17 Business Intelligence Examples Across Industries

Business intelligence quickly becomes abstract. This article makes it concrete through 17 documented examples in the sectors of retail, manufacturing, healthcare, logistics, finance, and many others, as well as an overview of the platforms that underpin them. The variable that shapes each choice here: knowing whether your bottleneck is at the level of data integration, or of the distribution of actionable information to the people who take action.

Key takeaways

Here are the main points to keep in mind:

• Business intelligence transforms scattered data into actionable decisions, with proven results like European Wax Center which reduced its reporting time by more than 90 %
• The 5 steps of BI (data sourcing, analysis, situational awareness, risk assessment, decision support) offer teams a framework to move from information to measurable results
• Implementations according to sectors differ considerably, ranging from monitoring the temperature of the cold chain by DHL to Utah Jazz which tracks player performance and ticketing revenue on a single platform
• Modern BI platforms combine dashboards with predictive analytics and AI to provide actionable insights directly in the tools that users already open every day
• A successful launch depends less on the platform's features than on choosing a high-value use case and on the willingness to invest fully in it

Scope and neutrality note

The 17 examples presented here reflect an editorial choice and not an exhaustive coverage of the capabilities of BI. They are presented in no particular order and are not the subject of any ranking among themselves. Some are drawn from published customer testimonials, others from widely documented implementations. Domo appears among the platforms addressed because Domo publishes this article, and several of the examples cited are customers of Domo. The results, tools, and communicated metrics evolve over time, so consider these figures as proof of what has been accomplished rather than as a guarantee of the results that you will obtain.

What is business intelligence?

Business intelligence (BI) describes the practice consisting of gathering data, storing it in a reliable place, analyzing it, and presenting the results to the people who must make decisions. Spreadsheets and static PDFs stop at description. BI tools go further, by producing interactive dashboards, forecasts, and alerts that prompt action.

What falls under BI has expanded considerably. Platforms now connect to hundreds of systems, integrate AI capabilities, and transmit results into the applications where the work is actually carried out, which constitutes the foundation of true data-driven decision-making. Ask a question in natural language, get a recommendation in return, and act accordingly that same afternoon. It is an experience very different from waiting for a monthly report.

{{custom-cta-1}}

The 4 pillars of business intelligence

Four pillars support every BI system worthy of trust, and each of them answers a distinct question.

• Data collection: Databases, applications, Internet of Things (IoT) devices, and third-party platforms all hold pieces of the whole. Collection consists of finding where the data resides and integrating it into one place.
• Data storage: Data warehouses and cloud platforms keep data accessible, secure, and governed. Strong data governance involves quality controls, access controls, and audit trails operating continuously. Teams that treat storage as a one-time setup accumulate data debt that quietly corrupts everything downstream.
• Data analysis : Statistical methods, machine learning, and analytical techniques bring out the patterns, trends, and anomalies hidden in the numbers. This is where one finds the answer to the question “Why did sales drop in Q3?”
• Data visualization and reporting: Dashboards, charts, and reports make readable what is complex. The best among them answer the stakeholder’s question immediately and allow the curious to dig deeper.

5 stages of business intelligence

In practice, BI goes through five stages, each feeding the next in a loop that repeats continuously.

1. Data sourcing: Finding and connecting the systems that contain relevant information, whether these are customer relationship management (CRM) platforms, financial software, or IoT sensors. Across 900+ franchised locations, European Wax Center interconnected Salesforce, Google Sheets, and its point-of-sale (POS) software to build a single foundation.
2. Data analysis: The cleaned data is paired with statistical methods and algorithms, and trends emerge. What happened, and why? Traeger Grills comes in at this stage when reviewing sales targets, gross margins, and stock availability across different departments.
3. Situation awareness: Teams must know the current state of the company before analysis has meaning. Dashboards and live monitoring present key performance indicators (KPIs), operational metrics, and trends. DHL's cold chain dashboards fit into this stage, displaying container temperatures and the compliance status of global shipments in transit.
4. Risk assessment: Knowing where things stand makes it possible to spot what could go wrong afterward. Predictive models enable healthcare teams to flag patients likely to be readmitted, in order to enable early intervention. A warning to take seriously: treating a model's results as a certainty, without taking confidence intervals into account, leads teams to act on the basis of predictions that they should have questioned.
5. Decision support: Recommendations reach the people who make the decisions. Evidence replaces instinct, and choices are made more quickly and with more conviction. The Utah Jazz rely on this stage for ticket pricing and fan loyalty actions based on engagement data.

