Most companies collect a massive volume of business data every day – flowing in from their enterprise resource planning (ERP) software, e-commerce platform, supply chain, and many other internal and external sources. To actually take advantage of this data, and use it to make data-driven decisions, they need a modern business intelligence (BI) system.

Business intelligence definition

Business intelligence refers to the processes and tools used to analyze business data, turn it into actionable insights, and help everyone in an organization make better-informed decisions. Also known as a decision support system (DSS), a BI system analyzes current and historical data and presents findings in easy-to-digest reports, dashboards, graphs, charts, and maps that can be shared across the company.

BI is sometimes called “descriptive analytics” because it describes how a business is performing today and how it performed in the past. It answers questions like “What happened?” and “What needs to change?” – but it does not get into why something happened or what might happen next.

Business intelligence software showing income statement
BI software comparing income statements across several years.

Business intelligence vs. business analytics

Business intelligence and business analytics are two terms that are often used interchangeably. Is there a difference? There is currently no consensus of opinion. That said, a common distinction is that business intelligence focuses on what happened in the past and what is happening now (descriptive analytics). Whereas business analytics focuses on:

But at the end of the day, both BI and business analytics are vital – working together to provide companies with all four types of analytics (descriptive, diagnostic, predictive, and prescriptive) and the big picture insights decision-makers need.

“What’s the difference between business analytics and business intelligence? The correct answer is: everybody has an opinion, but nobody knows, and you shouldn’t care.”

Timo Elliot, Innovation Evangelist, SAP

Regardless of the label applied, what is important is that organizations have the tools and technology they need to get answers to their business questions, solve the problem at hand, or reach a specific goal. This is why several major software vendors have started to combine BI and business analytics on a single cloud platform, providing organizations with all the analytics capabilities they need in one place – and rendering the whole taxonomy debate moot.

Key benefits of business intelligence

A successful BI program shines light on ways to increase profits and performance, discover issues, optimize operations, and much more. Here are just a few of the many benefits of BI:

Business intelligence tools and systems

There are many different tools used in a business intelligence system. Here are some of the most common:

BI reporting

BI reporting – presenting data and insights to end users in a way that is easy to understand and act on – is fundamental to every business. Reports use summaries and visual elements like charts and graphs to show users’ trends over time, relationships between variables, and much more. They are also interactive, so users can slice and dice tables or drill deeper into data as needed. Reports can be automated and sent out on a regular, predetermined schedule – or ad hoc and generated on the fly.

Querying

Querying tools allow users to ask business questions and get answers through intuitive interfaces. With modern querying tools, submitting a query can be as simple as asking Google (or even Siri) a question – like “Where are shipping delays happening?”, “Did quarterly sales meet their targets?”, or “How many widgets were sold yesterday?”

BI dashboards

Dashboards are one of the most popular BI tools. They use continually updated charts, graphs, tables, and other types of data visualization to track pre-defined KPIs and other business metrics – and provide an at-a-glance overview of performance in near-real time. Managers and employees can use interactive features to customize which information they want to view, drill into data for further analysis, and share results with other stakeholders.

BI software showing financial performance
BI dashboard showing the financial performance across countries and business units.

Data visualization

The ability to visualize data and see it in context is one area where BI really shines. Charts, graphs, maps, and other visual formats bring data to life in a way that can be quickly and easily understood. Trends and outliers are more apparent. Colors and patterns paint a picture of the story behind data in a way that columns and rows in a spreadsheet never could. Data visualization is used throughout a BI system – in reports, as answers to queries, and in dashboards.

OLAP

Online analytical processing (OLAP) is a technology that powers the data discovery capabilities in many business intelligence systems. OLAP allows for fast, multidimensional analysis across huge volumes of information stored in a data warehouse or other central data store.   

Data preparation

Data preparation involves compiling multiple data sources and generally preparing it for data analysis. Using a process called extract, transform, and load (ETL), raw data is cleansed, categorized, and then loaded into a data warehouse. Good BI systems automate many of these processes and allow for setting dimensions and measures.

Data warehouse

A data warehouse holds aggregated data from multiple sources that’s been cleansed and formatted so that it can be accessed by BI and other analytics tools.

Examples of business intelligence in action

Today’s BI tools make it easier for everyone across an organization to access, analyze, and act on current and historical data. Here are a few examples of BI use cases in different business areas:

Traditional vs. modern BI

Business intelligence has been around for over 30 years and traditionally, it was driven by IT. Questions were submitted to the IT team and answers were provided back to the business in the form of a static report. If there were follow-up questions, they were re-submitted to IT and usually placed in the back of the queue. This time-consuming process has been replaced by modern BI – which is far more interactive.

Modern, self-service BI tools let business users query data themselves, set up dashboards, generate reports, and share their findings from any Web browser or mobile device – all with minimal IT involvement. Recently, artificial intelligence (AI) and machine learning technologies have made this process even simpler – and faster – by automating many BI processes, including data discovery and the creation of reports and visualizations.

Increasingly, companies are choosing cloud-based BI tools that connect to more data sources and are available 24×7 from anywhere. And they are choosing solutions that offer embedded BI – BI that is embedded directly into workflows and processes so users can make better decisions in the moment and in context.

The most modern BI platforms today combine business intelligence, advanced and predictive analytics, and planning tools in a single analytics cloud solution. They are augmented by AI and machine learning technologies, they can be embedded in any process, and they democratize BI and analytics by making them easy to use for everyone – not just IT departments or professional analysts.

Modern BI and analytics

Bring the power of BI to every team member with SAP Analytics Cloud – AI-driven BI, augmented analytics, and planning in one solution.

Business intelligence FAQs

Business intelligence is focused on analyzing past and current data to paint a picture of the current state of the business. Data science takes a cross-discipline approach to analyzing the same data, using statistical algorithms and models to uncover hidden and predictive insights from structured and unstructured data.

Business intelligence is descriptive, giving insights into what’s happening now and what happened in the past. Business analytics is an umbrella term for data analysis techniques that can also predict what will happen and show what’s needed to create better outcomes.

Business intelligence tools work together to turn data into actionable insights. Many of these operate “under the hood” to prepare, mine, store, and process data so that it can be accessed by BI systems. Others are focused on helping business users interact with data and interpret results through interactive dashboards and data visualizations.

A BI analyst, as the title suggests, gathers and analyzes data and then identifies areas where businesses can improve. They generally keep tools and databases up to date, develop BI strategies, and communicate findings to stakeholders.

A BI developer is responsible for creating, deploying, and managing business intelligence reporting tools and interfaces designed to solve specific problems within a company. A typical BI developer is versed in software engineering, databases, and data analysis. Responsibilities include translating business requirements into technical ones, helping with data model design, creating technical documentation, and more.

Although modern business intelligence tools offer an out-of-the-box self-service experience to allow business analysts and power users with technical backgrounds to uncover the insights needed to address challenges, BI developers are still needed to govern and scale the delivery of trusted corporate reports and dashboards to everyday business users – information workers and decision-makers – without such technical backgrounds.

BI reporting is the part of business intelligence focused on presenting analyzed data in the form of dashboards, reports, and data visualizations which can be summarized and easily shared around the organization.

Data visualization is the representation of data through graphs, maps, dashboards, charts, and other visual formats. It helps business users visualize trends, outliers, and patterns at a glance. Visual analysis is central to business intelligence reporting.

A decision support system refers to any interactive computer-based system that can gather and analyze information from large datasets, including raw data, documents, and knowledge bases. As the name suggests, DSS systems support planners and managers in making informed decisions based on insights surfaced through the analysis process.