Business Intelligence (BI): A Complete Guide

Business intelligence, known as BI, is the secret weapon for companies looking to make sense of the information avalanche of their data. Business intelligence exists to guide people—and the companies they work at—to actionable insights by transforming raw information into smart, effective strategies. The importance of business intelligence is that data is no longer just data, but a new currency. That's where business intelligence serves as the magnifying glass that helps businesses make sense of the plethora of data at their disposal, aiding them in making more effective and strategic decisions. Think of business intelligence not as an optional extra, but as a must-have tool in any business arsenal. And when it’s working properly, businesses find themselves ahead of the competition. 

Here’s one way to put this in perspective: the global BI market, valued at a hefty USD 24.9 billion in 2020, is projected to soar to an astounding USD 41.1 billion by 2026, according to Mordor Intelligence. That's an annual growth rate of 8.5%—a statistic too significant to ignore. Harnessing the power of BI means turning data into meaningful action, improving operations, sharpening strategic planning, and driving profitability. With this guide, we'll dive into the exciting world of BI, unpacking its potential and showcasing how it can supercharge your business's growth. So let’s start the exploration of BI's crucial role in business.

Table of Contents:

• What Is Business Intelligence?
• How Business Intelligence Works: The BI Process
• Business Intelligence Examples
• Benefits of Business Intelligence 
• Challenges of Business Intelligence
• Types of Business Intelligence 
• History of Business Intelligence
• Future of Business Intelligence
• Business Intelligence Tools, Platforms, & Solutions
• How to Choose the Right BI Solution
• Take the Next Step with Business Intelligence

What Is Business Intelligence (BI)?

So what is business intelligence exactly? The goal of BI is pretty simple at the end of the day—businesses want and very much need to take action on their data. Not just any actions, but the right actions. And the faster they can do that, the more successful they will be.

The process of taking action on data to gain a competitive advantage is the heart of what business intelligence exists to solve. 

This process assists in data-driven decision making by offering insights into current and historical data within an organization's context. BI helps businesses convert raw data into meaningful information, enabling them to understand their operations better, predict future trends, and make informed strategic decisions. It's used in everything from spotting sales trends to improving bottom line efficiency, making it an integral part of modern corporate strategy.

BI can provide insights into diverse areas such as customer behavior, market trends, operational efficiency, and more. For instance, a retailer could use BI to analyze buying patterns and trends, which helps it tailor marketing efforts to its exact audience, which in turn boosts sales. A manufacturing company might leverage BI to enhance production efficiency, reduce wastage, and improve overall profitability.

Business intelligence software is a kind of application software that runs everything we described above. BI software is designed to retrieve, analyze, transform, and report data for companies. These tools are essential for understanding an organization's raw data and are used for everything from basic reporting and online analytical processing to complex predictive analytics. And while this process originally took place on premises for companies, it’s now almost entirely done through cloud computing, which means the data isn’t stored on physical servers but on cloud-based data warehouses. 

How Business Intelligence Works: The BI Process

How business intelligence actually works isn’t as simple as it sounds. In order to get to dashboards or analytics that point to action, there’s a process any business must follow first.

The reality is that every business is collecting data all of the time. That means, in some ways, every business is in the data business. It’s the step of turning that data into action that drives the heart of business intelligence. The business intelligence process involves several key steps to effectively manage data and turn it into actionable insights. Let’s examine the four key steps:

1) Data Collection:

The first step in the business intelligence process is data collection. This involves gathering raw data from various sources within and outside the organization. These sources can include transactional databases, Enterprise Resource Planning (ERP) systems, sales data, website analytics, surveys, social media monitoring, customer relationship management (CRM) systems, and more. The data can be both structured (organized in tables) and unstructured (free-form text, images, videos, etc.).

While all companies have the potential for data collection, not all do it automatically. The approach largely depends on the size of the company, the resources at its disposal, and its level of digital sophistication. The trend, however, leans towards automatic data collection, especially with the growing digital orientation of businesses.

