What Is Business Intelligence (BI)

The term Business Intelligence was first used back in 1865 by British writer Richard Millar Devens in his book Cyclopædia of Commercial and Business Anecdotes. He used it to describe how a banker gained a commercial edge by gathering and using information before his competitors did. In other words, the original idea behind BI was using information to make better business decisions.

The modern concept of BI, however, emerged much later, mainly in the 1980s and 1990s, as new technologies started being developed.

Business Intelligence, as we know it today, is a set of technical procedures for extracting, managing and analyzing an organization's data in order to surface key findings that inform strategic decisions.

BI tools give users access to historical, current, third-party or in-house data, as well as structured and unstructured data, including data from social media. This lets them understand how the business is performing and analyze what it can, and should, do going forward.

BI doesn't tell decision-makers what will happen if they take a certain action, nor what they should do. It's not just about building reports either. Rather, BI offers a way of consuming information that helps identify trends and generate insights, insights that improve decisions, catch problems or anomalies, grow revenue and uncover business opportunities.

Business Intelligence vs. Business Analytics

BI and BA are two approaches to working with data to support decision-making inside a company.

Business Intelligence (BI) focuses on understanding and describing what's happening in the business based on current data, while Business Analytics (BA) uses that data to run more advanced analysis and predict likely future outcomes. Tools like Power BI, Tableau or Excel are just what help apply these approaches.

For example, BI might show that the company gained 300 new customers last month and sales grew 15%.

BA, on the other hand, might analyze that data and conclude that increasing advertising and offering discounts would likely push sales up another 15% next month.

How BI Works

Systems for Business Intelligence usually rely on data warehouses to source the information they use. Their main advantage is that they bring information from different sources together in one place, making data analysis and reporting easier.

That's how BI presents results to users, as reports, charts, maps and dashboards.

Data warehouses can include an OLAP tool (Online Analytical Processing) that lets you analyze data from different angles, that is, multidimensionally; for example, breaking down sales by country, quarter and product at the same time.

The BI Process

The BI process is iterative and generally follows this order:

  • Business analysis: understanding the company's activity, its business model, processes, customers, products, competitive landscape and main operational challenges.
  • Data exploration: identifying existing data sources, how they're generated, collected and stored, and evaluating their quality and availability before designing reports or analytical models.
  • Defining KPIs: determining the key indicators that will measure performance, surface opportunities for improvement and support decision-making.
  • Data cleaning and modeling: cleaning up, transforming and structuring the data through logical and physical modeling, building data warehouses or data marts ready for analysis.
  • Interactive reports and visualization: building dynamic dashboards and interactive reports with tools like Power BI or Tableau to visualize key information and make it easier to analyze.
  • Ongoing monitoring and optimization: overseeing data quality and freshness, catching errors, rolling out continuous improvements and adapting the system to new business needs.

Benefits and Challenges

BI is as much a way of thinking as it is a set of software and hardware. By adopting a data-driven culture centered on process, digital technology and data analysis, an organization can uncover key information to make better decisions and gain a competitive edge.

That cultural shift doesn't happen just by having the right software and hardware, it also takes building the right processes, methodologies and an organizational culture geared toward the strategic use of data.

Benefits

  • Clearer reports: BI makes information easier to read and understand through dashboards, charts and interactive reports.
  • Data and information consolidated in one place: it brings together data from different systems and departments, making information easier to access and analyze.
  • Better efficiency and process optimization: it helps spot inefficient tasks, bottlenecks and opportunities to improve operational processes.
  • Deeper, data-based conclusions: it helps identify trends, patterns and relationships that aren't obvious at first glance.
  • Better decision-making: it provides reliable, up-to-date information that supports more precise strategic and operational decisions.
  • Higher customer satisfaction: by better understanding customer behavior and needs, companies can improve their products, services and experiences.
  • Higher employee satisfaction: it gives easier access to clear information and automates repetitive tasks, improving how work is organized and how productive teams are.

Challenges

  • Conflicting conclusions: when different departments analyze data on their own, self-service BI, they can reach different or contradictory conclusions. This can create confusion and make it harder for the company to stick to a single course of action, especially when human bias creeps into the analysis.
  • Lack of technical skills: specialized profiles in analysis, engineering and data modeling are needed to properly build and roll out BI solutions.
  • Costs: building a robust BI system can require a significant upfront investment. Over the long run, though, the benefits usually outweigh those initial costs.

BI Best Practices

Data is the engine behind successful organizations. Beyond the traditional data-related roles, decision-makers across the whole organization need flexible, self-service access to data-driven insights, powered by artificial intelligence (AI). From marketing and human resources to finance and supply chain, leaders in every area can use this information to improve decision-making and productivity company-wide.

Organizations benefit when they can fully evaluate their operations and processes, understand their customers, analyze the market and drive improvements. To do that, they need tools capable of pulling business information from multiple sources, analyzing it, uncovering patterns and finding solutions. To build a BI system that makes all of this possible, organizations should:

  • Set clear business goals: figuring out what information is most valuable and actionable helps define what data needs to be collected and what capabilities the BI system needs to have.
  • Provide company-wide user training: the cultural shift toward a data-driven organization works best when every user gets clear training on the new tools. Insufficient training can discourage adoption or lead to incorrect results.
  • Monitor data quality and relevance: data needs to be constantly monitored to ensure results stay consistent and reliable. It also needs to be secure, accurate, private and properly governed.
  • Ensure decision-makers have access to the data: many companies still don't collect or analyze their data properly. Organizations with modern data architectures and solid BI adoption gain a competitive edge and can move toward predictive analytics and real-time decisions.

BI Use Cases

Business Intelligence adds value across many functions and industries. Some examples:

  • Customer service: quick access to customer and product information to resolve queries or issues more efficiently.
  • Finance and banking: helps assess an organization's financial health, spot risks and predict future outcomes by combining customer data with market conditions.
  • Healthcare: gives fast access to medical information and improves tracking of internal operations, like inventory and resources.
  • Retail: allows comparing performance across stores, channels and regions to cut costs and improve results.
  • Sales and marketing: by combining data on promotions, sales and customer behavior, companies can plan more effective campaigns and improve targeting.
  • Security and compliance: centralizing data improves monitoring accuracy and makes it easier to meet regulatory requirements.
  • Statistical analysis: descriptive analysis helps detect trends and understand why certain things happen.
  • Supply chain: real-time visibility into global data helps identify inefficiencies and logistics bottlenecks.