With the advance of technology and the consequences resulting from this evolution, competitiveness in the business world has increased. Therefore, the need for a smarter method to support business decision making that works in a scalable way has emerged.
The concept of Business Intelligence is the set of tools and processes based on technology that has as its main objective to support companies in decision making and monitor the results of investments in a more intelligent and strategic way to improve and support the management of different businesses in order to identify opportunities and prevent possible risks.
The basis of this concept goes through a process of transforming a large amount of data into relevant and useful information that has the purpose of managing the business in an efficient way. It involves a set of tools, applications, and methodologies that enable the collection of data from internal systems and external sources, organize them for analysis, develop and execute reports, and create visual reports to present the data in an easy-to-understand way.
One of the components of Business Intelligence is the Data Warehouse and one without the other would not work. The cleaning, extraction, transformation, and storage of data must be performed through the Data Warehouse (central repository), considered a fundamental component of BI since the entire volume of data is stored in the Data Warehouse for later organization and analysis.
In the context of Data Warehouse and Business Intelligence comes the process of ETL (extract, transform, load) that passes through the following three important steps for the success and transition of data from the source systems to the Data Warehouse:
This step can be understood as the phase in which the data is extracted from the source systems and transported to a secondary area where it is converted into a single format. Its conversion is fundamental considering the heterogeneity of the information extracted from the source systems. Therefore, it is fundamental to do a previous structuring for later, the adequate treatment to be done.
Once the data has been collected, it is transformed and 'cleaned'. In this phase the data is corrected, standardized, and treated according to the needs of each business.
Data mapping is a part of the transformation process that provides detailed instructions to an application on how to obtain the data needed to be processed.
The loading step follows the transformation. Once all the necessary data processing is done, the loading of the Data Warehouse begins - it contains four key properties to support decision-making that define the DW as a data set:
Organized by subject, providing a simpler view on a given subject and allowing a better analysis;
Integrated, in order to solve the conflicts and inconsistencies of the various data formats;
Time-structured, allowing for the detection of long-term patterns and relationships;
Non-volatile, in the sense that records may undergo changes or updates after being entered.
This step is a normal challenge for businesses because it allows the various intervenients to monitor the evolution of results according to the defined goals and objectives.
A solution in this sense is the creation of dashboards - panels that present metrics and visual indicators for achieving goals and objectives that facilitate the understanding of the information obtained and allow the monitoring of results on a daily, weekly or real-time basis.
Large amounts of data have a lot of potential but they don't mean much if you can't operationalize their use because it is necessary to use complete and dynamic integration systems that are able to generate, group, cross-reference and compare raw data from various sources, transforming them into relevant information for businesses in order to facilitate management, information search and decision-making.
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