AI Predictive Analytics: What It Is, How It Works, and Business Use Cases

AI predictive analytics uses machine learning to forecast outcomes more quickly and more accurately than traditional statistical methods. But here is the critical distinction from generative AI tools like ChatGPT: it predicts what will happen rather than creating new content. This article explains the core techniques behind predictive models, walks through implementation requirements, and covers use cases spanning marketing, manufacturing, transportation, and other industries.

Key takeaways

Here are the main points to keep in mind:

• AI predictive analytics uses machine learning algorithms to analyze historical data and forecast future outcomes with greater speed and accuracy than traditional statistical methods
• Core techniques include neural networks, regression models, decision trees, and clustering algorithms, each suited to different prediction types
• Unlike generative AI, which creates new content, predictive AI focuses on forecasting probabilities and outcomes based on patterns in existing data
• Practical applications span marketing, healthcare, retail, finance, manufacturing, transportation, and education, with measurable impacts on efficiency and decision-making
• Successful implementation requires governed, AI-ready data and clear alignment between predictions and business workflows

What is AI predictive analytics?

AI predictive analytics uses data, mathematical models, and statistical methods powered by artificial intelligence to forecast the likelihood of future outcomes. By examining historical data through AI and machine learning, organizations can anticipate what's likely to happen next and make decisions accordingly.

To accomplish this, predictive analytics relies on data science to analyze and extract insights from large quantities of historical data from a variety of sources such as databases, videos, images, logs, and more.

So what makes AI predictive analytics different from the standard version? Traditional predictive analytics leans on methods and techniques such as regression analysis, data mining, and modeling that require significant manual intervention. AI predictive analytics automates much of this process, using machine learning to identify patterns humans might miss and continuously improving its predictions as new data becomes available.

This predictive technology helps organizations anticipate customer behavior, prepare for demand, mitigate risks, and adapt quickly to market changes. What used to take countless hours of research, compilation, and analysis can now be done in seconds.

Differences between artificial intelligence and predictive analytics

Is predictive analytics the same as AI? Not exactly. Predictive analytics involves using statistical models and historical data to forecast future trends and behaviors. It is a subset of AI and one of AI's many applications. AI, on the other hand, uses machine learning models and computers to process language, recognize patterns, and make decisions.

While predictive analytics helps people make informed, data-driven decisions, AI can take it a step further by operating with greater autonomy, learning from new data, and adapting its approach over time. Traditional predictive analytics is a more manual process that requires human intervention and analysis at each stage.

Comparison table

The following table breaks down the key differences between AI and predictive analytics across several dimensions:

Generative AI vs predictive AI

With tools like ChatGPT becoming household names, a common question arises: what's the difference between generative AI and predictive AI?

Generative AI creates new content. It produces text, images, code, or other outputs that didn't exist before, drawing on patterns learned from training data to generate something original. ChatGPT, DALL-E, and similar tools fall into this category.

Predictive AI forecasts outcomes. Rather than creating something new, it analyzes historical data to estimate what is likely to happen next. Will this customer churn? When will this machine need maintenance? What will demand look like next quarter?

The distinction matters because the outputs serve different purposes. Generative AI helps you create; predictive AI helps you anticipate.

A simple decision framework: Is the system predicting a value, category, or event based on historical patterns? That's predictive AI. Is it generating new content that did not exist before? That's generative AI.

Large language models (LLMs) can assist predictive workflows by generating feature descriptions, summarizing model outputs, or creating narrative explanations of predictions. But they do not replace predictive models themselves. The LLM might help you communicate a churn score, but the churn score still comes from a predictive model trained on customer behavior data. Teams sometimes conflate LLM outputs with actual predictions, and that mistake can lead to decisions based on generated text rather than validated forecasts.

When to use predictive AI vs generative AI

Choosing between these approaches depends on what you're trying to accomplish.

Use predictive AI when you need to forecast outcomes, identify risks, or optimize decisions based on historical patterns. Examples include demand forecasting, fraud detection, predictive maintenance, and customer churn prediction.

Use generative AI when you need to create content, automate communication, or generate new ideas. Examples include drafting marketing copy, generating code, creating images, and building conversational interfaces.

Many organizations use both. A marketing team might use predictive AI to identify which customers are most likely to convert, then use generative AI to create personalized messages for those customers. The predictive model answers "who should we target?" while the generative model answers "what should we say to them?"

How predictive analytics and AI work together

Predictive analytics and AI work together in several ways, especially when it comes to using data. Gathering and analyzing data is essential for predicting future outcomes, and AI speeds up the process. It can analyze and understand large volumes of data to predict the probability of future events. Plus, it can bring in data from multiple sources and provide a complete picture of that information.

Another key application of AI is modeling, where it is used to build predictive models trained on historical data. These models can then be used to make predictions about future events, trends, or outcomes. AI-powered predictive analytics models can help provide essential insights to make decisions around those future events. The automation behind AI also streamlines operations and processes related to creating predictive models.

AI and predictive analytics work together to provide predictions and recommendations that inform action. Organizations can use these insights to power strategies and tactics used to enhance operations, convert customers, allocate resources, and more.

The predictive analytics process

Understanding how AI predictive analytics works in practice helps clarify what's involved in implementation. The process typically follows these stages:

1. Problem framing: Define the specific outcome you want to predict and the decision it will inform. What metric defines success? What action will you take based on the prediction?
2. Data collection: Gather historical data from relevant sources including databases, applications, Internet of Things (IoT) sensors, and external feeds. Identify the minimum viable dataset for your use case.
3. Data preparation: Clean, transform, and structure the data so it's ready for analysis, addressing missing values, outliers, and inconsistencies. Check for data leakage where future information might accidentally inform training.
4. Feature engineering: Identify and create the variables that will be most useful for making predictions. This step often determines model performance more than algorithm selection.
5. Model training: Apply machine learning algorithms to the prepared data, allowing the model to learn patterns and relationships. Split data into training and testing sets to avoid overfitting.
6. Model validation: Test the model against held-out data to assess accuracy and identify potential issues. Use metrics appropriate to your problem type (classification vs regression vs time series forecasting).
7. Deployment and monitoring: Integrate the model into business workflows where it can generate predictions on new data. Track model performance over time and retrain as needed when accuracy degrades or conditions change.

At each stage, decision checkpoints help ensure you're on track: Do you have enough labeled data? Is your target variable well-defined? Are you accounting for concept drift?

The role of data quality and governance

The accuracy of any prediction depends on the quality of the data feeding it. Garbage in, garbage out applies with particular force to AI predictive analytics.

Effective predictive analytics requires data that is accurate, complete, consistent, and timely. Organizations also need clear governance policies that define who can access data, how it should be used, and what controls ensure compliance with regulations.

This is where the foundation matters. Before organizations can activate AI for predictions, they need to make their data AI-ready through proper integration, quality management, and governance frameworks. Without this foundation, even sophisticated algorithms will produce unreliable results.

Common data pitfalls that undermine predictive accuracy include the following:

• Data leakage: Using information that would not be available at prediction time. For example, predicting customer churn using "days since last login" becomes problematic if customers who have already churned have infinite values for this field. This leaks the target variable into the features.
• Class imbalance: When one outcome vastly outnumbers others. If only 0.1 percent of transactions are fraudulent, a model that predicts "not fraud" for everything achieves 99.9 percent accuracy while detecting zero fraud. This example illustrates why accuracy alone can be misleading. You need metrics like precision, recall, and the F1 score, which balances precision and recall, to understand true model performance on imbalanced data. Solutions include resampling techniques, cost-sensitive learning, or anomaly detection approaches.
• Concept drift: When the patterns the model learned no longer reflect current conditions. A fraud model trained on 2020 data may not detect 2025 fraud patterns because tactics evolve. Monitor model performance and retrain regularly.
• Missing data: Handling gaps requires thoughtful approaches like imputation (filling with mean, median, or mode), indicator variables that flag missing fields, or model-based imputation. Avoid dropping rows unless missingness is truly random.

Addressing these issues requires ongoing monitoring, not just a one-time data cleanup.

Predictive analytics techniques and algorithms

AI predictive analytics draws on several algorithm types, each suited to different kinds of prediction problems.

Neural networks

Neural networks excel at recognizing complex patterns in data, particularly when relationships between variables are non-linear or difficult to specify in advance. They're structured in layers that process information progressively, with each layer extracting higher-level features from the data.

