Business Intelligence - Introduction
Business Intelligence is a collection of procedures, mechanisms, and technologies that modify raw data into significant information that drives cost-effective business services. It is a collection of software and services to modify data into actionable intelligence and recognition.
BI has a huge impact on an organizations methods and tactical and operational business decisions. BI supports fact-based decision-making using historical data rather than assumptions and gut feelings.
BI tools implement data analysis and make documents, summaries, dashboards, maps, graphs, and charts to support users with detailed intelligence about the features of the business.
Business Intelligence is one of the most dynamic tools many organizations use to know their user base and industry better. It defines the business methodology in which the raw information is transformed into useful data which support decision-making.
Business intelligence has wide software, and if talking about the advantage of business intelligence in the retail sector, current business intelligence tools allow organizations to take benefit of information not only to consider current sales but also to estimate future potential, patterns, trends, and understand the demand of the user on a deeper level.
BI helps make data easily accessible and understandable for decision-makers so they can make informed choices. This ongoing process is designed to be efficient and scalable, ensuring businesses have the insights they need to improve performance and achieve their goals.
BI was created to help businesses overcome the issue of "garbage in, garbage out," which occurs when data analysis is based on inaccurate or incomplete information.
Business Intelligence Objective
The main objective of business intelligence is to make Business work effectively by ensuring that information is easy to access, understand, and secure. Start by using intuitive tools that present accurate and up-to-date data clearly, so users can make better decisions. Data should come from reliable sources and be accessible only to those who need it.
Traditional BI Vs Modern BI
Heres a comparison to highlight their key differences −
| Traditional BI | Modern BI |
|---|---|
| Traditional BI often depended on IT teams to access data, which made it harder for business users to get the results they needed. | Modern BI makes it easy for business users by giving them access to data and the tools they need, especially AI tools, to quickly achieve the desired results. |
| Traditional BI, business users often had to wait for reports, which could mean the information was outdated by the time they received it. | With modern BI, business users can access accurate, up-to-date information whenever they need it. |
| Traditional BI is time-consuming and involves delays. | Modern BI allows quick access to data. |
| Traditional BI often results in inconsistent data usage, which can create confusion and errors. | Modern BI ensures consistent data use, so everyone has access to the same reliable information. |
| Traditional BI platforms mostly focused on giving users detailed historical reports and user-friendly ad-hoc analysis tools. | Modern BI focuses on real-time data analysis and advanced tools for in-depth insights and predictive analytics. |
| Traditional BI often required access from specific locations or devices, making it harder to get data on the go. | Modern BI makes it easy for your team to access data and insights from anywhere, on any device. |
Methods of Business Intelligence
The methods of business intelligence are as follows −
1. Data analysis visualization
Data analysis visualization is all about how it visualizes the data. It presents records on dashboards and uses customized metrics associated with the business to create better decisions based on facts.
2. Reporting
Business intelligence tools are used for reporting information gathering from all the sources and processing it to enable better reporting and financial decision-making with a rational mind.
3. Predictive Analytics
Predictive analytics is how you learn an action will work. The fact is you don't learn, and if you learn not 100 percent. However, with business intelligence, it can make an evidence-based decision to drive business further. Business intelligence allows us to create a reasonable prediction of the current trends and user behaviors that impact the organization's complete development.
4. Data Mining
Data mining is a computer-supported technique to reveal previously anonymous or unnoticed relations between data entities. Data mining is the procedure of discovering useful new correlations, designs, and trends by sharing a high amount of data saved in the warehouse, using pattern recognition technologies such as statistical and numerical approaches.
Types of BI Tools and Software
BI tools are software programs that help in gathering, processing, and analyzing a large amount of data from different sources and these software turns these data into valuable information, making it easier for businesses to understand and use the data for decision-making.
Below are different BI software and solutions −
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Spreadsheets − In this software, the user inputs, stores, edits, organizes, computes, and visualizes data.
Examples − MS Excel, Google Sheets, etc.
- OLAP − OLAP stands for Online Analytical Processing. OLAP solutions allow users to view and analyze data from different perspectives by storing it in a multidimensional format.
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Data visualization − It helps us to represent the data in the form of charts, graphs, plots, maps etc.
Examples − Tableau Desktop, Power BI Desktop, etc.
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Data mining − In business, we handle large amounts of data. To search and analyze this data to find valuable insights, we use data mining.
Example − Knime, RapidMiner, etc.
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Database − A database stores a large amount of data. There are various types of databases such as relational, NoSQL, and distributed databases.
Examples
- Relational Database − Oracle DB, SQL Server, PostgreSQL.
- NoSQL Databases − MongoDB, Cassandra.
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ETL Tool − ETL stands for Extract, transform, and load. It is a process that is used for data integration. It involves extracting data, transforming it, and loading it into a destination.
Examples − Informatica, Ab Initio, IBM DataStage, etc.
- Project management Tools − JIRA software, MS Project, MS Excel.
- Data Modeling Tools − Oracle Data Modeler, Toad Data Modeler
- Reporting and Analytics − MicroStrategy, SAP BusinessObjects Business Intelligence.