See why teams choose Pecan.ai over Alteryx and Qlik Predict
Alteryx and Qlik Predict extend analytics platforms with machine learning capabilities, helping technical users prepare data and build predictive models alongside reporting and analytics workflows. Pecan is purpose-built for predictive AI. