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Decision-Driven Warehouse Whitepaper  
 

The Decision Driven Warehouse
 
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Executive Summary
Decision-Driven Warehouse Architecture
Building the Data Warehouse
Summary
 
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Executive Summary
Today’s Decision Support System (DSS) market place has about as many definitions of “warehouse” as vendors: Centralized warehouse, operational data store, distributed warehouse, virtual warehouse, data mart, replicated online transaction processing (OLTP) data. If you are currently undertaking a Data Warehousing project, you are faced with trying to select the right approach from a myriad of options.

Proponents of each warehouse strategy often defend their design as the only acceptable one and focus on the physical aspects of extracting, moving, organizing, and accessing huge amounts of data. Obviously, data is necessary for Decision Support. However, while selecting a physical approach is necessary, it is not enough to build a successful Data Warehouse.

How do you define success in a warehouse implementation? Success is reached when an organization’s goals (such as increased profitability, improved market share, or expense reduction) are met as a result of the implementation. The resulting environment must produce decisions that move the enterprise towards its goal. By its very nature, the Data Warehouse must provide a way of measuring the effects of those decisions. After all, it is the decision that is the output—not the data.

By focusing on decision making to achieve goals, you address a host of critical issues that might be missed when you focus solely on the data. These issues include:

bullet.gif  What is the goal of the warehouse?
A tangible, measurable benefit must be derived from the project. The goal must be stated in business terms (e.g. increase profitability by $10 million in the first year). The goal should not be to deliver all OLTP data to the masses. This may in fact happen, but it must be done in support of specific objectives.


bullet.gif  How do I measure the effect of a decision on achieving the goal?
If you cannot do this, you won’t begin to see the impact of successful decision making, much less prove the success of your implementation.


bullet.gif  What internal or external data is available to support these measures?
What frequency, history, and quality of data is required? What system availability is necessary? If you focus on the data you need to make decisions rather than focus on the data itself, this actually narrows the scope of a project. Rather than assuming that all data is necessary, you only extract the data and the grain that is important for decision making. Furthermore, you can avoid costly mistakes by uncovering problems that would otherwise be discovered at the end of a project—like necessary information that is not being collected by operational systems or a complicated schema that confuses decision makers.

bullet.gif  How can the data be stored so that performance is not an issue?
A data-centric approach is concerned with raw performance, not decisions. Without considering the decision-making process, you cannot design a physical database that really produces answers.

bullet.gif  What tools or automated decision-making systems are appropriate?
Your tool selection should be based not just on its feature set, but on how well the tool operates on your data in your decision-making environment. The tool needs to fit the type of analysis necessary to accomplish your goals. To evaluate that, you need to understand your decision-making processes. By defining these processes, you may even find ways of automating the way you make decisions.

bullet.gif  Will the decision makers know how to use the data to answer questions?
How will they judge the quality of the data they are using? What can they do if they notice obviously bad data? If your model is to deliver “as is” data to your decision makers and hope they know what to do with it, you will have an expensive data bank of questionable quality accessed only by people with technical skills.

bullet.gif  How does one secure the data without limiting access to data needed for a complete answer?
Decision Support data can be as sensitive as OLTP data. An effective strategy for protecting data that works with your chosen tool can be a real challenge, but a necessary component.

The data-centric approach is often chosen as the fastest means of getting something to the “users.” In fact, focusing on delivering a decision-driven Data Warehouse is a faster, more cost-effective approach to real success. Why? Because as much as you might like to avoid asking the hard questions, you will waste valuable time and money if you do not. Furthermore, with the right methodology, it is not as hard as you think.
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