How BI works by industry

Business intelligence does not present itself in the same way for every organization. That is the whole point. The right BI tools adapt to the unique challenges, data sources, and regulatory requirements of your industry sector.

• Retail: Inventory optimization, insights on regional sales, tracking of customer retention. Typical data sources include point-of-sale (POS) systems, e-commerce platforms, and loyalty program databases. Key KPIs: inventory turnover rate, same-store sales growth, customer lifetime value.
• Manufacturing industry: Supply chain visibility, production efficiency, quality assurance. Data comes from enterprise resource planning (ERP) systems, IoT sensors on production lines, and supplier portals. Key KPIs: overall equipment effectiveness (OEE), defect rate, percentage of on-time deliveries.
• Logistics: Real-time monitoring, compliance tracking, route optimization. Data sources include GPS tracking, warehouse management systems, and IoT temperature sensors. Key KPIs: on-time delivery rate, cost per mile, shipment exception rate. Regulatory considerations include chain-of-custody documentation and cold chain compliance.
• Telecommunications: Network health analysis, outage detection, service quality forecasting. Data streams come from network monitoring tools, customer service platforms, and billing systems. Key KPIs: mean time to repair (MTTR), network uptime percentage, customer churn rate.
• E-commerce: Conversion tracking, personalized marketing, churn reduction. Data sources include web analytics, marketing automation platforms, and customer data platforms. Key KPIs: cart abandonment rate, customer acquisition cost (CAC), average order value.
• Healthcare: Forecasting of patient outcomes, resource planning, reduction of readmissions. Data comes from electronic health records (EHR), claims data, and bed management systems. Key KPIs: 30-day readmission rate, average length of stay, bed occupancy rate. Regulatory considerations include compliance with the Health Insurance Portability and Accountability Act (HIPAA) and governance of patient data.
• Financial services: Fraud detection, risk modeling, customer segmentation. Data sources include transaction systems, credit bureaus, and market data feeds. Key KPIs: fraud detection rate, false positive rate, days sales outstanding (DSO). Regulatory considerations include compliance with the Sarbanes-Oxley Act (SOX) and the requirements of the Payment Card Industry Data Security Standard (PCI-DSS).
• Hospitality: Sentiment analysis, insights on staff performance, customer satisfaction indicators. Data comes from property management systems, review platforms, and customer surveys. Key KPIs: RevPAR (revenue per available room), customer satisfaction score, repeat booking rate.
• Nonprofit organizations: Donor engagement, impact reporting, program efficiency. Data sources include CRM systems, volunteer tracking tools, and fundraising platforms. Key KPIs: donor retention rate, cost per dollar raised, program outcome indicators.
• Agriculture: Insights on crop performance, distribution logistics, alignment of business units. Data comes from field sensors, weather systems, and supply chain platforms. Key KPIs: yield per acre, input cost per unit, distribution efficiency.

Top business intelligence tools in 2026

The choice of platform depends on your constraints. Teams drowned under disconnected source systems attach great importance to the breadth of integration. Teams already having a clean data warehouse care more about modeling, governance, and the reach of information beyond the analyst's screen. The tools below all appear regularly in analysts' research and benefit from a significant presence on evaluation platforms.

This list is not exhaustive and the tools are not ranked. The selection reflects an editorial judgment based on market presence, analyst coverage, and ratings on evaluation platforms. As platform capabilities evolve, please verify current features during your evaluation.

Tool comparison table

Domo is situated in the integration and distribution category: it connects operational systems, applies governed AI on top, and sends the results into the workflows that users already use, rather than asking them to consult a reporting tool. Tableau and Qlik Sense reward teams that want to explore visually and that already have reliable upstream data. Looker and Sigma assume the existence of a data warehouse and build governed modeling on top. ThoughtSpot specifically targets the questioning issue. SAP Analytics Cloud and MicroStrategy suit established enterprise reporting estates with dedicated administrators, while Metabase and Power BI are frequently the starting points for smaller teams or teams standardized on Microsoft.

