What is business intelligence?

Business intelligence (BI) is the practice of collecting and transforming raw data into accessible, actionable business insights for stakeholders and decision makers. It is a practice that can apply to virtually any business and business model. As long as there is reliable data available, business intelligence can harvest that data, structure it, and generate insights that can help to move the business forward. Over time, business intelligence seeks to improve data maturity, the measure of an organization’s ability to use their data more effectively.

While closely related, data analytics and business intelligence serve different purposes in helping organizations make data-driven decisions. Think of it this way: data analysts are the specialists who investigate specific questions, while BI professionals are the architects who build the systems for continuous monitoring and insight generation.

A data analyst often uses historical data to answer questions like, "What were our top-performing products last quarter?" or "Why did our customer acquisition cost increase?" This role is typically more generalized and focuses on the tactical analysis of data to solve a specific problem.

A BI professional, on the other hand, requires a more advanced and technical skillset. They focus on building the data infrastructure and tools, such as interactive dashboards and real-time reports, that enable multiple stakeholders to continuously monitor key business metrics. Their work provides the foundation that allows data analysts and other decision-makers to quickly access and understand data on an ongoing basis. In short, BI is the system, and data analytics is the process of using that system to find answers.

Here’s a breakdown of some additional differences:

In practical terms, BI professionals gather, structure and analyze data using various tools and techniques. That analysis is then used to generate insights, which inform business decisions large and small. But what does this look like in the day-to-day? Let’s imagine you’ve just started your first role in business intelligence, and you need to help a retail company improve sales. You might start with a common three-step framework: capture, analyze, monitor.

1. Capture: Start by looking for the most relevant data sources that you can use to capture data. For this example, let’s say transaction records and customer demographics. As you start to collect this data, you’ll also need to think about data infrastructure for efficient storage and retrieval, while ensuring overall quality, consistency, and security.
   
   
2. Analyze: After gathering the data and creating an optimized infrastructure for it, it’s time to analyze, leveraging tools that help structure the data and generate insights. This could be creating a tool that identifies trends in customer purchasing behavior, so analysts and managers can understand which products are selling well, and why.
   
   
3. Monitor: You’ll need to implement systems that track key performance indicators (KPIs) on an ongoing basis, so you can analyze and respond to new developments. For instance, you might build an interactive dashboard to track daily sales figures and inventory levels, so that store managers can respond in real-time to changes in demand.

All three stages are continuous and iterative, so business intelligence professionals are always working to evolve and upgrade the systems and tools they create.

You can have the best data in the world, but if you can’t communicate the resulting insights, it’ll go to waste. This is why effective reporting matters so much. Reporting is the step that communicates the data and insights, leading to action, results, and increased data maturity.

Good reporting:

• Is easy to understand, even for non-technical users.
• Is relevant to the specific needs of stakeholders.
• Provides context to help users interpret the data correctly.
• Delivers business insights in a timely manner.

What’s the value of business intelligence to stakeholders, decision-makers, and an organization?

• Informed decision-making: BI helps organizations make faster, more informed decisions based on real-time metrics and trends.
  
  
• Improved efficiency: By automating processes like reporting and monitoring, BI saves time and allows stakeholders to focus on higher-value tasks.
  
  
• Clearer communication: BI tools present data in more accessible ways that help stakeholders understand insights without getting lost in irrelevant details.
  
  
• A competitive edge: Organizations with effective BI can spot opportunities and risks earlier.

To improve data maturity and generate business insights, business intelligence professionals must understand their stakeholder needs, build and manage data infrastructure, and communicate skillfully. Business intelligence professionals should also be well-versed in common programming languages, data governance practices, and advanced data analytics techniques. Here’s a breakdown of the core competencies of a business intelligence professional, including some of the things we’ve already discussed:

• Data modeling: Business intelligence designs the structure and relationships of data to ensure consistency, clarity, and usability across systems. Data modeling helps organize information so users can understand how data is connected and trust the results of the analysis.
  
  
• Databases and data warehouses: These are systems that store and organize data for both day-to-day operations (online transaction processing databases) and analytical purposes (data warehouses, data lakes or data marts, and online analytical processing databases).
  
  
• Data pipelines and ETL/ELT: These refer to automated processes that move data from various sources to its final destination, such as a data warehouse. This includes ETL (extract, transform, load) or ELT (extract, load, transform) workflows that clean, convert, and integrate data for analysis.
  
  
• Data visualization and reporting: These are the primary means of translating complex data into actionable business insights for stakeholders. Without these skills, a business intelligence professional cannot effectively communicate the value of their work or empower others to make data-driven decisions.
  
  
• SQL: Structured Query Language (SQL) is the programming language at the foundation of accurate reporting and actionable insights. Whether you’re a junior data analyst or working in advanced business intelligence, you’ll likely use SQL daily to find, organize and analyze data stored in databases.
  
  
• Python: Nowadays, many business intelligence professionals use the programming language Python, discussed earlier, to help speed up analysis and automate repetitive tasks.
  
  
• Data governance: As a business intelligence professional, you are responsible for establishing and enforcing the policies and procedures that preserve data availability, integrity, and security.