What You Should Know About Building a Data Warehouse
As we move further into the Information Age, the global competition among companies has become more fierce, and many of them are relying more on data warehouses to help them make critical decisions. Before a company can use a data warehouse to achieve their own goals, they must first understand how it is built.
Some of the greatest challenges involving a data warehouse will be seen when it is implemented by the company for the first time. The quality of the data within the warehouse is very important. It must be accurate if the company wants to make good decisions based on it. Some of the things that the company will want to look at is the source of the data, and whether or not it comes from a program that is operational.
If the data does come from an operational program, a company will need to analyze the rules of this data before they allow it to be placed within a program that is operational. It should also be noted that it is critically important for companies to understand the rewards that surround placing data in the warehouse properly. When data is pulled from the Internet, this is of great importance. While some areas of the Internet like e-commerce may be highly accurate, other parts of the Internet may be highly inaccurate. Because of this, one important aspect of running a successful data warehouse is making sure the data has been cleaned.
Once the data has been cleaned, it can be analyzed properly. The maintainence of data plays an important role in the construction of the data warehouse. Dealing with personal names can be very challenging because many people may go by numerous names. Some of these may be legal names or nicknames. The same problem may also occur when dealing with addresses. If a person lives in an apartment, they can enter the information in a number of different ways, and this can cause inconsistencies in the system. To solve these problems, it is important for companies to construct programs that are capable of making correlatoins between data that is similar.
Every company that decides to use a data warehouse must figure out how they will store the data within it. A company must understand that there is a big difference between moving the data into the warehouse versus changing the operational systems in a way that makes them more friendly to the data warehouse. The recent advent of software that can automate the data cleaning process and combine this process with the transport of data via an operational system is useful, and can play an important role in the business decisions that are made by the company. In addition to placing data within the data warehouse, it is also important to make sure that data is defined.
Many of the users may not have a technical knowledge of the warehouse, and it is important for companies to make sure a definition of the data is made. Analysts will want to look at the potential queries that will commonly be made by the user, and make preparations for them. The number of possible queries for a data warheouse are quite numerous, and this is why it is so crucial for the analysts and developers to make sure the data is defined.
Queries which are highly intelligent will make the information that is found that much more valuable. The warehouse schema should be set up in such a way that it allows the largest number of questions to be asked and answered.
This schema should only be limited by the database management system itself. Another issue that companies will want to consider is how updated the data warehouse should be. The maintenance of the warehouse should be done based on the volatility of the data within it. For instance, an address that was entered for a customer 18 months ago may not be current, and the customer may no longer live at that address. The ultimate goal of a company should be to design a schema that is close to real time. A company can’t afford to make decisions on data that is old or obsolete. When the data warehouse is being updated in near real-time, this will allow the company to make decisions which are highly accurate.