Business Intelligence (BI) Concepts and Components

In increasingly complex data systems, ensuring that data is accurate, consistent, and timely can be a daunting task. Fortunately, by understanding the four elements of a business intelligence (BI) environment, companies can develop a BI strategy that effectively meets their needs.

But why utilize business intelligence in the first place?

In a nutshell, business intelligence allows organizations to make data-driven decisions. This process begins with data gathering, followed by data cleaning and standardization, analysis, and reporting.

All four of these steps are essential to creating an effective BI strategy. By learning more about each element, companies can create a BI plan that improves their bottom line.

In this article, we will discuss the four elements of a business intelligence environment: data, people, processes, and technology, and how they impact business intelligence strategy.

What is business intelligence?

Business intelligence is a set of strategies and technologies that gather data, interpret it, and transform it into actionable insights. BI uses a variety of tools, such as data mining, charts and visualizations, business analytics, and performance benchmarking, to help executives make better business decisions.

What are the four main components of BI?

Because BI is a series of strategies, BI implementation is always ongoing. It touches many areas of a business. In order to understand the BI cyclical process, you need to understand the four main components: data gathering, data standardization, data analysis, and reporting.

1. Data gathering

The first step in business intelligence is to gather data. This includes collecting data from all your sources as well as collecting historical data to inform strategic decisions. This first step of gathering data will give you a baseline to which you can compare future metrics. 

2. Data Standardization

Data standardization is often the most challenging aspect of Business Intelligence (BI). It begins with data cleaning—filtering out inaccuracies, incomplete records, duplicates, and irrelevant information. Once the data’s quality is verified, the next step is storage. This involves implementing data automation, setting permissions, establishing security protocols, and other technical measures to ensure seamless storage. Additionally, the data must be properly categorized and organized within the storage system for easy and quick access. Standardization also requires converting data into consistent, compatible formats, ensuring it remains readable and can be processed uniformly across systems.

Data Warehousing and Storage

While standardization ensures consistency and quality, data warehousing provides the backbone for efficient storage. A data warehouse serves as a centralized hub where standardized data is securely stored, enabling users to run queries and generate reports without disrupting operational systems.

In some cases, organizations complement data warehouses with data marts—smaller, specialized repositories tailored for specific departments or use cases. Whether leveraging a large-scale warehouse or a focused data mart, these storage solutions ensure that standardized data is secure, well-organized, and readily accessible for analysis, visualization, and dashboarding.

3. Data analysis

Now that you have your data gathered and standardized, you can begin the analysis component. This is the stage where you start getting insights and building strategy. Identify trends, compare data sets, observe correlations, visualize data into charts and graphs, and make predictions based on real-time updates. The more you analyze the data, the more actionable insights you’ll find to help improve your business processes. 

4. Reporting

A data report is a business intelligence (BI) tool that consolidates specific data sets into a single snapshot. Think of it as a photograph capturing a moment in time—like someone mid-jump off a diving board. The data is static and unchanging after the report is created or exported. However, it still provides valuable insights: you can deduce past events (the person jumped off the diving board) and make predictions about the future (they’ll likely hit the water soon, and you can even estimate when).

Dashboards and Advanced Analytics

Modern BI platforms elevate reporting to the next level with interactive dashboards that deliver real-time insights into performance metrics, key performance indicators (KPIs), and historical trends. These dashboards are customizable for various teams—executives can track strategic objectives, while marketers monitor campaign results with precision.

Beyond dashboards, BI systems offer advanced analytics tools such as data mining, OLAP (Online Analytical Processing), and machine learning models. These capabilities empower businesses with deeper insights, enabling tasks like customer segmentation, trend forecasting, and predictive modeling. Equipped with the right tools, companies can transition from simply analyzing past data to crafting forward-looking strategies with confidence.

Data Integration and Transformation

Data integration serves as the backbone of any effective business intelligence (BI) system. It involves gathering data from various internal and external sources and combining it into a unified, consistent format for analysis. This process streamlines the flow of information, breaking down silos and creating a single source of truth for decision-making.

