Businesses are generating more and more data every data, but how can they turn that raw data into better decisions, operational efficiencies, and strategic advantages? That's where business intelligence (BI) comes in.
So what is business intelligence, exactly? And why should you care, even if you don't spend your days poring over data tables as part of a specialized data analysis team?
In this guide, we'll walk you through all the details of business intelligence, including common approaches, common tools and software, and why everyone should consider improving their business intelligence skillset.
What is business intelligence?
Business intelligence (or BI, for short) is the science of turning organizational data into strategic insights. It includes the methods (like data analysis and data visualization) and technologies that companies use to make smarter decisions with data.
Modern organizations collect valuable data on their operations, products, finances, and more. Business intelligence connects the dots between different data sets in order to uncover and understand what’s happening at the company.
To do so, business intelligence analysts need to access and transform the data, explore patterns, perform analyses, create data visualizations, and communicate findings across their organization. Ultimately, this work allows departments and leaders to make more informed decisions and operate more efficiently.
Who should care about business intelligence?
Oftentimes, business intelligence reporting is conducted by a data team or a business intelligence team. But, business intelligence skills are often useful for other team members across the business — even those who don’t work with data day-to-day. For example, product managers with a deep understanding of their business data can more effectively prioritize features based on the lifetime value of a customer. Likewise, data-driven marketing managers can more precisely identify how campaigns impact their pipeline.
When employees can make sense of shared data visualizations and analyses, they can make decisions faster and with greater confidence. Teamwide data literacy facilitates cross-functional collaboration and transparency between analysts and their stakeholders for quicker iteration, less back-and-forths, and more trusted results.
To that end, organizations are working to build better data culture, including by investing in their employees’ data analysis skills across roles and departments.
What are the essential steps in business intelligence?
The specific steps to get from raw business data to useful insights will differ based on each question, project, and company. In general, business intelligence involves:
Data collection: Database engineers build data warehouses to efficiently store and share company data, often in relational databases.
Data access: Data analysts or business intelligence analysts access and return relevant data from their data warehouse, usually by writing SQL queries.
Initial data exploration: Data analysts gain familiarity with business data, uncover anomalies, and take a first look at patterns and trends through charts, summary statistics, and exploratory analyses.
Data transformation: Data is transformed through cleaning and wrangling (like joining tables, filtering, parsing or aggregating values, pivoting, selecting variables, etc.). This can take place before, during, and after initial data exploration.
Analysis and modeling: Data analysts answer specific business questions using data, with outputs usually captured in data visualizations or tables.
Communication and action: Findings from analyses are polished and added to dashboards, reports, or presentations to share with teammates and stakeholders. With results in-hand, the team can discuss and decide on what actions to take.
These steps are not a one-way street, and business intelligence is always iterative. At any point in or after a project, analysts and collaborators may realize new ideas or questions that inspire more exploration charts or different analysis paths.
Business intelligence analysts may also find that dashboards and reports that they’ve previously created are no longer being effectively utilized — a process known as dashboard rot — and so should continuously monitor usage, and look for ways to improve work to ensure their dashboards, charts, and data visualizations are accurate, actionable, and aligned with evolving business goals.
What are common approaches in business intelligence?
There are as many methods to analyze business data as there are business questions that need answering. Which is to say: quite a lot! But some of the more common techniques are:
Data mining
Scouring business data to discover patterns, changes, and relationships in complex data. Methods include dimensionality reduction (like principal component analysis), regression, and interactive data visualization to find takeaways in big, multivariate data.
Benchmarking
Comparing company metrics with internal baselines, industry standards, or competitor performance. An example of benchmarking in business intelligence is Voltron Data’s Theseus engine benchmarking analysis, showing how cost and performance compare across internal and external engines.
Clustering
Identifying natural groupings within complex data based on a number of variables. Common algorithms include k-means clustering and K-nearest neighbor (KNN). In business intelligence, clustering might help to segment users into three categories (power users, casual creators, and occasional visitors) to design targeted messaging for each group.
Predictive analytics
Predicting future outcomes based on existing data, for example using time series forecasting, regression, or decision trees. These analyses can help with things like inventory planning, resource allocation, and sales projections.
What tools and software are designed for business intelligence?
There is no shortage of software designed to help companies get more from their business data. The best option depends on the specific team’s skillsets, preferences, and analysis needs.
Some data teams use code-first tools for their business intelligence. Analysts commonly write SQL, Python, and R code in scripts, or in computational notebooks like Jupyter Notebooks, Quarto, or R Markdown documents. Observable Notebooks are browser-based notebooks for collaborative data exploration and visualization, with SQL and JavaScript options to wrangle, analyze, and visualize data.
Other BI tools are primarily UI-based data visualization tools, which provide an entryway for team members less familiar with code to explore data, create visualizations, and display them in dashboards and reports. These include tools like PowerBI, Qlik, Looker, and Tableau.
One challenge with many business intelligence tools is the steep learning curve, and breakdowns in communication that lead to wasted cycles. That’s why we built Observable Canvases, our collaborative whiteboards for data exploration. In canvases, analysts and their stakeholders can use code and UI tools to wrangle and visualize data in fluid, branched analyses, with integrated AI to accelerate time-to-insight.
Everyone has a role in business intelligence
Working with and getting value from company data isn’t just for data teams anymore. In today’s cross-functional workplaces, everyone has a role to play in business intelligence.
When organizations empower people across teams to grow their data literacy and participate in data analysis, they can improve decisions made with data by bringing valuable perspectives and expertise to the table.
Observable Canvases can help data analysts, stakeholders, and other team members answer business questions faster, together. Sign up for early access to canvases today.