Evaluation criteria for BI tools

Six questions tend to distinguish platforms during evaluation more reliably than feature lists do.

Deployment flexibility is the criterion that buyers most often underestimate. A dashboard that no one opens produces nothing, and the question of whether information reaches users within their existing tools determines the success of more implementations than the quality of visualization ever will.

{{custom-cta-2}}

17 business intelligence examples across industries

Here is how different sectors use business intelligence to improve their operations and obtain measurable results. Each example follows a consistent model: the business question that must be answered, the data sources and the BI artifacts created, the decisions enabled, and the KPIs impacted.

Retail and e-commerce examples

European Wax Center turned to Domo to consolidate data from more than 900 franchises. Before Domo, the management team relied on spreadsheets and manual reports, which delayed the obtaining of insights and limited visibility into performance at the storelevel.

• Business question: How can franchisees and company management obtain real-time visibility into store performance without waiting for monthly reports?
• Data sources: Salesforce, Google Sheets, point-of-sale (POS) software integrated into a single platform
• BI artifact: Real-time dashboards displaying appointment volume, retail product sales, and customer retention by region
• Decision enabled: Franchisees can identify underperforming locations and adjust staffing or promotions in a few days rather than in a few weeks
• Impacted KPIs: Reporting time reduced by more than 90 %; visibility into the customer retention rate by location
• Implementation: Deployed in 8 weeks with connectors to existing systems

As the company's vice president of Business Intelligence stated: “Domo allows the team to see exactly what is happening across the entire network, without having to wait for static reports.”

A large meal delivery service uses BI to centralize and automate digital marketing reporting. Before BI, extracting indicators from various advertising platforms, CRM, and e-commerce systems could take hours. Sometimes days.

• Business question: Which marketing campaigns generate the highest customer lifetime value, and where should the team reallocate spending?
• Data sources: Application programming interfaces (APIs) of advertising platforms, CRM data, e-commerce transaction histories
• BI artifact: Campaign performance dashboards with real-time ROI calculations and breakdowns by customer segment
• Decision enabled: Marketing teams can carry out A/B tests on offers, target high-value segments, and pause underperforming campaigns in a few hours
• Impacted KPIs: Customer acquisition cost (CAC), campaign ROI, churn rate by acquisition channel

An outdoor goods retailer had more than 90 terabytes of data to better understand the members of its cooperative. The company wanted to personalize experiences and optimize its product assortment in its physical and online stores.

• Business question: How do regional preferences differ, and how should teams adjust merchandising and inventory according to locations?
• Data sources: Customer purchase histories, product affinity data, regional demographic information
• BI artifact: Regional preference dashboards showing brand and category preferences by geographic area
• Decision enabled: Merchandising teams discovered that members in the Pacific Northwest preferred different brands and categories from those of members in the Southwest, which influenced local inventory and event planning
• Impacted KPIs: Inventory turnover rate, customer satisfaction scores, regional sales growth

Amazon uses BI to personalize recommendations, optimize pricing strategies, and manage its complex supply chain.

• Business question: How can the company anticipate customer needs and maximize operational efficiency across millions of stock keeping units (SKUs)?
• Data sources: Customer behavior data, inventory levels, supplier performance indicators, market pricing data
• BI artifact: Predictive models for demand forecasting, dynamic pricing algorithms, supply chain optimization dashboards
• Decision enabled: Launch new products based on forecast demand, identify top-performing categories, create consistent customer journeys
• Impacted KPIs: Conversion rate, inventory carrying cost, delivery time

Starbucks tracks data from its loyalty program and mobile app to analyze millions of transactions each week.

• Business question: How can the company personalize marketing at scale and optimize store location decisions?
• Data sources: Loyalty program transactions, mobile app engagement, neighborhood spending data, foot traffic patterns
• BI artifact: Customer segmentation models, store location analysis dashboards, personalized offer engines
• Decision enabled: Hyper-personalized marketing campaigns, optimized inventory planning, data-based new store location decisions
• Impacted KPIs: Customer retention rate, average transaction value, new store performance

Manufacturing and supply chain examples

Traeger Grills adopted Domo to bring real-time data visibility to every department, from finance to field sales. Before Domo, disconnected systems and manual processes created silos that slowed the company.

