Business Intelligence

Components of business intelligence

BI systems comprise a variety of methods, including analytics, data modeling, data mining, reporting, visualization and more to present data in easy-to-understand forms that organizations can use to identify problems, improve processes, discover trends and pursue business opportunities. Key components of business intelligence include:

Data collection and integration
Before data can be transformed into business intelligence, it must be gathered from sources such as databases, applications and external systems and integrated into a unified format for analysis. Data pipelines facilitate data flows from source to destination throughout the process. Data engineers use ETL (extract, transform, load) to gather data from different sources, transform it into a usable form and load it into user-accessible systems. Another type of data integration process is ELT (extract, load, transform), where raw data is moved from a source system to a destination resource, such as a data warehouse.

Semantic layers
Semantic layers act as an intermediary between raw data sources and analytical tools. They build on the foundation of data integration to present data in a business-friendly format. Semantic layers make data more actionable by improving usability, consistency and alignment with business objectives.

Data warehousing
BI is closely intertwined with data warehousing. A data warehouse serves as a centralized repository for storing data in a structured, business-friendly format, enabling seamless analysis and reporting. While the data warehouse provides the infrastructure for data storage and quality assurance, BI leverages curated data to analyze trends, evaluate performance and optimize strategies. Combining robust data warehousing with advanced BI practices can achieve faster data preparation, improved compliance and more-effective analytics.

Data analysis
Data analysis is the process of examining collected data to uncover patterns, correlations and insights. It uses statistical methods, machine learning algorithms, data mining, data discovery or data modeling and other methods and tools to process and interpret data.

Data analytics are central to business intelligence, but the two processes have different methods and goals. Data analytics work with data using technical tools to reveal what has happened or will happen. Business intelligence is a low-code/no-code process of enabling business users to make decisions and take action using that information.

Reporting and data visualization
Data visualization and reporting are key to translating insights into action. Data visualization tools create charts, graphs, dashboards and heat maps to make complex datasets comprehensible at a glance. These visuals help decision-makers quickly identify key metrics, recognize trends and track performance. Reporting complements visualization by organizing and summarizing data into structured formats tailored to specific audiences.

Business intelligence types and tools

BI systems use different types of BI to fulfill different needs. These include:

Real-time business intelligence
Real-time business intelligence (RTBI) enables organizations to access, analyze and act on data as it’s generated, providing immediate insights into ongoing operations and market dynamics. While traditional BI often relies on periodic batch processing, RTBI analyzes data as it’s generated, ensuring that decisions are based on the most up-to-date information. This capability is critical in industries where timely responses are essential, such as finance, logistics and retail.

Embedded business intelligence
Embedded BI places BI capabilities directly into business applications or workflows, allowing users to access data insights within their day-to-day tools. This integration provides contextual analytics where decisions are made, enhancing efficiency and effectiveness.

Self-service business intelligence
Self-service business intelligence (SSBI) enables nontechnical users to access, analyze and visualize data without relying heavily on IT or data specialists. With user-friendly tools and intuitive interfaces, SSBI empowers employees to generate reports, create dashboards and explore datasets independently, democratizing data and streamlining data insight generation and response. Semantic layers are crucial for self-service BI, simplifying data access while maintaining governance.

Business intelligence tools
BI tools are crucial for the process of changing raw data into actionable insights. Some of the most common BI tools and software include:

• Data visualization tools represent datasets with easy-to-understand, interactive dashboards, graphs and charts
• 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
• Semantic layer tools represent complex data structures and concepts in a business-friendly format
• Data warehousing tools facilitate data storage and management
• Predictive analytics tools use statistical models and machine learning (ML) algorithms to create forecasts
• Operational BI tools provide real-time analytics to monitor day-to-day operations

BI tools are widely available from several vendors. Leading BI tools include Tableau, Power BI by Microsoft, Qlik, ThoughtSpot, Looker (Google Cloud Platform), Oracle Business Intelligence, SAP, SAS, Domo and Salesforce.

The business intelligence process

The business intelligence process takes data from its raw form and turns it into insights. Steps in this flow include:

1. Data identification: Data to be used for analysis is identified. Data may reside in a data warehouse, data lake or the cloud or come from business areas such as CRM, the supply chain, industry data, point of sale, inventory or marketing, for example.
2. Data collection: Data is gathered from various sources, cleaned, integrated and prepared for analysis.
3. Analysis: Data is analyzed to find trends, anomalies and patterns in the data.
4. Reporting and visualization: User-friendly reports and data visualizations such as dashboards, graphs and charts are created, enabling users to quickly understand data, drill down into details and identify important insights.
5. Decision and action: Stakeholders make decisions based on BI insights, implementing a plan to effect change or begin new initiatives.

Business intelligence use cases

Businesses in a wide range of fields use BI to help people make better decisions. Examples include:

• Customer insights: BI can provide a comprehensive view of customer behavior, preferences and feedback
• Customer service: AI helps improve customer service — for example, with chatbots that can quickly and accurately help customers, easing the burden on human agents and allowing them to focus on higher-value tasks
• Finance: Financial teams use BI for tracking expenses, analyzing profit margins and optimizing budgets. Real-time dashboards provide clear insights into financial health, identify risks and help in strategic planning.
• Healthcare: BI aids healthcare providers in personalizing care, improving patient outcomes, optimizing resource allocation and reducing operational costs
• Human resources: HR teams use BI to analyze trends in recruitment, employee performance and retention trends, aiding in workforce planning and engagement strategies
• Marketing: Marketers use BI to assess marketing campaign effectiveness by analyzing key performance indicators (KPIs) such as conversion rates, engagement rates and ROI
• Operational efficiency: BI tools help organizations track and optimize daily operations by analyzing process performance and identifying areas for improvement
• Retail: Retailers use BI to analyze purchasing behavior, optimize pricing strategies, manage inventory and optimize efficiency while reducing costs
• Risk management: BI helps identify and mitigate potential risks in areas including operations, compliance and financial activities
• Sales: BI tools analyze sales performance, customer behavior, pricing and market conditions and generate forecasts to predict future trends
• Supply chain: BI tools monitor supply chain activities to predict demand, identify bottlenecks and streamline inventory management to reduce costs and improve efficiency

Real-life BI applications
Leading companies are using BI to drive business in new directions. Examples include:

Barilla, the largest pasta producer in the world, implemented a traceability system using BI. The company analyzes supplier performance to stack rank suppliers by product quality and on-time delivery to assess supplier risk. Data teams can now easily monitor shipments overseas in near real time, predict demand and adjust production to improve inventory management.

SEGA Europe is using AI-enhanced BI to assist decision-makers by enabling them to ask ad hoc questions in real time about sales and player behavior without having to depend on data experts. Users can now get detailed insights about game sales and gameplay data by asking in natural language. This capability has increased productivity and makes data-driven decision-making faster throughout the organization.

The Canadian Broadcasting Corporation (CBC/Radio-Canada) extracts insights from vast amounts of disparate data to help the company better understand signals such as subscriber churn trends, content consumption and relationships between different types of content. With these BI insights, CBC can drive more engagement with personalization, adapting to deliver programming that will resonate better with listeners.

Compass, a real estate technology company, uses business intelligence to help real estate agents find homeowners who are most likely to sell their properties. Agents can determine when to increase or decrease marketing plans for specific listings from the data. These capabilities help Compass agents grow their business.

How AI is transforming business intelligence

AI is revolutionizing BI by automating complex tasks and democratizing access to data insights. AI-powered BI tools utilize ML algorithms to process data from multiple sources, identify patterns and extract actionable insights at unprecedented speeds. With the integration of natural language processing (NLP), these systems enable nontechnical users to interact with data through simple, conversational queries, eliminating the need for specialized expertise. This democratization fosters a data-driven culture across organizations, where employees at all levels can access and leverage BI tools for faster, more informed decision-making.

The advent of GenAI and custom large language models (LLMs) offers opportunities for deeper contextual understanding and more-precise insights tailored to unique business environments. These tools, combined with unified data platforms such as data lakehouses, consolidate information across silos, providing a comprehensive view of organizational data.

Moreover, AI learns from data ecosystems, resulting in more-intuitive BI systems that support self-service analytics and broader organizational engagement with data. By integrating AI into everyday workflows, BI systems are becoming indispensable tools for faster, more accurate decision-making, ultimately enhancing organizational agility and competitiveness in a rapidly evolving digital landscape.

Bringing AI to business intelligence with Databricks

Databricks AI/BI is a new type of business intelligence product built to democratize analytics and insights for organizations. Databricks AI/BI enables anyone to ask questions of data in natural language and receive highly relevant and trusted AI-generated insights. Databricks AI/BI moves beyond traditional BI systems with bolt-on AI assistants by learning an enterprise’s entire data estate, usage patterns and business semantics. This deep knowledge allows AI/BI to deliver accurate answers from complex, real-world data. 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.