2) Data Storage:

Once the data is collected, it needs to be stored in a central repository for easy access and retrieval. Data warehouses like Snowflake, BigQuery, or Redshift; or data lakes like Databricks, are commonly used for this purpose. These repositories are designed to handle large volumes of data and often scale by separating compute and storage layers. The data is transformed and organized into a consistent format during the storage process, making it easier for analysis.
‍
Data warehousing can be an intricate process, considering it involves consolidating data from various sources into a single, comprehensive repository for advanced analysis and reporting. The complexity stems from data diversity, the need for real-time updates, handling high data volumes, and maintaining data integrity and security. Nevertheless, executing data warehousing correctly is crucial for businesses. It provides a reliable, consolidated view of the organization's data. A well-implemented data warehouse can significantly enhance a company's efficiency, productivity, and overall competitive advantage.

3) Data Integration and Cleaning:

Often, data comes from disparate sources, and it may be inconsistent or contain errors. The next step in the BI process is data integration and cleaning, where data from different sources is combined, and any errors or inconsistencies are resolved. This step is crucial to ensure that the data used for analysis is accurate and reliable.

This process is typically carried out by data analysts, data scientists, or designated data cleaning specialists within an organization. The analysts use various techniques and algorithms to identify discrepancies, such as inconsistencies, duplicates, or missing values in the collected data. The importance of data cleaning cannot be overstated. Clean data leads to more accurate analyses and reliable insights, empowering businesses to make informed decisions. Without it, the risk of inaccurate interpretations and misguided decisions increases, which can have significant adverse impacts on a company's performance and growth.

4) Data Analysis:

With the data now stored and cleaned, it's time to extract meaningful insights from it. BI employs various analytical techniques, including descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics helps to summarize historical data, diagnostic analytics aims to identify the reasons behind past performance, predictive analytics uses historical data to make future predictions, and prescriptive analytics suggests optimal actions based on the analysis. All are important, and all guide companies to make different decisions about what to do next.

5) Data Presentation:

The final step in the BI process involves presenting the analyzed data in a format that is easily understandable and actionable by decision-makers. Dashboards, reports, charts, and graphs are common tools used to present data visually, making it easier to identify trends, patterns, and opportunities. These visualizations empower stakeholders to make data-driven decisions, optimize business processes, and identify areas for improvement.

‍

Business Intelligence Examples

Now let’s look at business intelligence examples. There are nearly an endless number of ways to display the information gathered in the business intelligence process. That’s because there are a nearly endless number of ways to slice and dice the data at a company’s disposal. The key is to focus on what story you want to tell through the final dashboard or presentation, and to make that information front and center.

For instance, a retail business could use business intelligence to analyze years' worth of sales data, identifying patterns and trends that might predict future sales spikes or dips. The software could also enable a deep-dive into customer segmentation, helping the business to tailor marketing and sales efforts towards specific customer groups to improve conversions.

In a manufacturing setting, BI could be used to optimize supply chain processes. By analyzing procurement data, production metrics, and inventory levels, the software could identify bottlenecks and inefficiencies in the production line. This insight could then be used to streamline operations, potentially leading to significant cost savings and improved production times.

In the restaurant industry, DoorDash uses business intelligence to optimize operations, compare vendor performance, and ultimately make faster decisions. In health care, researchers can find participants for studies while protecting personal identifiable information (PII.) And in finance, asset managers at Blackstone can achieve data democratization. 

Benefits of Business Intelligence

The high-level benefit of business intelligence is that companies are making decisions based on data about their company, what’s happened in the past, and what may happen in the future. That includes the following:

• Better, Faster Decision Making: One of the most significant benefits of business intelligence is its capacity to enhance decision-making processes. Businesses are awash in data, from financial information to customer behavior analytics. However, it is challenging to translate this raw data into actionable insights. BI helps analyze complex datasets, identify trends, and predict future outcomes, thereby enabling leaders and managers to make informed decisions.

• Improved Operational Efficiency: Another crucial advantage of BI is that it increases operational efficiency. BI systems collect, integrate, and present business information in an easy-to-understand format. This reduces the time and resources spent on manual data gathering and analysis, freeing up staff to focus on more critical tasks. Moreover, the insights provided by BI tools can highlight areas of inefficiency or waste.

