Business Intelligence Examples and Use Cases

Business intelligence gets described in a lot of different ways: dashboards, forecasting, customer analysis, operational reporting and so on. The problem with that framing is that it tends to mix together three different things — the business question, the analysis behind it and the interface someone uses to find an answer.

In practice, a better starting point is the use case. An organization is trying to monitor performance, improve an operation, understand customers, manage financial performance and risk, or give more people direct access to analysis. A dashboard, third-party BI tool or conversational interface are ways of putting the analysis in front of the person making the decision.

As BI interfaces expand, keeping the use case the focus makes a BI system easier to maintain. The same governed analytical data might feed a dashboard, support an ad hoc question or sit behind an AI-assisted BI interface. The way someone accesses the analysis may change, but the underlying use case determines which data is needed, how current it needs to be and what decision the result is meant to inform.

Organizations use business intelligence to connect data with decisions. Most BI use cases fit into five common categories.

Performance monitoring

Performance monitoring tracks the measures an organization uses to understand whether results are moving according to plan. Revenue, profit margin, inventory turns, customer churn, student attendance and service availability all fit this pattern, provided the metric informs a decision or prompts investigation.

This is often where a BI program starts. The underlying data frequently exists already, a KPI provides an agreed reference point and the reporting cadence is relatively predictable. Once teams trust those measures, they have a basis for investigating the conditions behind them.

Operational optimization

Operational BI focuses on a process: how long it takes, what it costs, where capacity is constrained or whether service levels are being met. A manufacturer might examine throughput and downtime, while a retailer tracks fulfillment delays or a customer service organization monitors emerging product issues.

Timing shapes the value of these use cases. An operational metric delivered after the relevant scheduling, routing or inventory decision has closed offers considerably less room for intervention, even when the calculation itself is accurate.

Customer intelligence

Customer intelligence brings together behavioral, transactional and other customer data to support decisions about acquisition, engagement, retention and service. Common questions include which segments are responding to a campaign, where customers are dropping out of a journey and which activities correlate with repeat purchases.

The analysis often crosses systems. Marketing engagement might sit in one source, purchases in another and customer service interactions somewhere else, so a useful customer view depends on consistent identities and definitions across those records.

Financial planning and risk

Forecasts, budget analysis, margin reporting, regulatory reporting and environmental, social and governance (ESG) analysis all use BI to examine financial performance or exposure. In these settings, the calculation itself needs a defensible definition: finance, risk and compliance teams need to know how a figure was derived and which data contributed to it.

The reporting cadence varies with the decision. A quarterly planning cycle has a different tolerance for data latency than an intraday risk question, even when both draw from the same underlying financial data.

These categories often overlap in a mature BI program. For example, a team might monitor a KPI first, investigate the operational or customer behavior behind a change, then use that analysis to adjust a forecast or resource plan.

See where analytics is headed: Hear Snowflake’s Head of Analytics explain how shared data and semantic context support BI across tools and interfaces:

The categories above provide a helpful way to organize BI use cases, but each real-word use case is defined by a specific decision, the data behind it and the outcome the organization is trying to change.

Across education, financial services, retail, insurance, pharmaceuticals and technology, Snowflake customers apply BI to very different problems, from calculating student performance metrics faster to reducing acquisition cost or improving access to investment data. The examples below show what those use cases look like in practice.

Performance monitoring: Massachusetts Executive Office of Education

The Massachusetts Executive Office of Education brings educational data into a centralized analytics environment where analysts prepare and validate data with SQL and Python, automate reporting workflows and publish student metrics through Power BI. Policymakers, school boards and other stakeholders use those reports to track measures such as enrollment, attendance, suspensions, graduation rates and student growth, then evaluate policies, scholarships, awards and educational spending against the results they produce.

One particularly compute-intensive measure, the student growth percentile, previously took more than five hours to calculate for the current year. The agency now calculates that metric along with six years of progress targets 93% faster. Its broader modernization also saves $1.5 million annually.

As Brenton Stewart, Coordinator of School Redesign and Impact at the Massachusetts Department of Elementary and Secondary Education, explains, “Now that our dashboards are automatically updated, we can spend more time analyzing trends and making comparative assessments.”

Operational optimization: Simba Sleep and Miro

Simba Sleep combines CRM, sales, marketing and product data in a centralized data lake, using Fivetran for ingestion, dbt Cloud for transformation and Power BI for reporting. Teams analyze that data for forecasting, customer sentiment and operational issues across products, delivery partners and service operations. One concrete workflow starts with customer support tickets: when complaints indicate a possible product problem, customer experience and product leads receive reporting that helps them identify the issue and respond.

With data processing times reduced by approximately 75% (as of March 2025), reports that previously required hours are now available in near real time. That shorter reporting cycle gives teams more time to investigate and address issues while they’re still affecting customers.

Miro uses BI for a different operational question: how people are actually using its collaboration product and where the experience needs adjustment. The company combines product events with financial, marketing and sales data in Snowflake, transforms the data with dbt and surfaces dashboards and reports through Looker. Product, design and engineering teams then analyze feature usage and user journeys across billions of events.

That analysis has informed specific product decisions. During the rollout of new AI features, usage data showed that many customers weren’t selecting the toggle required to access them. Miro’s product team used the finding to investigate the source of the friction and adjust the experience to increase adoption. While processing multiple petabytes of data, Miro also reports up to 99% uptime for internal data users.

Customer intelligence: Allergan Aesthetics

Allergan Aesthetics wanted to understand customers across brands and digital interactions rather than treating each encounter as an isolated record. Segment collects and standardizes customer activity, which Allergan brings into Snowflake to create a unified customer view. Its product engineering team uses that data to build machine learning models that predict relevant offers, products and content, while marketing teams use the resulting audiences and data science scores to target campaigns across channels.

That customer intelligence feeds directly into campaign decisions. Instead of relying on broad audience groups, teams iterate on active campaigns using customer profiles and model outputs, then measure whether those changes improve acquisition performance. Allergan reports that this approach reduced completed-purchase cost per acquisition for social advertising.

Financial planning and risk: FIS

FIS uses analytics to support regulatory compliance across capital markets. Its Compliance Suite brings together branch surveillance, communications surveillance, anti-money laundering and regulatory reporting workflows, giving financial institutions a centralized environment for reviewing the orders, routes and trades subject to regulatory oversight.

By consolidating three previously separate systems, FIS shortened the time required to process compliance workloads and reduced the infrastructure supporting them. Overall execution is 2.5x faster and the environment uses less than 20% of the previous compute. FIS also reports 33% savings in license, maintenance and overhead costs.

Extending BI through self-service: Indeed

Indeed uses self-service at a much larger scale. The company uses a 52-petabyte data lake as a source for reporting and analytics, converting data sets to Apache Iceberg tables so analysts can query them directly through Snowflake and other compatible tools. Instead of sending each request through data engineering, analysts develop their own analyses, examine user journeys and use the resulting findings to inform decisions affecting employers and job seekers.

Self-service also extends into specific business workflows. Indeed marketers use Snowflake with GrowthLoop to build customer audiences directly, reducing campaign deployment from a process that could take three months to as little as one day, while Snowflake Data Clean Rooms supports incrementality analysis that connects advertising exposure with outcomes such as job seeker profiles, employer accounts and applications. For employer-facing dashboards, Indeed moved analytical APIs to Snowflake and later adopted Interactive Analytics, reducing average query latency by 60%; depending on workload, its analysts have also recorded query cost savings of up to 74% (as of July 2025).

Industry still influences how BI gets applied, particularly through regulatory requirements, data sources and the amount of latency a decision tolerates.

Healthcare

Healthcare organizations use BI to track patient outcomes, analyze capacity and staffing, monitor costs and fulfill regulatory reporting requirements. A hospital examining bed utilization is performing the same basic performance-monitoring job as an organization tracking product availability, although patient privacy requirements place tighter controls around which records people see and how data is governed.

