While data flow diagrams and entity relationship diagrams showcase how data moves in a given system and the types of interactions involved in the process, there are bigger and more complicated components involved with the use of data. In a company, only so much data can be stored therefore there has to be something that helps store all the data without it interfering with the actual useful information. Also when the specific data is selected, there has to be something that uses the data to put it together to help make business decisions.
These two components are known as data warehouse and business intelligence respectively, a data warehouse is basically storage of a company’s electronically stored data. Often in a company, a regular database does not have the room to store all the company’s data there is information that’s not required in the present time however it might be of use at a later time Information such as customers who made a purchase recently might not be useful now but it will have more use in the long run to gain valuable information for research.
This information will be kept in a data warehouse where the information can be used for research data warehouses are often used and designed to make possible the occurrence of reporting and analysis. Essential components include retrieving, analyzing, extracting, transforming, loading and managing data. A more elaborate definition for data warehousing includes business intelligence tools to extract and load information into the repository while managing metadata.
Companies have to invest in an effective data warehouse and supplemental business intelligence program to be successful. The use of data warehousing comes with many advantages. For starters, data warehousing is sought for by an organization’s need for reliable, consolidated and integrated reporting and analysis of its data at various levels of aggregation.
It is used to realize the value of data because the information is an asset and it’s essential to realize how much it’s worth through reporting and analyzing also, data warehousing is used to make better decisions. It can be used to turn data into information to create a competitive advantage for the company. The convenience of querying data not stored in a database management system or operating when sources are not available makes the warehouse useful as well. The problems that can arise from data warehouses come from the fact that these warehouses can be so big that data becomes difficult to manage if there is a possibility of missing or unrecorded fields. Also if there is incorrect information or duplicate records, the warehouse fall victim to redundancy. Much time is needed to extract, clean, and load data, and eventually, problems will be found in systems feeding the data warehouse a large system like this usually means high maintenance.
A data warehouse structures its functions in three layers with them being staging, integration, and access the purpose of staging is used to store raw data so that it can be used by users at a later time. This staging area is like a foundation area where transaction data can be transformed for use in the data warehouse. The integration layer combines layers and create abstraction from users the access layer distributes the data outward. When information is analyzed and transformed, managers can use it for other applications such as data mining and online analytical processing. To put it simply, data mining is the process of extracting hidden patterns of data from different perspectives and summarizing it into useful information. Online analytical processing or OLAP is an approach to quickly reveal business trends and statistics not directly obtainable through the data warehouse alone.
These applications tie the data warehouse to the business intelligence aspect of business decision-making. Business intelligence is a term that is commonly connected with data warehousing to the point where these two terms are used interchangeably. This association happens because both of them deal with strategic planning Business intelligence refers to the usage of techniques used by organizations to gather information from data warehouses and ultimately make tactical decisions regarding the company. Although it doesn’t have to be solely dependent, the data warehouse is an essential aspect to achieving BI. When it comes to information processing, there are two kinds; transactional processing where attention is focused on the modification and addition of data and analytical processing where focus is on reporting and analyzing data. Business intelligence falls under the latter and works with data more on a knowledge-based rather than just the numbers.
Business intelligence is needed to prevent information overloading which is when there is too much information that we can’t find what we need BI helps lessen the gap between the irrelevant data and the knowledge or information that leads to a decision. The flow of a business intelligence systems starts from a data source this could be the data warehouse or data mart discussed before Bl tools such as OLAP or data mining are then used to complete the BI application process. During the delivery of info, data flows into a server like a web site or portal and finally made available to users through their device of use like a computer or phone. The idea behind data warehouse and business intelligence could be simply explained through data warehouses are in charge for data preparation and business intelligence is responsible for data usage. One is used to store a lot of data regarding the company while the other uses that data through different processes to facilitate business decisions. Even though they have different applications, they are closely linked to accomplishing a common goal of the organization.