To remain competitive, businesses must have a clear view of all their data, which is where business intelligence (BI) tools come in. After all, nearly half of all businesses already use BI tools, and projections show that this trend will continue in the coming years.

However, for those who haven’t yet adopted a tool or are simply interested in learning more, it can be difficult to define BI. We created this comprehensive guide to educate people on what business intelligence is, how it works and other topics.

What exactly is business intelligence?

To assist organizations in making more data-driven decisions, business intelligence combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices. In practice, modern business intelligence is demonstrated when you have a comprehensive view of your organization’s data and use that data to drive change, eliminate inefficiencies, and quickly adapt to market or supply changes. Modern business intelligence solutions prioritize self-service flexibility, governed data on trusted platforms, empowered business users, and speed to insight.

It’s worth noting that this is a very modern definition of BI—and BI has a tumultuous history as a buzzword. Traditional Business Intelligence, complete with capital letters, first appeared in the 1960s as a system for sharing information across organizations. In 1989, the term “Business Intelligence” was coined, along with computer models for decision making. These programmes progressed, transforming data into insights before becoming a specialised offering from BI teams with IT-reliant service solutions. This article is only the tip of the iceberg when it comes to BI.

How does business intelligence function?

Businesses and organisations have concerns and objectives. To answer these questions and track performance against these objectives, they collect the necessary data, analyze it, and decide which actions to take to achieve their objectives.

On the technical side, raw data from business systems is collected. Data is processed before being saved in data warehouses, the cloud, applications, and files. Users can then access the data and begin the analysis process to answer business questions.

BI platforms also provide data visualization tools, which convert data into charts or graphs and present them to key stakeholders or decision-makers.

BI techniques

Business intelligence is an umbrella term that encompasses the processes and methods of collecting, storing, and analyzing data from business operations or activities in order to optimize performance. All of these factors combine to form a comprehensive view of a business, allowing people to make more informed, actionable decisions. In recent years, business intelligence has expanded to include more processes and activities to aid in performance improvement. Among these procedures are:

Data mining is the process of discovering trends in large datasets by using databases, statistics, and machine learning (ML).

Reporting: The dissemination of data analysis to stakeholders in order for them to draw conclusions and make decisions.

Performance metrics and benchmarking: Using customized dashboards to compare current performance data to historical data to track performance against goals.

Descriptive analytics: Investigating what happened using preliminary data analysis.

Querying: By asking data-specific questions, BI extracts answers from data sets.

Statistical analysis: Taking descriptive analytics results and further exploring the data with statistics to determine how and why this trend occurred.

Data visualization is the process of transforming data analysis into visual representations such as charts, graphs, and histograms in order to make data easier to consume.

Exploration of data through visual storytelling to communicate insights on the fly and remain in the flow of analysis

Data preparation is the process of compiling multiple data sources, identifying dimensions and measurements, and preparing the data for analysis.

How BI, data analytics, and business analytics interact with one another

Business intelligence incorporates data analytics and business analytics, but only as components of the overall process. BI assists users in drawing conclusions from data analysis. Data scientists delve into the details of data, employing advanced statistics and predictive analytics to identify patterns and forecast future patterns.

“Why did this happen, and what can happen next?” data analytics asks. Business intelligence translates the results of those models and algorithms into actionable language. “Business analytics” includes “data mining, predictive analytics, applied analytics, and statistics,” according to Gartner’s IT glossary. In short, businesses use business analytics as part of a larger business intelligence strategy.

BI is intended to respond to specific queries and provide quick analysis for decisions or planning. Companies, on the other hand, can use analytics processes to continuously improve follow-up questions and iteration. Because answering one question will almost always lead to more questions and iteration, business analytics should not be a linear process. Consider the process as a continuous cycle of data access, discovery, exploration, and information sharing. This is referred to as the analytics cycle, a modern term that describes how businesses use analytics to respond to changing questions and expectations.