuring the results of decisions.

Myth 2: Financial measurements are still regarded as the best indicator of the success of a business.

Believe me, your organization's controller will still tell you that financial measurements are the most indicative of success. However, the truth is that financial measurements are (and always will be) important, but they alone do not indicate success. More and more, organizations are relying on measures such as customer satisfaction, customer retention or employee satisfaction. The idea behind balanced scorecards is that companies need both financial and nonfinancial indicators to determine success. The reporting of bottom-line profits is, of course, critical to a company's long- term success; and a quarter where profits were increasing would, in most cases, bode well for the organization. However, it would be incontrovertible evidence of a company's need to improve if customer satisfaction scores were steadily declining despite an increase in revenues or profits. Declining customer satisfaction is an indicator that revenues may suffer down the road as customers are likely to search out competitors for products or services.

Also, clickstream analysis has become an important (though admittedly narrowly focused) indicator of "eyeballs" or how many customers are viewing your organization's offerings. Clickstream analysis uses information from the Web server log to monitor and measure Web site visits. The idea is to improve site performance from both a technical and content perspective, improve the customer experience (and, of course, loyalty), contribute to overall knowledge about customer channel usage and identify opportunities and risks. Clickstream data needs to be integrated with other channel data to provide an overall picture of customer adoption. Multichannel CRM analytics will be a growing area for the market in general as companies seek to coordinate their customer- related activities within a common overall direction.

Myth 3: Analytic applications are comprised only of balanced scorecards or executive dashboards.

Scorecards and dashboards are a big part of analytic application development. However, they are not the only activities being accomplished within analytic applications. An example of additional activity involves packaged supply chain management applications. These applications have focused on process-specific data, taking a horizontal view of data and focusing on providing insight into opportunities for enhancing revenues and controlling costs through both historical and current data analysis. Most of the ERP vendors (such as SAP, Oracle and PeopleSoft) are complementing their transactional systems with analytic applications. This is a conscious effort on their part to expand their vertical focus into horizontal analytic application functionality.

Another example of an analytic application is demand planning or forecasting within the overall category of materials management or supply chain applications. Demand planning is important to appropriately deploy enterprise resources to optimize revenues and reduce expenses. However, demand planning requires some information that is outside of a company's databases or data warehouse which makes it difficult to acquire and integrate that information. The need is to relate the external information with information available internally. One of the key enablers of this data integration is extensible markup language (XML), which provides standards for content and data definitions. Use of XML by all members of the supply chain will be necessary to achieve dramatic intercompany demand planning results.

Myth 4: Analytic applications are only of interest to internal business executives.

It's true that internal executives and managers find analytic applications such as balanced scorecards compelling indicators of the organization's health and well-being. However, they are not the only audiences for this type of analysis. Increasingly, a company's investors and leading analysts (both technical and equity analysts) are looking for information regarding market share, customer retention, customer satisfaction, etc. In fact, Patricia Seybold, CEO of the Patricia Seybold Group, has indicated that more analysts are looking at "a company's customer base, the churn rate of customers, retention rate, growth rate, market share, the lifetime value of a customer" (TheStreet.com, 4/23/01). These metrics, which are normally found on dashboards or scorecards, are now of interest outside the organization, which only makes their importance more pronounced. It's not just good internal fact- based management that dictates the use of analytic applications, but providing fact-based information to the external world as well.

Myth 5: Analytic applications require a data warehouse or data marts.

A technique that has been recently developed circumvents the need for a central physical data warehouse. This technique is known as "distributed query management," and it provides a logical view of underlying heterogeneous data sources, whether they be data warehouses, data marts, legacy systems, operational data stores, operational databases or external data stores. Distributed query management provides a logical view of underlying heterogeneous data sources without the need for creating a central physical data warehouse. This method parses queries and, depending on the query requirements, gets data from separate data sources. By utilizing this method, traditional data warehousing can be combined with real-time monitoring and alerting.

There is a genuine need for combining historical analysis with real-time monitoring and alerting. This requires new approaches to data integration (such as distributed query management) and data delivery (such as the ability to notify appropriate contacts via wireless devices and voice portals). Other new approaches will be necessary to provide seamless interaction utilizing historical data analysis with real-time transactional review and monitoring. The reason these approaches are necessary is that customers are demanding the more personalized treatment that requires more integrated information management.

IDC states that the digitization of data, primarily due to e-commerce and the Web, is growing at an astonishing rate of 100 percent annually. While storage vendors drool at this proclamation, the majority of businesses are questioning how to harness this information to help them make better decisions. While business intelligence tools assist with point-in-time decision making, analytic applications are the "killer apps" which can help ensure that critical business decisions are based on facts, even with the existence of more and more data. The increased amount of information accessed by analytic applications is not analyzed within balanced scorecards alone, but within a wide variety of systems that deal with operational performance and customer relationship management, in addition to business performance management. Despite the overwhelming proliferation of data (or maybe because of it!), responsible business management demands that both internal business managers and external investors or analysts base decisions on facts ­ and nothing but the facts! Analytic applications can help us do just that.

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