A key component of data integration is the use of ETL (Extract, Transform, Load) tools and processes, which automate the collection, transformation, and consolidation of data. These tools pull raw information from a wide range of systems—such as CRM platforms, ERP software, marketing tools, and even spreadsheets—before transforming it into a standardized format and loading it into a centralized data warehouse or BI platform.

The transformation stage is especially critical, as raw data is cleaned, standardized, and validated—a process commonly referred to as data cleansing or scrubbing. This step ensures that the data is accurate, reliable, and ready for analysis. Without proper integration and transformation, organizations risk working with siloed, inconsistent, or outdated data, leading to unreliable insights and missed opportunities.

BI systems that prioritize robust data integration enable companies to access trustworthy, consistent information across all departments. By eliminating data silos and ensuring accuracy, they empower leaders to make confident, data-driven decisions that drive growth and innovation.

What Are the Four Key Environmental Factors That Impact a Business Intelligence Strategy?

A successful business intelligence (BI) strategy is not just about having the right tools or data; it relies on the environment that supports it. That environment can be divided into four core factors: data, people, processes, and technology. These elements are split into two categories: internal, which your business can control, and external, which it must respond to. Together, they create the foundation of a strong and scalable BI approach.

Let’s explore how each of these factors influences your BI strategy.

1. Data: The Foundation of Business Intelligence

Without high-quality, reliable data, there is no BI. Data flows from both internal sources such as CRM systems, ERP platforms, and operational databases, and external ones, including market research, social media, and third-party providers.

However, volume alone does not make data useful. The real challenge lies in managing the 3Vs of big data: volume, variety, and velocity. Businesses must ensure their data is accurate, complete, and current. This requires robust systems for collecting, storing, and preparing data so that it is always ready to power smarter decisions.

2. People: The Driving Force Behind BI

BI only delivers value when the right people can act on it. From analysts and engineers who prepare the data to business users who rely on insights to guide decisions, everyone has a part to play.

Fostering a culture of data literacy is key. That means helping teams understand how to interpret dashboards, ask the right questions, and use data tools with confidence. When people across the organization are aligned on how to engage with BI, they’re better equipped to drive impact.

3. Processes: The Framework for Action

BI success hinges on having efficient, repeatable processes in place. From data collection to reporting, each step should be clearly defined, standardized, and documented. This not only ensures consistency but also makes your BI scalable and easier to maintain.

Strong data governance frameworks, automated workflows, and clear roles and responsibilities help reduce errors and speed up insight delivery. The result? Fewer bottlenecks and more confidence in the numbers.

4. Technology: The Engine That Powers It All

The right technology stack turns BI from a concept into a competitive advantage. Modern platforms can process vast datasets, visualize trends in real time, and even surface insights automatically through machine learning.

But powerful doesn’t have to mean complicated. Today’s BI tools should be intuitive enough for non-technical users while still offering deep functionality for data teams. From self-service dashboards to predictive models, the right tools help your organization turn raw data into decisions—fast.

Why These Factors Matter

Together, data, people, processes, and technology form the backbone of your BI strategy. Data gives you the raw material. People turn it into insight. Processes ensure it’s repeatable. And technology brings it all to life.

When these elements work in harmony, BI becomes more than just reporting; it becomes a core part of how your business operates and grows.

How Do These Factors Influence BI in Practice?

Understanding the four environmental factors is one thing—putting them into action is another. Here’s how each one influences the day-to-day work of BI: from gathering and preparing data to analyzing and reporting on it.

Data: Powering Every BI Function

Data is at the core of every BI process. It must be gathered from the right sources, both internal (like sales records and customer data) and external (like market benchmarks or social sentiment). Once collected, it needs to be cleaned, standardized, and stored in formats that make it easy to analyze and trust.

High-quality data enables reliable analysis. Without it, even the best tools can’t deliver useful insights.

People: Turning Data Into Decisions

People are the bridge between data and action. Analysts make sure the data is ready for use, while business users apply insights to their work. When team members are empowered with data literacy and given the right training, they’re able to use BI tools to their full potential.

Ultimately, people don’t just “use” BI—they make it meaningful.

Processes: Creating Consistency and Clarity

Strong BI processes ensure that every step—from collection to visualization—is efficient and reliable. This includes cleaning messy data, aligning formats, applying consistent logic, and building intuitive dashboards.