These algorithms power many advanced prediction applications, from image recognition to natural language processing to demand forecasting. They require substantial data to train effectively but can capture subtleties that simpler models miss. Neural networks are particularly well-suited for problems involving unstructured data like images, text, or sensor readings.

Regression models

Regression models estimate relationships between variables, making them useful when you want to understand how different factors influence an outcome. Linear regression predicts continuous values (like revenue or temperature), while logistic regression predicts categorical outcomes (like whether a customer will churn or not).

These models are interpretable. You can see exactly how each input variable affects the prediction. This transparency makes them valuable in regulated industries or situations where you need to explain why a prediction was made. For many business problems, a well-tuned regression model delivers accuracy comparable to more complex approaches while remaining easier to validate and maintain.

Decision trees and random forests

Decision trees make predictions by following a series of branching rules, similar to a flowchart. Intuitive to understand. Can handle both numerical and categorical data.

Random forests combine many decision trees, each trained on a slightly different subset of the data. By aggregating predictions across trees, random forests typically achieve higher accuracy than individual trees while reducing the risk of overfitting. These ensemble methods work well for classification problems like fraud detection or customer segmentation where you need both accuracy and some degree of interpretability.

Clustering algorithms

Clustering algorithms group similar data points together without being told in advance what the groups should be. K-means clustering, for example, partitions data into a specified number of clusters based on similarity.

These algorithms are particularly useful for customer segmentation, anomaly detection, and discovering natural groupings in data that can inform subsequent prediction efforts. Unlike supervised learning methods, clustering does not require labeled historical outcomes, making it valuable when you're exploring data or when labeled examples are scarce.

Choosing the right approach

Selecting an algorithm depends on several factors, not just the prediction type. The following considerations help match techniques to business problems:

• Prediction type: Are you predicting a category (classification), a numeric value (regression), when something will happen (time-to-event), or identifying unusual patterns (anomaly detection)?
• Interpretability requirements: Do stakeholders need to understand why a prediction was made, or is accuracy the primary concern? Regulated industries often require explainable models.
• Data volume: Neural networks typically need large datasets to perform well; simpler models can work effectively with less data.
• Latency constraints: Some models predict in milliseconds; others require more computation time. Real-time fraud detection has different requirements than monthly demand forecasting.
• Maintenance capacity: Complex models may require more ongoing tuning and monitoring than simpler alternatives.

A practical mapping of use cases to recommended approaches can guide initial decisions:

For most organizations starting with predictive analytics, beginning with interpretable models like regression or decision trees makes sense.

How to choose an AI predictive analytics tool

Selecting the right platform for AI predictive analytics involves evaluating several factors, not just algorithmic capabilities.

Key evaluation criteria

When assessing AI predictive analytics tools, consider the following criteria:

• Data connectivity: Can the platform connect to your existing data sources, including cloud data warehouses, databases, software as a service (SaaS) applications, and streaming data? The fewer integration barriers, the more quickly you can start generating predictions.
• Governance and security: Does the platform provide controls for data access, model management, and compliance? Predictions are only valuable if you can trust them and explain them when needed.
• Ease of use: Can business people work with the platform, or does every project require data science expertise? Tools that democratize access to predictive capabilities deliver value more broadly across the organization.
• Scalability: Will the platform handle your data volumes as they grow? Can it support multiple prediction use cases simultaneously without performance degradation?
• Integration with workflows: Can predictions flow directly into the systems where decisions get made, whether that's a customer relationship management (CRM) system, enterprise resource planning (ERP) system, or operational dashboard? Predictions that require manual export and import create friction that limits adoption.
• Model transparency: Can you understand why the model made a particular prediction? Explainability matters for building trust and meeting regulatory requirements.
• Total cost of ownership: In addition to licensing costs, consider implementation effort, training requirements, and ongoing maintenance. A platform that requires extensive professional services may cost more than it appears.

Build vs buy considerations

Organizations also face a fundamental choice between building custom predictive capabilities and adopting existing platforms.

Building in-house makes sense when you have unique data or domain requirements that off-the-shelf tools cannot address, when you have machine learning engineering talent available, or when you need full control and customization over every aspect of the model.

Buying or using a platform makes sense when you need fast time-to-value, when you lack deep machine learning expertise, or when your use case is relatively standard (churn prediction, demand forecasting, fraud detection).

Automated machine learning (AutoML) platforms can bridge the gap, offering automated model selection and tuning without requiring deep machine learning expertise. These work well when you have clean, structured data and need fast prototyping. Custom development remains necessary when you need cutting-edge techniques, have complex feature engineering needs, or require full model interpretability for compliance.

The most effective AI predictive analytics implementations connect predictions directly to action. A churn score that sits in a database helps no one.

Examples and use cases of AI predictive analytics

Just as AI predictive analytics provides many benefits, its application is suitable for a variety of industries. Any industry that can benefit from data-driven insights and information regarding the prediction of future outcomes and behaviors should explore using AI predictive analytics.

Marketing

AI predictive analytics can transform marketing in several ways. One of the top use cases applies to customer data, as marketers can pull insights and information from vast amounts of information regarding customer preferences, interests, behaviors, and patterns. Key applications include:

• Personalization: Marketing departments can create personalized product recommendations and tailored campaigns using historical data on purchases, engagements, and browsing habits.
• Behavior prediction: AI predictive analytics can be used to predict future buying habits and build related promotions and campaigns to drive more sales.
• Customer churn prediction: By using AI predictive modeling, businesses can project which customers are likely to churn or leave a business. Marketing teams can respond by creating targeted, personalized campaigns to reduce customer churn rates.
• Segmentation: Marketers can more effectively segment customers based on various factors, such as behaviors, demographics, and interests. Doing so allows companies to create hyper-personalized, targeted content for specific customer segments.
• Predictive Customer Lifetime Value (LTV): AI predictive analytics can be used to predict the value of a customer over their lifetime of engagement with a company taking into account past purchases, buying habits, engagement levels, and more. Marketers can then prioritize efforts for customers with the greatest potential LTV.

Healthcare

AI predictive analytics is revolutionizing healthcare by improving patient outcomes and enhancing decision-making processes. Several applications demonstrate its impact:

• Disease detection: AI predictive analytics can analyze patterns and anomalies that may indicate the presence of certain diseases and illnesses. This method is often more accurate than traditional detection methods and can support earlier intervention and improved patient outcomes.
• Readmission risk prediction: Similarly, these predictive models can read through and analyze pertinent data to identify patients who are at greater risk of hospital readmission. This allows providers to intervene as necessary and adjust the post-case plans for patients.
• Healthcare fraud detection: Patterns of healthcare fraud are often difficult to detect manually. AI predictive analytics can make all the difference by analyzing patterns to uncover anomalies. This ability helps providers and organizations reduce financial losses.

Retail

AI predictive analytics is highly applicable in the retail industry, particularly in how to manage inventory, supply chain, and pricing. Common applications include:

• Inventory management: Retail companies can use AI predictive analytics to analyze customer buying habits and market trends. Retailers can then make more informed decisions about purchasing and managing inventory.
• Supply chain management: AI predictive analytics can analyze historical data, market conditions, and external factors to predict potential changes in the supply chain. Retailers can use this information to mitigate risks and strategize accordingly.
• Dynamic pricing: Retailers can adjust prices based on inventory, demand, seasons, and competitive factors through the use of AI predictive analytics.

Finance

Financial services organizations use AI predictive analytics for risk management and fraud prevention:

• Fraud detection: Financial organizations can use AI predictive analytics to identify and analyze data for unusual patterns that could indicate fraud. These irregularities might include frequent returns or orders from multiple individuals at the same address. By uncovering these fraudulent activities, organizations can safeguard their profits and protect legitimate customers.
• Credit risk assessment and scoring: AI predictive analytics analyzes data to determine the credit risk of individuals and businesses. What makes this predictive technology a game-changer is its ability to consider non-traditional data, such as employment history, utility payments, and behavioral patterns.

Manufacturing

AI predictive analytics fits into manufacturing processes and operations by supporting predictive maintenance, quality control, and resource management:

• Predictive maintenance: Regularly maintaining appliances, tools, equipment, and machinery is essential in manufacturing. However, it can be difficult to determine when maintenance is required, which can lead to excess downtime. AI predictive analytics can tap into equipment data to determine when maintenance is needed and avoid failures.
• Quality control: Likewise, predictive analytics can monitor data patterns to identify quality issues. This information is then used to adjust processes to improve quality and avoid defective outputs.
• Resource control: As manufacturing companies improve quality and rely on predictive maintenance, they can optimize the allocation and usage of their resources to generate cost savings.
• Process improvement: Predictive analytics helps to optimize processes by uncovering areas for improvement and identifying bottlenecks.

