Business Intelligence(BI) is a set of methods,Business Intelligence: A Guide on Everything You Need to know Articles architectures, and technologies that transform raw data into important information that drives valuable business actions. It is a suite of software and servicesto convert data into actionable intelligence and knowledge.
In this tutorial, you will Learn-

What is Business Intelligence?
Why is Business Intelligence Important?
Process and Activity of Business Intelligence.
Four types of Business Intelligence Users.
Business Intelligence system Advantages
Business Intelligence system Disadvantages.
Trends in Business Intelligence.

What is Business Intelligence?

Business intelligence (BI) links business analytics, data mining, data visualization, data tools, and infrastructure. And best practices to help businesses to make more data-driven judgments. In practice, you know you’ve got modern business intelligence when you have a complete view of your business’s data and use that data to drive change, eliminate incompetence, and instantly adapt to market or supply changes.
Why is Business Intelligence important?

Business intelligence can help organizations make better judgments by showing present and historical data within their industry context. Analysts can leverage BI to provide performance and competitor benchmarks to make the company run smoother and more efficiently. Analysts can also more easily spot market trends to improve sales or revenue. Used efficiently, the right data can help with anything from compliance to hiring efforts

Some ways that business intelligence can help businesses make more intelligent, data-driven decisions:

Recognize ways to improve profit

Analyze customer behavior

Compare data with competitors

Track performance

Optimize operations

Predict success

Spot market trends

Identify issues or problems

Process and Activity of Business Intelligence.

Over the past few years, business intelligence has grown to include more processes and activities to help increase performance. These processes include:

Data Mining: Using databases, statistics, and machine learning to open trends in large datasets.

Reporting: Sharing data analysis to stakeholders so they can draw results and make judgments.

Performance metrics and benchmarking: Comparing current performance data to historical data to follow the performance on goals, typically using customized dashboards.

Detailed analytics: Using preliminary data analysis to find out what happened.

Querying: Asking the data specific issues, BI extracting the results from the datasets.

Mathematical analysis: Taking the results from detailed analytics and further examining the data using statistics such as how this trend happened and how?.

Data visualization: Turning data analysis into visual representations such as charts, graphs, and histograms to more efficiently use data.