• Business question: How can everyone, from the CEO to field representatives, access the same numbers at the same time?
• Data sources: Salesforce, NetSuite, inventory management systems
• BI artifact: Centralized dashboards for sales targets, gross margins, and inventory availability; accessible on mobile for field teams
• Decision enabled: The finance team accelerated the month-end close; sales representatives manage the performance of their territory in real time from mobile devices
• Impacted KPIs: Closing timeline reduced by 50 %; visibility into sales target attainment by territory
• Implementation: Integration of 3 main systems with mobile deployment for field teams

As the director of Business Intelligence emphasized: “From now on, everyone, from the CEO to the representatives, can see the same numbers, on the same platform, at the same time.”

DHL relies on Domo to ensure the integrity of temperature-sensitive pharmaceutical shipments. Tens of thousands of data points flow in from IoT sensors across global shipments. The company needed a way to visualize and act on these data in real time.

• Business question: How can the company ensure that each pharmaceutical shipment remains compliant and detect anomalies before they become problems?
• Data sources: IoT temperature sensors, GPS tracking, shipment route data, compliance thresholds
• BI artifact: Real-time dashboards tracking container temperature, shipment routes, and compliance status across the cold chain network
• Decision enabled: Operations teams can investigate shipment anomalies proactively rather than reactively
• Impacted KPIs: Anomaly investigation time reduced by 80 % (teams can address potential compliance issues before they escalate into costly product losses or regulatory violations); visibility into the compliance rate across the global network

“Domo helps the team ensure that every shipment remains compliant,” said a senior DHL executive. “It is the difference between reaction and prevention.”

Arysta LifeScience improved cross-functional decision-making by integrating data from its different business units through BI tools.

• Business question: How can teams align performance indicators, production data, and distribution timelines across business units?
• Data sources: Production systems, distribution logistics, business unit performance data
• BI artifact: Centralized dashboards giving management visibility into cross-functional indicators
• Decision enabled: Data-based strategic decisions with alignment across previously siloed business units
• Impacted KPIs: Decision cycle time, cross-functional alignment indicators

Financial services and fraud detection examples

Many financial institutions use BI to detect anomalies and prevent fraud. In a rapidly changing sector, early detection is paramount.

• Business question: How can analysts identify fraudulent transactions in real time while minimizing false positives that frustrate legitimate customers?
• Data sources: Transactional data, customer profiles, histories of fraud patterns, behavioral signals
• BI artifact: Real-time alert dashboards, machine learning models for anomaly detection, investigation workflow tools
• Decision enabled: Analysts receive prioritized alerts for suspicious activities, which allows rapid investigation and response
• Impacted KPIs: Fraud detection rate (percentage of fraudulent transactions intercepted), false positive rate (legitimate transactions incorrectly flagged), average investigation time

Banks using BI in this way have reported significant reductions in losses related to fraud while improving the customer experience through reducing false declines. Adjusting detection thresholds too aggressively with the aim of intercepting more fraud often backfires on them, overwhelming analysts with false positives and frustrating legitimate customers.

American Express uses real-time BI tools combined with machine learning to detect fraud across millions of cardholder accounts.

• Business question: How can American Express protect consumers against fraud while maintaining a smooth transaction experience?
• Data sources: Behavioral data, transaction patterns, device information, location data
• BI artifact: Real-time fraud scoring models, automated alert systems, investigation dashboards
• Decision enabled: The instant flagging of unusual patterns allows a rapid response without disrupting legitimate transactions
• Impacted KPIs: Fraud loss rate, customer protection indicators, transaction approval rate

Healthcare and education examples

Healthcare providers use BI to track patient data and hospital performance indicators. With tight budgets and high stakes, every insight counts.