• Enhanced Customer Satisfaction: Business intelligence can also significantly improve customer satisfaction. By analyzing data on customer behavior, preferences, and feedback, companies can better understand their customers' needs and tailor their offerings accordingly. Furthermore, this data can be used to predict future consumer trends, allowing businesses to stay ahead of the market and continually meet their customers' expectations. This leads to more satisfied customers, increased customer loyalty, and ultimately, higher revenue.

• Identifying New Opportunities: BI tools can uncover new opportunities for growth. By analyzing market trends, customer data, and competitive landscapes, BI can help identify underserved markets, new product or service opportunities, or potential partnerships. This intelligence allows businesses to capitalize on these opportunities before their competitors do. Moreover, these insights can help companies make strategic decisions about resource allocation, strategic initiatives, and business development.

• Data Security and Governance: Business intelligence systems also play a pivotal role in strengthening data security and governance. Businesses generate and manage vast amounts of sensitive information, such as financial data, customer details, and trade secrets, which need to be carefully protected. BI systems provide robust security features, including user authentication, encryption, and access controls, which prevent unauthorized access and data breaches. Additionally, they allow for comprehensive data governance by ensuring data quality and consistency. They provide functionalities for data cleansing, validation, and consolidation, making sure that the data used for decision-making is accurate, reliable, and up-to-date. Moreover, BI systems enable companies to comply with data privacy regulations by tracking data usage and maintaining detailed logs for auditing purposes. This not only safeguards the company's critical data, but also bolsters its reputation and trustworthiness in the eyes of stakeholders.

• Mitigate Risk: One of the biggest challenges for enterprises is finding a tool that has robust security and governance. That means business intelligence plays a vital role in risk mitigation. The business environment is filled with uncertainties and potential risks, ranging from economic fluctuations to evolving consumer behaviors. By leveraging BI, businesses can monitor and analyze these trends, predict potential impacts, and take proactive steps to mitigate risk. This allows businesses to respond swiftly to changes in the business environment, ensuring their survival and growth in a competitive market.

Challenges of Business Intelligence

While business intelligence might sound quick and easy to implement, substantial challenges can still arise for companies. One key challenge is data quality and integration. Given that BI systems depend heavily on the input data, if the data is inconsistent, inaccurate, or outdated, the insights generated will be unreliable. Integrating this data from diverse sources into a coherent, usable format can also be complex and time-consuming. 

Another significant challenge is that not everyone in a company has the same level of proficiency with learning new tools. Ensuring user adoption and literacy can be a massive challenge. While BI tools can provide powerful insights, they can be complicated and intimidating for non-technical staff. Training employees to use these tools effectively and encouraging a data-driven culture within the organization can be difficult. 

Additionally, maintaining data privacy and security is increasingly crucial as data volumes grow, and failure to do so can lead to legal issues and damage to the company's reputation. Lastly, while BI systems can automate many processes, they require substantial upfront investment and ongoing maintenance, presenting a challenge for budget-constrained businesses. That means companies that are hesitant to go all in might find themselves similarly hesitant to spend what’s necessary to become truly data-driven with business intelligence. 

Types of Business Intelligence

There are several key types of business intelligence most companies rely on, usually falling into one of four key categories:

Descriptive Analytics: 

This type of analytics utilizes historical data to comprehend past events. By leveraging data consolidation and data mining methodologies, it provides insight into what has occurred previously. The question it answers is: "What has happened?" Reports giving a historical perspective of business metrics are a typical example of this.

Diagnostic Analytics: 

This kind of analytics goes deeper into the findings of descriptive analytics to identify the root cause of the results. It uses methods like data drilling, data discovery, data mining, and correlation analysis to answer: "Why did it happen?"

Predictive Analytics: 

Predictive analytics applies statistical models and forecasting methods to predict future possibilities. It employs various techniques, such as data mining, artificial intelligence, machine learning, and predictive modeling to answer: "What could happen?" Industries like health care, retail, finance, and more use it extensively to foresee future trends.

Prescriptive Analytics: 

Prescriptive analytics uses optimization and simulation algorithms to suggest potential outcomes. At the end of the day, all BI helps guide action. Prescriptive analytics work on prescribing which action to take without the user needing to actually be the one to make the decision. 

It advises on what steps to take based on the findings of descriptive, diagnostic, and predictive analytics to answer: "What should we do?" The key distinction of prescriptive analytics is that it not only predicts future results, but also suggests actions to leverage these predictions.