Retail and consumer goods

Inventory turns, demand forecasts, promotion performance and customer segmentation place BI close to day-to-day retail decisions. Data often spans ecommerce, point-of-sale, inventory, loyalty and marketing systems, making a consistent view of products and customers particularly useful when teams compare performance across channels.

Financial services

Financial institutions apply BI to risk analysis, regulatory and ESG reporting, portfolio performance, margins and operating costs. Auditability carries particular weight: when a figure informs a regulatory submission or investment decision, teams need a shared definition and a traceable relationship to its underlying data.

Manufacturing and logistics

Throughput, quality, downtime and supply chain visibility are common BI use cases in manufacturing and logistics. Increasingly, those analyses combine machine or sensor data with orders, inventory, staffing and financial information, giving operational teams a broader view of what contributed to a delay or change in output.

Marketing

Marketing teams use BI for campaign performance, attribution, customer segmentation and spend efficiency. Because sales, finance and marketing systems often represent revenue, conversions or customers differently, shared definitions help keep campaign reporting aligned with the business measures used elsewhere in the organization.

Across each industry, the same analytical patterns recur. For example, a retailer watching stock turns and a hospital monitoring bed utilization are both tracking capacity against demand; their data sources, privacy requirements and acceptable refresh intervals determine how the use case is implemented.

A well-defined use case depends on the analytical foundation underneath it. When the data, business definitions or delivery cadence don’t line up with the decision, teams spend more time reconciling the analysis or waiting for it than using it.

• Relevant data remains split across systems: A customer, financial or operational question often draws from several sources. If those sources aren’t brought together in a consistent way, teams work from partial views or must spend time reconciling extracts before they can answer the question.
• Business logic lives inside individual reports and tools: When revenue, active customers or another shared metric is defined differently in multiple dashboards, the same question produces different results depending on where someone asks it. Teams need a way to govern definitions of metrics and dimensions so they can be shared across BI tools and other analytical interfaces, keeping the business logic close to the data.
• The data or query response misses the decision window: Quarterly planning and an operational response have very different timing requirements. If data refreshes overnight when a team needs to respond during the day, or queries slow as more people use the same analytical environment, answers reach the user late, leaving them little room to act.
• Access expands without consistent governance: Self-service gives more people a direct path to analysis, which also increases the number of users, tools and interfaces touching the underlying data. Applying governance and access controls at the data layer helps preserve the same permissions as analytical access expands.
• The use case is coupled too tightly to one interface: A metric defined only inside a dashboard has to be recreated when the organization wants to use it in another BI tool, an ad hoc SQL workflow or an AI-assisted experience. Keeping shared metrics and business context in the data layer gives those interfaces a common analytical foundation.

Snowflake gives teams a shared foundation for BI use cases across dashboards, third-party BI tools and AI-assisted analytical experiences. Data and business context remain governed in Snowflake while teams choose the interface that fits a particular workflow.

Snowflake Horizon Context provides a governed context layer for AI, BI and applications, bringing business definitions and relationships closer to the underlying data. Within that layer, Semantic Views define metrics, dimensions and relationships once so the same business logic can be reused across analytical tools rather than recreated inside individual dashboards. Snowflake has also expanded interoperability so those definitions can flow into BI platforms such as Tableau, Sigma, Hex and soon Power BI (in private preview).

For conversational and agentic analytics, Cortex Agents use that semantic context to work with structured data, while Snowflake CoWork gives business users an interface for asking questions, exploring enterprise data and acting on the results. The interface can therefore change without requiring teams to redefine the underlying business question or its metrics.

A strong BI program starts with a decision: what an organization needs to know, how quickly it needs the answer and what someone will do differently once the information is available. The interface follows from the requirements of the use case.

Across the customer examples above, dashboards, self-service analytics and other analytical experiences vary considerably. The underlying foundation stays consistent. Organizations monitor results, improve operations, understand customers, manage financial performance and risk, then extend those capabilities to more people as shared definitions and governed data provide a common foundation.