• Business question: Which patients present the highest readmission risk, and how can care teams intervene proactively?
• Data sources: Electronic health records (EHR), claims data, patient demographics, histories of readmission patterns
• BI artifact: Predictive risk models, readmission dashboards, physician utilization reports
• Decision enabled: Care teams can prioritize high-risk patients for follow-up care and discharge planning
• Impacted KPIs: 30-day readmission rate (one hospital group observed a 20 % reduction, translating into better outcomes for patients and significant savings under value-based care models), average length of stay, physician utilization rate

A large university needed a way to centralize and share data across departments to support strategic planning and improve the student experience. With Domo, they created dashboards that gave management an overview of student enrollment trends, course fill rates, and faculty workload.

• Business question: How can the university optimize recruitment campaigns and course offerings based on enrollment patterns?
• Data sources: Student enrollment data, course registration systems, faculty schedules, tuition revenue
• BI artifact: Executive dashboards showing enrollment trends, course fill rates, and faculty workload; tracking of admissions campaign performance
• Decision enabled: The admissions team optimized recruitment campaigns; the finance office tracked tuition revenue in real time
• Impacted KPIs: Forecast accuracy for semester enrollments improved by 30 %

“Domo empowers management through information that is both comprehensive and easy to absorb,” shared a university spokesperson.

Hospitality, sports, and nonprofits examples

Hotel chains use BI to analyze feedback from surveys, online reviews, and social media. In a service-driven sector, sentiment data is worth gold.

• Business question: Where are the service gaps across different properties, and how can teams remedy them systematically?
• Data sources: Customer surveys, online review platforms, social media mentions, operational data
• BI artifact: Sentiment analysis dashboards, property comparison reports, service gap identification tools
• Decision enabled: One brand discovered that customers consistently gave a lower rating to breakfast in certain properties, which led to targeted menu and staffing updates
• Impacted KPIs: Customer satisfaction scores, service consistency across properties, repeat booking rate

Organizations like United Way use Domo to unify data coming from dozens of sources (including CRM systems, volunteer tracking tools, and fundraising platforms) into a single source of truth.

• Business question: How can the organization demonstrate its impact to donors and board members while optimizing program efficiency?
• Data sources: CRM systems, volunteer tracking tools, fundraising platforms, program outcome data
• BI artifact: Impact dashboards showing donations by region, program participation rates, and outcomes for the community; transparency reports intended for donors
• Decision enabled: Replacement of monthly reporting cycles with real-time data access; sharing impact dashboards directly with donors and board members
• Impacted KPIs: Donor engagement, donor retention rate, program efficiency indicators

“Domo fundamentally changed the way the organization communicates about its impact,” noted a program manager.

The Utah Jazz uses Domo to bring together performance data, fan engagement indicators, and revenue operations on a single platform.

• Business question: How can the team optimize both player performance and business operations using the same data platform?
• Data sources: Player performance data (minutes played, shooting efficiency, recovery time), ticket sales, merchandising, sponsorship data, fan engagement indicators
• BI artifact: Player performance dashboards for coaches and analysts; business operations dashboards for ticket pricing and fan retention
• Decision enabled: Coaches guide training decisions based on player data; business teams optimize pricing strategies and improve fan retention
• Impacted KPIs: Player performance indicators, ticketing revenue, fan retention rate

“Domo makes it easier to distribute performance information across the entire organization,” said an executive. “It has become an integral part of the team's strategy, on the court as well as off it.”

Telecommunications and technology examples

Many telecommunications companies use BI to track network performance across millions of customers. Downtime, slow speeds, or signal problems can quickly lead to customer churn.

• Business question: How can teams identify and resolve network problems before they impact the customer experience?
• Data sources: Device telemetry, network sensors, customer feedback, bandwidth usage data
• BI artifact: Real-time network status dashboards, outage detection alerts, repair prioritization tools
• Decision enabled: Engineers identify outages, monitor bandwidth usage, and prioritize repairs according to customer impact
• Impacted KPIs: Mean time to repair (MTTR) reduced by 35 % at a major provider, a reduction that is directly correlated with a decrease in customer complaints and a drop in churn during service interruptions; network uptime percentage, customer churn rate

Synerise created an AI-powered BI ecosystem that unifies customer data across websites, applications, and marketing channels.