‍History of Business Intelligence

When it comes to the history of business intelligence, you could argue that the very first examples of business intelligence were the advent of punch cards that were used to tabulate and analyze data. Hans Peter Luhn, an IBM researcher, is credited with first using the term "business intelligence" in a 1958 article titled, "A Business Intelligence System," where he described an automatic system that disseminates information to different parts of an organization. Luhn's system emphasized the potential use of computer technology in data processing for business needs.

However, it was Howard Dresner, a Gartner Group analyst who now runs his own advisory firm Dresner Advisory Services, who popularized the term "business intelligence" in the context we understand today. In 1989, Dresner defined BI as "concepts and methods to improve business decision making by using fact-based support systems." He basically broadened the term to include a spectrum of tools, technologies, and applications that help organizations gather, store, analyze, and give access to data to help make informed business decisions.

In conclusion, while Luhn is credited with the first use of the term "business intelligence," it was Dresner who defined and popularized the term in the way we understand it today. Both made significant contributions, but they applied the term in different contexts.

It was Dresner's idea that blossomed, leading to a remarkable transformation in the way companies use data. Over the years, BI has evolved from simple data collection to complex data mining. The advent of AI and machine learning providing predictive insights is now shaping the future of decision-making in business.

Future of Business Intelligence

As machine learning and AI overhaul business as we know it today, it’s almost impossible to fully predict the future of business intelligence. In addition to AI transforming workflows, the volume of data itself has and will continue to grow exponentially, which means increasing demand to analyze it and drive action from that data. Here are a few key ways the future of BI will change:

Privacy and Security 

As companies work to expand their BI capabilities, security and privacy will become even more important. Ensuring the privacy, integrity, and security of growing amounts of data will become paramount. New protocols and regulations will likely emerge to protect sensitive information and ensure ethical data practices. Consequently, BI tools will need to incorporate advanced security features, such as encryption and multi-factor authentication, and adhere to regulatory standards. Moreover, as businesses handle larger amounts of data, the focus on data governance will increase, ensuring data quality and reliability, and driving the implementation of policies for data management and usage.

Democratization of Data 

Democratization of data will play a pivotal role in the evolution of business intelligence. BI tools will become more user-friendly and intuitive, allowing employees across all levels of an organization to harness and understand data, irrespective of their technical abilities. Becoming a data-driven company with a data-driven culture will no longer be for only elite companies. This shift towards data democratization will make interactive dashboards and self-service BI tools the norm.

Predictions

With AI and ML, predictive and prescriptive analytics will grow, allowing businesses not only to understand what has happened and why, but also to forecast what will happen next and recommend actions to optimize future outcomes. Predictive analytics in particular will be significantly improved. These algorithms can sift through massive amounts of historical data to identify patterns and trends, which can then be used to forecast future trends and behaviors. This capacity will improve the accuracy of predictions.

Faster Data Preparation

AI and ML can also automate data preparation and real time processes, tasks that traditionally consume a significant amount of time. These technologies can identify, clean, and format unstructured data, making it ready for analysis much more quickly and efficiently.

NLP Advancements

Advancements in natural language processing (NLP) will make BI tools more user-friendly and accessible. Users will be able to interact with BI software using their natural language, asking questions and receiving answers in an intuitive and user-friendly manner. This not only makes the data more accessible to non-technical users, but also helps to integrate data-driven decision making into daily business activities. While this type of AI is already used, it will grow considerably, and may even become the key way users interact with business intelligence in the future. 

Real-Time Analytics

Lastly, AI and ML will enable real-time analytics, a game-changer for BI. Traditional BI analyses are often based on historical data and can be time-consuming. With AI and ML, businesses can analyze data in real-time, enabling instantaneous decision-making based on current data. This can be particularly useful in rapidly-changing industries where real-time data can provide a competitive edge.

Business Intelligence Tools, Platforms & Solutions

Since BI is truly a process, a wide array of business intelligence solutions are available to assist businesses in data analysis and decision-making processes. These tools facilitate data collection, integration, analysis, visualization, and reporting, providing businesses with valuable insights into their operations and the marketplace.

