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Use With Your Data Warehouse - Rules
Article Posted On Date : Monday, February 8, 2010
Use With Your Data Warehouse - Rules
Once a company has successfully implemented their data warehouse, it is important for them to establish rules and regulations with which to use it. While different companies will have different rules when it comes to handling their data warehouses, their are some general principles that you will want to pay attention to. These principles will not only make using your data warehouse easier, but it will also allow the organization to use it much more efficiently. The very first rule of thumb is to realize thatdata warehouses are challenging to use. Many experts say that at least 30% of the info they give out may not be consistent. One of the most problematic things about this is the company may not notice the error if they are dealing with an operational unit that is transaction based. Despite this, this error percentage should not be allowed in the data warehouse. When you consider the fact that many large scaledata warehouses can cost millions of dollars to purchase and implement, and error percentage of 30% is not acceptable. To solve this problem, it is important for companies to analyze their data carefully before making decisions that are based on it. It is unwise to just accept data as it is, without carefully looking for errors or other problems. The second rule of data warehouses is to understand the data that is stored. It has been said that knowledge is power, but this is only a half truth. Knowledge that is stored and unused is potential power. Companies will want to perform an analysis each day of the databases that are connected to the data warehouse. To understand the data, the analysts must be able to find relationships among numerous systems. Once these relationships are found, they must be maintained when the data is moved within the data warehouse. The implementation of a data warehouse will often require the user to make some modifications to the schema of the database. If the user does not understand the various relationships among the systems, they may be prone to generating errors that could compromise the accuracy and efficiency of the system. Anotherimportant rule of thumb is to learn how to find entities that are equivalent or equal to each other. One of the most common problems that can occur in a data warehouse is when the same pieces of data appear in various parts of the system with different names. For instance, two departments within the organization may be helping one customer, but the name of the department may be placed in the system twice under different names. One name could be spelled out, and the other name could be an abbreviation. This can create serious problems in the system if it is not corrected, and the best way to solve this problem is to use a data transformation tool. Because many large companies and organizations are comprised of many different departments, serious problems can arise when each them decides to store information in a different way. One of the situations where this occurs frequently is during mergers. To avoid this problem,companies will want to establish a standard database structure. This will make mergers much easier when they occur. Perhaps one of the most important principles of data warehousing is to use metadata in a way that supports the quality of the data within the data warehouse. Metadata can be defined as "the data about data." It is the data which describes the data within the data warehouse. One of the biggest challenge that companies will face is trying to harmonize the metadata across multiple vendor tools. To deal with this issue, companies will want to make sure they generate the metadata and use it for interfaces or other products. Look for vendors who are able to integrate metadata from numerous sources that are disparate. It is also important for companies to make sure they choose the right data transformation products. A data transformation product is a device that extracts, cleans, and loads data into the data warehouse. It will also record a history of this process. Thedata transformation product is crucially important, and companies must choose the product carefully.