Transportation and logistics

Organizations in the transportation and logistics industry lean on AI predictive analytics to optimize operations, lower costs, and reduce inefficiencies:

• Predictive maintenance: AI predictive analytics can determine when maintenance should be performed on machinery and equipment to minimize failure and downtime.
• Last-mile delivery: By analyzing conditions such as demand, traffic, or weather, transportation and logistics companies can optimize routes to get deliveries to customers more quickly while safeguarding resources.
• Transportation Management Systems: To plan for capacity more effectively, AI predictive analytics can forecast future demand. Companies can use this information to prepare personnel, fleets, routes, and other resources.

Education

AI predictive analytics is equally applicable in the educational sector, from elementary to higher education:

• Early intervention: Educators can identify at-risk students and plan for early intervention through AI predictive analytics.
• Personalized learning: AI predictive analytics can be used to personalize educational content and learning paths so educators can focus on the individual needs, preferences, and learning styles of students.
• Administrative planning: Administrators can use predictive technology to study trends and patterns in scheduling and enrollment.

Benefits of AI predictive analytics

AI-powered predictive analytics is a transformative technology that can enhance nearly every aspect of a business. By applying advanced algorithms and data-driven insights, organizations can find new opportunities, mitigate risks, and achieve measurable results.

Improved decision-making and risk mitigation

AI predictive analytics provides actionable insights by analyzing historical data to forecast future trends. Businesses can make informed, proactive decisions and develop strategies that align with anticipated outcomes.

By identifying potential issues or anomalies before they escalate, businesses can take preventative measures to minimize risks. Whether it's detecting fraud, predicting equipment failure, or managing market volatility, predictive analytics helps organizations stay one step ahead.

Operational efficiency and cost reduction

AI predictive analytics streamlines processes by identifying inefficiencies and automating repetitive tasks. It improves resource allocation, reduces waste, and helps operations run at peak performance levels.

With improved efficiency and more effective decision-making, businesses can reduce operational costs. Predicting demand and resource needs helps avoid overproduction or underutilization.

Enhanced customer experience

AI predictive analytics uses customer behavioral data to personalize interactions, anticipate needs, and improve overall satisfaction. More targeted marketing campaigns. Tailored product recommendations. A smoother customer journey.

Competitive advantage through data visibility

AI unifies data from multiple sources and formats, providing a comprehensive view of organizational performance. This holistic perspective enables cross-departmental collaboration and stronger decision-making at all levels.

Organizations that harness AI predictive analytics gain a significant edge in their industries.

Measuring business impact

Predictions alone don't create value. Action does. Understanding how to measure the business impact of AI predictive analytics helps justify investment and optimize ongoing performance.

Calculating ROI

A straightforward ROI framework for predictive analytics considers both the gains from better decisions and the costs of implementation:

ROI = (Gain from investment - Cost of investment) / Cost of investment

For predictive analytics specifically, the gain calculation often looks like this: (Cost of problem × Reduction rate) - False positive costs.

Consider a customer churn example. If your average customer lifetime value is $500, your annual churn rate is 10 percent (meaning 1,000 customers churn per year), and your retention campaign costs $50 per customer, a predictive model that identifies 60 percent of churners with 80 percent precision could generate significant returns. The math: (1,000 churners × 0.6 identified × 0.8 precision × 0.5 retention rate × $500 LTV) - (1,000 × 0.6 × $50 campaign cost) = $120,000 - $30,000 = $90,000 annual gain. This calculation shows why even modest improvements in prediction accuracy can translate to substantial financial returns.

Understanding the cost of errors

Different types of prediction errors carry different business costs. Optimizing for the right balance matters more than maximizing overall accuracy.

In fraud detection, a false negative (missed fraud) might cost $1,000 in losses, while a false positive (blocked legitimate transaction) might cost $10 in customer friction and support time. In this case, you'd set your decision threshold to catch more fraud even if it means more false alarms.

In medical screening, a false negative (missed disease) could be life-threatening, while a false positive (unnecessary follow-up test) is inconvenient but manageable. The threshold should favor sensitivity over specificity.

