The data warehouse will usher in a new paradigm for decision making through the incremental delivery of business-driven analytical solutions. It will enable fact-based decision making to drive improved cycle times and greater efficiencies. The overall quality of decisions will be improved, with less time spent on data gathering and more time on value-added analytical activities.

Sound good? If you are thinking, "I'll take two of those," then look again. What have you really bought? Which decisions will be improved? And how? What value-added analytical activities will be possible? And when? What efficiencies and for whom? Does incremental delivery actually mean a partial solution? An effective vision statement for the data warehouse and associated decision support systems should create and sustain the organization's energy and commitment over a period of time. The vision must specifically describe the capabilities, decision process improvements and organizational levers which will be implemented. Who gets what, when and why must be specified.

This column presents the first of two discussions focused on the characteristics and key elements of an effective DSS vision. This month we'll look at how to frame and define an effective vision and review the first three of six key vision elements.

Perhaps you agree that an effective decision support vision would have been a good idea, but you've already implemented the first step in your grand plan ­ it's too late, right? Not at all. A vision is not something you print in multi-color and post by the elevator to get lost alongside the lunch menu. The vision is as much about sustaining the value of a decision support investment as it is about helping to get it implemented. In fact, once you have something running, a vision can serve to put that application in the context of the organization's strategy and growth agenda, making both the vision and the existing decision support applications more relevant.

Framing the Vision

An effective vision exhibits three essential characteristics. First, it establishes a view of the destination. The vision must describe the end-state. It should not be in abstract terms or be a collection of platitudes. Each person across each function within each department affected by the new systems must understand what is changing and when. Each individual must also be clear on how they will be personally impacted, whether it means learning new skills, taking on new job roles or interacting with different people. The second essential characteristic of the vision is that it defines a sense of direction to reach the destination. Articulating how the vision will be achieved is vital. Will the new decision support capability be implemented by line of business, by department or linked to a particular process which spans organizational boundaries? Showing where the organization will be in a year or two is not valuable if the vision doesn't outline the migration from the current state to the vision state. Third, and perhaps most important, an effective vision should evoke a sense of purpose as to why the trip to the destination is worth taking. Any major change initiative will impose some level of uncertainty and discomfort, even for the strongest proponents. Clarity around the reason and benefits ­ both for the organization and the individuals affected ­ is critical to gain and keep the necessary level of support to execute over time.

Defining the Elements

In order for your decision support vision to compel your company and to provide clarity, it must reflect your organization's culture and structure. Looking at the experiences of those who have succeeded in implementing and sustaining decision support initiatives reveals six elements which make up an effective vision (see Figure 1). Through these key elements, the essential characteristics of the decision support strategy are brought to life.


Figure 1: DSS Vision Elements

End-User Enablement. This aspect of the vision statement describes the performance levers focused on improving the decisioning environment for the user community. Training which focuses on tool usage is just the beginning. What needs to be communicated are the training opportunities which will guide users on how to apply the DSS in doing their jobs more effectively. The vision should also specify how the DSS will strive to capture and promote the reuse of knowledge assets and key learning associated with the DSS. For example, the organization might institute royalty programs which "pay" a user each time someone else in the organization uses a knowledge object that user submitted to the knowledge base. The prospect of a new DSS can be made less threatening and usage more appealing when such programs are included in the vision.

Decision Support Infrastructure. This describes the tangible changes which will be visible to the user ­ what will be showing up on the desktop, for starters. The users must have a concrete picture of the technical enablers which will accompany the new decision support capability. Determine if new technologies will be implemented or if new functionality will be delivered through existing toolsets. Or perhaps an analytical application will be imbedded as a component in another system, in which case it may go unseen ­ but hopefully not unnoticed ­ by the users. The vision must also describe how current analytical models (such as pricing, customer attrition or profitability) will be used and what new models will be introduced.

Decision Process Integration. Here the vision depicts how and how much the DSS will be explicitly incorporated into the steps of the decision-making process. If the DSS is intended to serve as an ad hoc analytical tool used at the decision-maker's discretion, probably only general usage guidelines are necessary. In fact, meta data services may suffice. By contrast, if the new tools and enablers are to be instrumented into discrete activities, the existing decisioning process will be significantly changed. For example, imagine a pricing process which has been automated with workflow technology in an insurance company. Modules of the DSS might be invoked at predefined work steps to ensure consistent and rigorous adherence to risk and profitability business rules. In this situation, methods and practice protocols must specifically reflect the new analytical applications. Regardless of the extent to which decision process integration is planned, the vision must specify how the DSS components will be linked to the decision-making procedures to provide a clear picture of what to expect.

Next month we'll discuss the remaining three elements of an effective DSS vision.

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