Business Intelligence vs Analytics

Business intelligence (BI) is a set of technologies, processes and strategies designed to generate actionable insights from business data. Business intelligence systems gather and store raw business operations data, which is analyzed to transform it into meaningful information that supports better decision-making.

Business analytics (BA) is considered by many experts to be a superset of BI. It’s often defined as the use of statistics and math to interpret data and extract meaningful insights. While the terms overlap, business analytics typically encompasses BI and extends it with predictive and prescriptive capabilities.
 
BI and BA work in tandem to help organizations to make informed, tactical and strategic decisions based on accurate and timely data. These processes transform current and historical data into action, ranging from optimizing internal processes to enhancing customer satisfaction, ensuring compliance, getting ahead of market trends, fostering innovation and more.

What is Business Intelligence?

BI uses data to create comprehensive business metrics that organizations can use to manage daily operations. Use case examples include: 

• Real-time analytics: Generating real-time insights for faster decisions and adjustments
• Customer insights: Providing a comprehensive view of customer behavior, preferences and feedback
• Customer service: Enhancing service quality by equipping staff with actionable customer data, often in real time
• Efficiency: Analyzing process performance, identifying operational bottlenecks and suggesting data-driven improvements across processes such as supply chains and staffing
• Finance: Tracking expenses, analyzing profit margins, optimizing budgets and gaining insights into overall financial health
• Risk management: Identifying and mitigating potential risks in areas including operations, compliance and finance
• Transparency: Integrating data from multiple sources for a comprehensive business view, enabling well-informed strategies 

Business Intelligence Tools 

BI tools are crucial for the process of changing raw data into actionable insights that organizations can use to identify problems, improve processes and realize better performance. Some of the most common BI tools include: 

• Data visualization tools represent datasets with easy-to-understand, interactive dashboards, graphs and charts
• SQL editors facilitate querying and analyzing data via SQL code
• Operational BI tools provide real-time analytics to monitor day-to-day operations  
• Reporting tools organize, filter and display data, including generating structured reports
• Self-service tools enable nontechnical users to query, analyze and visualize data independently without extensive technical expertise or relying on technical staff
• Spreadsheets can serve as basic BI tools, enabling users to collect, categorize and analyze data

What is business analytics?

BA comprises the nuts and bolts of turning business data into meaningful information that humans can use to make decisions. Its purpose is to interpret and present data, empowering organizations to take action to drive growth. 

Types of Business Analytics

There are four main types of BA. These can be used together for comprehensive data-driven decision-making: 

• Descriptive analytics uses historical data to understand past performance and identify trends and patterns that affect present operations
• Predictive analytics builds on descriptive analytics, using statistical models and machine learning (ML) to predict future outcomes
• Diagnostic analytics goes deeper into historical data to identify the root causes of problems
• Prescriptive analytics uses data to analyze potential outcomes and recommend actions that are likely to yield the best results 

Business Analytics Tools and Techniques

Within these types of BA, several different types of techniques and tools are used, including: 

• Data mining: The process of sorting, filtering and classifying data from larger datasets to help solve complex business problems
• Machine learning: Leveraging algorithms to identify patterns, automate processes and uncover deeper insights from data
• Online analytical processing (OLAP): Technology for performing complex, high-speed queries or multidimensional analysis on large volumes of data
• Regression analysis: This statistical technique is used to understand relationships between variables and predict outcomes
• Forecasting: The process of using data analysis and statistical methods to predict future business trends and outcomes based on historical data

Business Intelligence vs. Business Analytics

The terms “business intelligence” and “business analytics” are often used interchangeably, along with other terms such as “data analytics.” But many experts in the field differentiate them by the business challenges they focus on, questions they can answer, methods they use, expertise required and the kind of insights they produce. 

Present vs. Future Focus

Focus on the present or the future is one way BI and BA differentiate. In many cases, BI uses historical data to inform day-to-day decisions on current operations using descriptive analytics. Meanwhile, BA tends to use predictive analytics to predict future trends or events based on what has happened in the past or is happening in the present.  

Tactical vs. Strategic Focus

BI can answer questions such as “What happened?” and “How did it happen?” to inform immediate tactical decisions, while BA is geared more toward answering questions about why something happened and what will happen in the future. These insights drive high-level long-term strategy and reveal opportunities for innovation. 

Skills and Expertise Requirements

Another difference between BI and BA is BI is generally aimed at helping business users make decisions without requiring the technical expertise of data analysts or scientists. Those experts use their skills and advanced technological tools to create BA insights business decision-makers need to move the organization forward. 

How BI and BA Work Together

BA is a key superset of BI, so when organizations are choosing how to make the most of their business data to drive action, it’s not really a choice between BI and BA. However, organizations should keep in mind the individual purposes and strengths of BI and BA in determining the processes to use in making data-driven decisions.  
 
Since BI focuses more on tactical decisions for current everyday operations, an organization would focus on it for use cases such as optimization of current processes or to meet a specific goal. An example is analyzing workflows to address bottlenecks or inefficiencies. On the other hand, if a company is looking for bigger changes — such as developing new products or strategies to align with emerging global market trends — it would utilize BA for its predictive strengths. 
 
However, BI and BA combined offer the most comprehensive strategy for leveraging business data. By using BI and BA together, organizations can harness the value of their own business data to enhance efficiency, improve performance, increase profitability, manage risk, set long-term strategy and more by driving informed decisions that align with larger organizational objectives.  

Real-world Examples of BI and BA 

BI and BA offer organizations the ability to improve in the moment while also proactively moving into the future. Together, they’re used in myriad ways to solve problems, optimize processes and chart a path for innovation. Examples include: 

• Moneta, the fourth biggest bank in the Czech Republic, used advanced analytics to harness insights leading to innovative technologies, with use cases such as real-time recommendations and fraud detection. The bank has improved operational efficiency and cross-team collaboration and has been widely recognized as the country’s most innovative bank. 
• AT&T has implemented a unified approach to data and AI. The company leverages ML models to proactively protect customers and their business, using real-time data, automatic alerts and recommendations to arm employees across operations. AT&T has reduced fraud by up to 80% with this real-time, automatic detection system. 
• Michelin has transformed into a data-driven organization, empowering the company to roll out new innovations that steer the automotive industry in new directions. Michelin has democratized data from diverse sources so teams can develop their own use cases, such as using AI to predict stock outages and reduce carbon emissions in the supply chain. 
• Australian financial services provider Shift has developed a process to collect and analyze data from disparate sources to quickly understand customers’ situations. The company can uncover insights that enable staff to have more meaningful conversations with customers and personalize the end-to-end experience.  Shift has implemented real-time decision-making for certain segments of customers and is now looking to implement unified credit and risk scores enabled by ML. 

Democratizing Business Insights with Databricks 

Databricks AI/BI is a new type of business intelligence product built to democratize analytics and insights for anyone. Powered by data intelligence, AI/BI features two complementary capabilities: Dashboards and Genie. Dashboards provide a low-code experience to help analysts quickly build highly interactive data visualizations for business teams using natural language and Genie allows business users to converse with their data to ask questions and self-serve their own analytics. Databricks AI/BI is native to the Databricks Data Intelligence Platform, providing instant insights at scale while ensuring unified governance and fine-grained security across the entire organization.