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Enterprise Decision Management Bridges Proprietary and Packaged Applications

  Article published in DM Direct Newsletter
January 14, 2005 Issue
 
  By James Taylor

There are things in this world that are refreshingly unique ... the structure of a snowflake or the whorls and ridges of a person's fingerprints. But when it comes to the fundamentals of business, there is a remarkable uniformity in the goals and challenges we all face. The pursuit of profits requires finding ways to raise revenues and lower costs.

On the revenue side of the equation companies seek ways to expand their customer base, provide more compelling and attractive products or services, make better marketing offers and set prices correctly. From a cost perspective, companies look for ways to handle more workload in less time with fewer people, retain profitable customers, reduce inefficiencies in work processes and comply with regulations and policies to avoid penalties and fines.

Companies have the same underlying goals as competitors in the same market. They face all the same business conditions. So how does a company distinguish itself to compete and succeed? It must make better decisions about where to place priorities, where to spend money and how to process transactions on a daily basis. To a large extent, business management is equivalent to decision management. Executives make strategic decisions for the entire enterprise. Line-of-business managers make policy decisions about how specific work tasks are to be accomplished. Customer-facing employees make decisions about how to treat individual customers. But what of the automated business systems that companies have installed? Do they help make these decisions? By and large they do not, being focused on the automation of processes and the recording, rather than the making, of decisions.

Automated support for business decisions has traditionally been available in one of two flavors. The first flavor is for a company to use ETL, EAI and business intelligence products to integrate and describe the data in the various databases used to store data, often in some kind of executive dashboard. All of this effort is typically focused on decision support, not decision automation.

The second option is to rely on application-specific reporting and automation from software vendors. These fall under categories such as CRM systems, ERP systems, Web-based shopping cart software and thousands more. These systems are attractive in establishing a structure and automated process around a business function but can be difficult to modify to reflect the differences in decision-making between purchasers. Decisions automated in this way also suffer from being part of only one system - I might automate my cross-sell offer in my call center but not be able to use that automation in my letter shop when printing letters.

Recently a new approach, enterprise decision management, has garnered attention for its ability to improve the precision, agility and consistency of operational decision making. Enterprise decision management provides a framework for setting decision criteria, automating those decisions in computer systems and reviewing and modifying the decisions as often as needed to respond to changing conditions.

Setting Decision Criteria

There are two approaches for making the decisions on which to run business operations. One is to set them explicitly and the other is to derive them from data. Every business has the former type of decision. Executive-level policies must be followed, laws and regulatory standards must be complied with and business experts take advantage of their experience and judgment. To appreciate the importance and value of this kind of decision criteria, all one has to do is to delve into compliance requirements such as Sarbanes-Oxley, Gramm-Leach-Bliley and the Health Insurance Portability and Accountability Act (HIPAA). The decision criteria used in this kind of decision making can best be described using business rules - statements of conditions and the actions to be taken if those conditions are met.

The second method for making effective decisions is to base them on data. If a company has tracked and stored historical information on its operations and its customers, this data can be used to help guide decisions. Powerful software packages are now available to make predictions from existing sets of data, to generate scoring algorithms for weighting different data characteristics and to segment populations into sub-groups that should each be treated differently for a particular business decision. When companies do not have their own data available, they can sometimes make use of pooled data from other companies in their industry or from basic demographic collections sold by third-party providers. The decision criteria used in this kind of decision-making can best be described using predictive analytics - analytic or mathematical models that predict likely future results from historical data.

Automating Decisions

To automate a decision, one must derive the decision criteria for that decision and the actions to be taken when those criteria are made. Once a set of decision criteria and actions has been established, it must be embedded into software so that it is available for use by personnel and systems throughout the company. Variables and equations derived during analytic modeling must be transformed to code so information from new transactions can be used to calculate a predictive measurement. Rules need to be executed in the proper sequence and at the proper time, depending upon input and calculated data values. Execution engines that enable this are essential to enterprise decision management. They carry out the task of ensuring that all decisions are made in a consistent manner, regardless of the origin of the transaction. A powerful decision execution engine with flexible deployment facilities can also cut implementation time, effort and cost by orders of magnitude when putting a new system into production.

Modifying Decisions

The third word in enterprise decision management may be the most important. Both business and technical professionals must be able to easily manage the decision processes and criteria within their systems. For business experts, this means exposing the rules used by automated systems in a non-technical fashion - free of programming syntax and operational code. Conditions, actions, weightings and dependencies require business interfaces using familiar business terms and graphical depictions so the responsible business people can quickly interpret and change critical factors. Of course the management interfaces also need to incorporate authorization, access, and security along with ways to audit, track and test changes before and after they are applied to production systems. A well-designed enterprise decision management architecture incorporates these capabilities.

