Why Data Warehouses Can Be Useful
A data warehouse is a tool that is constructed to give a specific view of data that an organization or company can gather during the course of carrying out various processes. Data warehouses are useful because they can allow a company to give managers and executives crucial information that will allow them to make better decisions.
In a day and age when the decision of one executive can make or break a company, this is crucially important. Every successful business gathers and records information that is related to their customers and various transactions. Many of these businesses will use an OLTP, or online transaction processing tool.
In the past, it was very difficult for managers or executives to get information about their company as a whole. This is still challenging today for companies that don’t use data warehouses. When a company uses a number of different systems, the information they retrieve can be inconsistent. Data warehouses are useful because they collect data and remodel it. The information is placed in a single unit, and the company can get a clear picture of how their company is performing. Most importantly, they will be able to make decisions with a great deal of confidence. Data will be stored in the warehouse from multiple sources. Once the data is stored, it must be cleaned and transformed.
The process of cleaning and transforming data is known as ETL, or Extraction, Transformation, and Loading. Properly caring for the data is an important part of maintaining a successful data warehouse. Most companies store data for the long term, and they follow set rules and procedures. The data warehouse is specifically designed to give managers information about the company as a single entity. Data will be placed in the warehouse periodically, and it will be done in batches. In most cases, the data will be stored at times when the company isn’t extremely busy. The data is considered to be non-volatile. One of the most powerful benefits of a data warehouse is the fact that operational forms of data can be optimized for a certain level of efficiency.
One concept that you will want to become familiar with is metadata. Metadata can be defined as the information on the data that is stored in the warehouse. In other words, its data about data. Metadata can be broken down into three categories, and this is operational, business, and administrative. The administrative is related to the columns and tables of the warehouse, and it also deals with the rules by which the data is maintained. As the second name implies, business metadata deals with various business terms. This data is especially important to those who will be making the key decisions. The operational metadata deals with the errors, history, and usage. As the name suggests, it deals with the operational issues surrounding the data.
Because many managers in the company will have different needs for the data, many of them will construct smaller data warehouses that are tailored towards certain subjects. These small data warehouses are referred to as being data marts. The data mart will get its information from the central data warehouse that is being used by the company. The last part of the data warehouse is the decision support program. These programs will get their information from the data warehouse, as well as the data marts. They will take the information they are given, and they will use it for querying purposes.
The decision support programs will fall under one of three categories, and these are data mining, SQL, or OLAP. These applications are designed in a way that will allow managers and executives to get important answers to their questions. These answers can assist them in the decision making process. The data can be presented in such a way, that it allows the decision makers to look at summarized data before looking for information that is much more specific. Data mining is quite powerful because it allows an AI or neural network to sift through the dat looking for important trends or relationships, connections that are impossible for humans to find within a short time period. Data mining will typically use logistic regression or specific algorithms.