• Business question: How can companies personalize customer journeys and predict future behaviors across all touchpoints?
• Data sources: Website behavior, app engagement, interactions across marketing channels, purchase history
• BI artifact: Customer journey visualization, predictive behavior models, intelligent segmentation tools
• Decision enabled: Companies personalize communications, optimize customer journeys, and automate engagement according to predicted behaviors
• Impacted KPIs: Conversion rate, customer loyalty indicators, marketing efficiency

BI for small and midsize businesses

The IT budgets of large companies are not a prerequisite for BI. Many small and midsize businesses already use it, whether it is a local retailer that observes sales trends across three sites or a regional service provider that adjusts its staffing according to seasonal demand. Self-service tools have reached such a level that a dedicated data team is now optional.

Returns on investment are often rapid. Automated reporting, regular KPI tracking and visibility into operational bottlenecks allow small organizations to react to market fluctuations by relying on facts rather than on assumptions.SMBs commonly report time savings of 15-20 percent in reporting from the first quarter of adoption.

What distinguishes those that succeed? They choose a high-impact use case and resist the urge for an immediate deployment across the scale of the entire company. Sales and inventory dashboards for a restaurant group. Project profitability for a professional services firm. The added value becomes obvious, employees notice it, and adoption spreads by itself.

How to get started with BI

Did these stories inspire you? A few practical steps will allow your organization to get started in business intelligence:

1. Start with a clear use case. Choose a business problem for which better data would change the game, such as sales forecasting, marketing attribution, or resource planning. Wanting to do everything at once is the best way to make a BI project fail. Allow one to two weeks to interview stakeholders in order to find the opportunity to prioritize.
2. Choose the right platform. Real-time data, scalability, and dashboards that users can actually use make up the foundation. Mobile access, connectors to the systems you already use, and a provider that answers the phone are additional advantages.
3. Obtain buy-in from the entire company. Adoption is the key to BI success or failure. Budget for integration, training, and tools accessible enough so that everyone, and not only your analysts, can explore the data and act according to their discoveries.
4. Build a data-driven culture. Quick wins deserve to be dwelled on, curiosity must be encouraged, and data has its place in daily decisions. Make exploration of the numbers easy, even fun.
5. Evaluate and iterate often. Your BI needs will evolve at the same time as your business. Plan regular check-ins to evaluate what works, refresh outdated dashboards, and integrate new data sources.

Building a data-driven culture

Buying a platform is not enough to make an organization data-driven. It is the data culture that accomplishes this work. Implementations that endure tend to share three characteristics: leaders who visibly make evidence-based decisions, teams that feel free to ask questions and dig into the numbers, and processes that make data accessible instead of restricting access to it.

Find ambassadors in each department and let them highlight the first successes. Share these stories. And choose tools that integrate where users already are, whether through analytics embedded in the applications they use, mobile dashboards for people in the field, or AI assistants that accept questions in natural language.

BI in the AI era

A trend is confirmed in each example above: BI gives organizations reasons for confidence. Retail, logistics, finance, healthcare, no matter. Current data allow you to detect problems while they are still minor.

Dashboards are no longer the end goal. AI agents, embedded analytics, and automated workflows bring actionable information directly where employees already work, instead of relying on separate reporting tools that one must think to consult. Natural language queries put data within reach of anyone who knows how to enter a question. Governed AI keeps automation within defined limits, humans setting the goals and constraints while machines handle execution and coordination. The number of charts has never been the goal. Transformed workflows and achieved results are, and organizations that act according to this distinction decide faster and serve their customers better.

Editorial methodology

We selected these 17 examples by favoring implementations involving named organizations, documented data sources, and communicated results, rather than generic illustrations of what BI could theoretically accomplish. Published customer testimonials, presentations at conferences, and widely relayed implementations constituted our source base. We also balanced this set across different sectors and company sizes so that readers from regulated sectors, retail, and nonprofit organizations all find relevant elements in it. When a specific metric appears, it reflects what the organization communicated rather than our own estimate. The tools appearing in the comparison table were chosen according to their coverage by analysts, their presence on evaluation platforms, and their observable adoption in companies.

Do you wish to discover what BI could bring to your company? Start a free trial and discover how Domo helps organizations transform their data into actions.