The types of BI tools include data visualization software, data warehousing solutions, dashboards, data mining applications, cloud data services, and more. These technologies often use sophisticated algorithms to analyze data and uncover patterns, trends, and relationships that might not be evident through traditional analysis methods. The best solution for a business often depends on its specific needs, the size of its data sets, and its technical capabilities.

Key BI tools include:

‍Data Visualization Tools: Software like Sigma, Tableau, or Microsoft Power BI can help businesses visualize their data in a more understandable and insightful manner through charts, graphs, and other visual elements.

‍Data Warehousing Solutions: These are systems for storing, retrieving, and managing large amounts of data. Examples include Snowflake, Databricks, Google BigQuery.

‍Data Modeling: Popular data modeling tools like dbt help teams help teams build data pipelines, perform data transformations, and ensure data quality.

‍Data Mining Applications: These tools, such as RapidMiner and IBM SPSS Modeler, enable businesses to explore large datasets and extract meaningful patterns and insights.

‍Reporting Tools: Software like SAP business Objects or Oracle BI can generate detailed reports to inform business decisions.

By leveraging these tools, businesses can gain a deeper understanding of their performance, customers, and market, which can inform strategic decisions and foster a competitive advantage.

‍How to Choose the Right BI Solution

There’s a lot to consider when choosing the right business intelligence solution for your business. Here are a few key factors we recommend considering: 

• Understand Your Business Needs and Goals: Clearly define the problem you are trying to solve, the insights you want to gain, and how you plan to use these insights. 

• Figure Out Which Kind of Analytics You Need: Identify if you need descriptive, diagnostic, predictive, or prescriptive analytics, or a combination of all of them. Different tools will be better at each one. 

• Determine Your Data Availability: Analyze what kind of data you have, how much data you have, where it's coming from, and how quickly it's generated. Also look at the quality of the data and figure out your process for cleaning and preparing the data for analysis. 

• Audit Your Resources: Resources include your budget, team members' skills, and company infrastructure. You might need more advanced technical skills for certain apps, while others can be managed by beginners. 

• Research Available Tools: Begin the process of auditing all of the available BI vendors. Look at each product’s features, as well as how easy it is to use, the types and number of integrations with other platforms, the ability to scale, and the customer/ vendor support it provides.

• Request a Demo or Free Trial:  Most BI vendors offer a demo or free trial of their product. This allows you to try it for yourself and decide if it really fits all of your company’s needs.

• Prioritize Data Security and Privacy: Any application you onboard should comply with all relevant data security and privacy regulations. Your BI solution should have robust security measures in place to protect your data as well as the data of your customers. .

• Read Reviews and Ask for References: Look at reviews on sites like TrustRadius and G2. You can even ask for references from other businesses that have used the tool. 

• Calculate the Total Cost of Ownership (TCO): On top of the direct costs, factor in all of the indirect costs like training, implementation, and maintenance.

• Choose a Solution That Scales: Whatever analytics application you go with must be able to grow with your business. Your data analytics needs will evolve, which means the solution also must be able to scale and adapt as well.

 Take the Next Step With Business Intelligence

At Sigma, we have a unique perspective on business intelligence. We sit at the intersection of artificial intelligence, business intelligence, and human intelligence. All that means is that we believe you need each of these aspects in order to build an effective strategy to uncover insights for your business. 

Sigma is a robust example of business intelligence software that enables data democratization by offering cloud-native and collaborative data exploration. It allows users, regardless of their technical skill level, to analyze large volumes of data and derive actionable insights to inform strategic decision-making. Because the data never leaves the warehouse, security and governance is built in. With a cloud-native solution, users get access to billions of rows and can work in the spreadsheet interface they already know. They can also use input tables to add more data to the analysis and perform predictive analysis in real time.

With new technological advancements and emerging trends, the landscape of BI is set to continually evolve as AI and ML grow into our work lives more than ever. However, the underpinning principle endures: the process of transforming data into insightful, actionable information to drive business decisions. Whether you're a data analyst, a business user, the CEO, or simply an enthusiast, this understanding of BI equips you with the knowledge to navigate the ever-changing data landscape. 

You can read more about how Sigma works or download a free trial here.

‍

‍