For decisions that are based on data-driven models, systems should allow both automatic and on-demand review and monitoring of the data and results flowing through the application to make sure they conform to underlying assumptions used when the models were created. Perhaps the most obvious example of this need is investment strategies before and after the Internet boom and bust of the late 1990s and early 2000s. A model for stock market growth and market segment attractiveness that was "trained" with data gathered between 1996 and 1999 would look quite different from a model based on data gathered between 2000 and 2003. This is obvious in retrospect, but in times of rapid change, it is easy to fall behind the curve in noticing when a new set of predictive criteria is needed.

The ongoing ability to review, modify and deploy changes to business strategy is key to successful enterprise decision management. The era of creating a single COBOL program that stays in operation for 30 years is behind us. Modern systems must be adaptable and responsive to changing business needs, competitive scenarios and market direction.

Practical Applications of Enterprise Decision Management

To demonstrate one scenario involving enterprise decision management, let us take a credit card example from the world of financial services. The market is very well-defined in terms of the types of products that are offered, how they are sold and how they are regulated. It would seem that different companies have little chance of distinguishing themselves from their competitors. But we will see that proper application of decision management can have significant consequences.

A large credit card issuer has an astonishing inventory of products that can be changed and reconfigured at will. Each combination of card type, annual fee, interest rate, promotional tie-in and background design is a different product to the credit company. Consider the difficulty in marketing this inventory to a massive consumer market. The credit company cannot publish a catalog and let purchasers select any combination they want. Products must be matched to a consumer's credit standing, likelihood of profitability, location, purchasing habits and interests. Customers may be reached through direct contact on a call center line, over an interactive Web site, through a direct mailer or via e-mail. Each interaction point has its own costs associated with it. For maximum effectiveness, offers should be consistent and reinforced at each contact point so the customer does not receive conflicting information. At the same time, different credit providers may have different strategic goals. For instance, one company may be trying to grow market share, while another is intent on increasing average credit profitability by weeding out less desirable accounts.

Each company chooses marketing campaigns and recommendations for prospects based on a combination of empirical factors and predictions based on data analysis. They consider federal, state and industry regulations in determining what can and cannot be offered. They examine the results of past offers made to individuals with similar demographics or behaviors in order to predict what will have the highest likelihood of success. And they balance business objectives against business costs to maximize profitability of their actions across the entire customer portfolio.

An enterprise decision management approach allows the credit company to run, review and deploy models based on current data sources. It gives a structured framework to line of business marketing managers and corporate compliance experts to introduce, remove or modify products and marketing rules at will. And it organizes and combines these decision factors into overall decision management applications so that decisions can be made, tracked and reported.

Bottom-Line Benefits

Enterprise decision management offers an integrated approach to dealing with the decision management problem. Instead of custom programming linking silos of business expertise, data modeling specialization and strategic management, an organization can have a unified approach to creating, deploying and executing decisions across its operations. It can incorporate sophisticated predictive analytics and scoring methodologies with business-level policy rules and compliance requirements.

 

This enterprise-level approach provides benefits such as increased precision and accuracy in business decisions; consistency of operations across different systems, locations and channels; and greater responsiveness to changing business conditions.

 

When applied to revenue generating activities such as marketing, customer interaction, product recommendation, up-sell/cross-sell offers and pricing, an enterprise decision management strategy can boost profits and reduce non-essential costs. It can be used to more closely align pricing to risk and help with acquisition and retention of the most desirable customers.

By lessening the burden of ongoing decision maintenance in automated systems, companies improve their time to market for products and services, and lower their application maintenance and support costs. Many companies have found that almost all the change requests for existing systems relate to changing the business rules within them. If business owners can safely make these changes themselves then IT maintenance on the system often drops precipitously.

Even in a business climate with so many common concerns and goals, companies can distinguish themselves and create a competitive advantage through the decisions that drive their operations and the efficiency with which they are managed. This is the promise of enterprise decision management.

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For more information on related topics visit the following related portals...
Business Performance Management and Enterprise Intelligence.

James Taylor is vice president of marketing for Enterprise Decision Management at Fair Isaac where he is responsible for working with clients to identify and bring to market advanced decision management solutions that better solve the demands of business users and IT. You can contact him at jamestaylor@fairisaac.com. For more information, please visit www.fairisaac.com/edm.



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