Well-designed processes reduce friction and make it easier for stakeholders to get the answers they need, when they need them.

Technology: Enabling Exploration at Scale

Modern BI platforms provide the technical foundation for analysis. They support the entire workflow—automating data prep, enabling real-time dashboards, and powering advanced analytics like forecasting or anomaly detection.

Great BI tools don’t just crunch numbers; they unlock exploration. They do this in ways that are scalable, secure, and accessible across the organization.

How can companies ensure that their business intelligence environment is effective and efficient?

Without the proper attention, a business intelligence environment can quickly become ineffective and inefficient. The best way to avoid this is to have a clear understanding of the four environmental factors that impact business intelligence.

For companies to be successful with business intelligence, they must understand how to gather data from all relevant sources, how to clean and standardize the data, how to load it into the BI system, and how to use it to make decisions.

In addition, companies need to ensure that their people are trained and empowered to use data to make decisions. And finally, companies need to have the right technology in place to support their business intelligence efforts.

For example, a company may have the best data gathering and analysis processes in place, but if the business users don’t understand how to use the data, then the efforts will be for naught.

Or a company may have the right technology in place, but if the data isn’t being collected properly, then the technology won’t be able to do its job.

It is only by taking into account all four of these environmental factors that companies can ensure that their business intelligence environment is effective and efficient. Then, and only then, can they reap the benefits of business intelligence.

What are some common mistakes that companies make when it comes to their business intelligence environment?

While business intelligence can be extremely beneficial to companies, there are some common mistakes that companies make when it comes to their business intelligence environment.

Not understanding the different types of data

When gathering data for business intelligence, it’s important to understand the three main types of data:

Structured data is organized in a predefined format, typically stored in a database, making it easy to access and analyze.

Unstructured data lacks a predefined format and includes things like emails, social media posts, and images.

Semi-structured data falls between structured and unstructured, with examples including XML and JSON files.

Understanding these data types and how to gather and analyze them effectively is crucial for businesses.

Not gathering data from all relevant sources

In order to make accurate decisions, companies need to gather data from all relevant sources. This includes internal data sources like financial reports and customer databases as well as external data sources like news articles and social media posts.

Not cleaning or standardizing the data

Once the data has been gathered, it needs to be cleaned and standardized. This includes things like removing duplicates, standardizing formats, and filling in missing values.

Not loading the data into the BI system effectively

Once the data has been cleaned and standardized, it needs to be loaded into the BI system. This can be done manually or through an automated process.

Not analyzing the data properly

Once the data is in the BI system, it needs to be analyzed. This includes things like creating reports, running queries, and building dashboards.

Not making decisions related to the data

Once the data has been analyzed, it needs to be used to make decisions. This includes things like setting prices, launching new products, and hiring new employees.

Not empowering people to use data

In order for business intelligence to be successful, people need to be empowered to use data to make decisions. This includes training people on how to use the BI system, giving them access to the data, and encouraging them to use data when making decisions.

Fortunately, you can avoid many of these mistakes by taking the time to develop a strategic business intelligence environment.

Business intelligence can be extremely beneficial to companies, but only if it is done correctly. In order to make sure that your business intelligence environment is effective, you need to take into account the four environmental factors: data, people, processes, and technology.

By understanding these four environmental factors and taking steps to ensure that they are all being considered, you can avoid many of the common mistakes that companies make when it comes to business intelligence.

When you exist in the right data environment for business intelligence, you set your company up for success.

How Domo Supports a Smarter BI Environment

Building an effective business intelligence environment requires more than good intentions. It takes the right infrastructure, processes, and tools. That is where Domo comes in. With powerful data integration, user-friendly dashboards, real-time analytics, and a flexible cloud-based platform, Domo helps companies bring their BI strategy to life. From data collection to decision-making, Domo empowers teams to access clean, centralized data, visualize insights instantly, and act with confidence. Whether you are just getting started or optimizing a mature BI environment, Domo makes it easier to align your data, people, processes, and technology all in one place.

Ready to see it in action? Watch a demo or try Domo for free today.