Understanding these tradeoffs helps you set decision thresholds based on business impact, not arbitrary probability cutoffs.

Connecting predictions to action

The most successful predictive analytics implementations define clear paths from prediction to action. For each use case, specify the following:

• What decision will be made based on the prediction?
• At what probability threshold will action be triggered?
• Who reviews edge cases before action is taken?
• How is feedback collected to improve the model over time?

High-stakes decisions (credit approval, medical diagnosis) typically require human review. Low-stakes, high-volume decisions (product recommendations, email send times) can often be fully automated. Medium-stakes decisions (fraud flagging) may use tiered automation: auto-approve low-risk, auto-decline high-risk, human review medium-risk.

Governance and compliance considerations

For enterprise organizations, especially those in regulated industries, governance is not optional. It is a prerequisite for deploying predictive analytics at scale.

Data privacy requirements

If your predictive model uses personal data (names, addresses, health records, financial information), compliance requirements apply. Under the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and similar regulations, organizations must obtain consent or establish legal basis for data use, implement data minimization (only collect what is necessary), provide data subject rights (access, deletion, portability), and encrypt data in transit and at rest.

Model risk management

Financial institutions face specific regulatory requirements for model governance. SR 11-7 in the US and similar frameworks elsewhere require organizations to document model purpose, methodology, and limitations; validate model performance on out-of-sample data; establish ongoing monitoring and revalidation schedules; define model governance (who approves changes?); and maintain audit trails of model versions and decisions.

Auditability checklist

For regulatory compliance and internal governance, maintain documentation of data lineage (where did training data come from?), feature definitions (how is each variable calculated?), model versioning (which model version made this prediction?), prediction logs (what was predicted, when, and why?), and human review records (for human-in-the-loop systems).

Bias and fairness

Test for disparate impact across protected groups. If your model has different error rates for different demographic groups, you may face discrimination claims and regulatory scrutiny. Fairness testing should be part of model validation, not an afterthought.

Explainability for compliance

GDPR Article 22 requires "meaningful information about the logic involved" in automated decisions. Use interpretable models (linear regression, decision trees) where possible, or explainability tools such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) to provide explanations for more complex models.

Model monitoring and maintenance

Predictive models are not set-and-forget systems. They degrade over time as conditions change, and organizations need strong model monitoring practices to maintain performance.

Detecting drift

Two types of drift can undermine model performance. Data drift occurs when the distribution of input features changes over time. If your model was trained on customers averaging age 35, but recent predictions are for customers averaging age 50, the model may no longer be valid. Concept drift occurs when the relationship between features and outcomes changes. Customer behaviors that predicted churn in 2023 may not predict churn in 2026.

Monitor for drift using statistical tests (Population Stability Index, Kolmogorov-Smirnov test) that compare current data distributions to training data distributions.

Retraining triggers

Establish clear criteria for when models need retraining. Common triggers include performance degradation (accuracy drops below threshold), significant data drift detected, business context changes (new products, new markets, new competitors), and scheduled revalidation (quarterly or annually depending on use case volatility).

Performance monitoring

Track model performance continuously, not just at deployment. Key metrics to monitor include prediction accuracy over time, feature importance stability, prediction distribution (are predictions clustering differently than expected?), and business outcome correlation (are high-probability predictions actually converting to outcomes?).

Getting started with AI predictive analytics

AI predictive analytics delivers the most value when predictions connect directly to business workflows and decisions. Starting with a clear use case, rather than a technology-first approach, helps ensure that predictive capabilities translate into measurable outcomes.

Organizations ready to explore AI predictive analytics should consider a few initial steps:

• Assess data readiness: Evaluate whether you have the historical data needed for your target predictions and whether that data is accessible, clean, and governed
• Start with a specific use case: Choose a prediction problem with clear business value and measurable outcomes, such as churn prediction or demand forecasting
• Align predictions with workflows: Plan how predictions will reach the people who need them and how those predictions will inform decisions or trigger actions
• Build governance from the start: Establish clear policies for model oversight, explainability, and human review before scaling predictive capabilities

The organizations seeing the greatest returns from AI predictive analytics are those that treat it not as a standalone technology project but as a capability woven into how the business operates. Find out how Domo can fit into your analytics strategy.