Business intelligence (BI) refers to the tools, systems, and strategies that create analysis and planning processes within a corporation. Comprehensive BI systems allow a company to gather, store, access, and analyze corporate data to aid in decision-making. These decisions include:
Changing company e-commerce strategies due to sales trends and customer clicks
Sending new sets of emails to prospective customers and leads
Purchasing new software because an old application wasn’t helping the company
Hiring a new employee because data revealed that there was a skills gap in one department
Because it incorporates data from across the enterprise—revenue operations, sales, supply chain, logistics—business intelligence insight is beneficial in areas like:
Business intelligence and related business technologies are a growing career field. For example, the job market for operations research analysts is expected to increase 25 percent between 2020 and 2030, according to the Bureau of Labor Statistics. Some of these skills include:
SQL, Python, and/or C/C++ programming language proficiency
Business intelligence software is designed to extract important information from an organization’s raw data to reveal insights that will help a business make faster and more accurate decisions. BI software integrates data from across the enterprise and provides end-users with self-service reporting and analysis.
OLAP: Based on the OLAP cube, a data structure optimized for quick data analysis. This enables four types of multidimensional data analysis: drill down, roll up, slice-and-dice, and pivot. These analyses are done very quickly on a large volume of data and provide a foundation for complex calculations, trend analysis, and data modeling.
Performance management tools: plans, often driven by software, for taking the focused data for different parts of a business and aggregating it into an operational plan. They’re helpful for a variety of situations, including budget forecasting, supply chain management, and risk management among others.
Data warehouse: Not to be mistaken with a database, a warehouse aggregates data from numerous sources for comparative analytics. Data warehousing allows business leaders to examine data from multiple applications or systems within the organization and understand how they are related.
Data mining: The process of uncovering patterns and unseen relationships across large sets of information. It uses a combination of statistics, artificial intelligence, and machine learning to determine what is most important from dense, repetitive data.
Real-time reporting capabilities: real-time reporting in business intelligence software helps an organization make a decision using live data. This is especially useful in marketing scenarios where a last-minute campaign or slight shift can have a dramatic impact on sales.
Features of business intelligence tools
To visualize information, BI tools allow users to:
View all data from a unified user interface
Create reports that they can bring to executive teams within the organization
Design dashboards for calculating metrics and viewing charts
Connect other applications to the business intelligence platform for increased organizational collaboration
Business Intelligence vs Business Analytics
BI and business analytics solutions include very similar features, like data collection, analyzing, and reporting; but their end goals are slightly different. Business analytics focuses on forecasting, predicting, and notifying and includes features like decision trees to help companies make choices based on data.
Business Intelligence
Business Analytics
Descriptive
Predictive, prescriptive
Focused on what’s happening now and what happened in the past
Focused on what’s going to happen in the future and what should be done for better outcomes
Data for business managers to analyze
Data for decision makers to study and then make business changes
Non-technical for end users
Technical; often analyzed by experienced data scientists
BI Example: a pharmaceutical company uses sales trends from the past five years and existing customer data to see what medicines have been selling best and how sales have changed over the years.
BA example: a pharmaceutical company uses customer data, sales from the past five years, and current medical marketing data to predict where next quarter’s sales should be and plan their budgets accordingly.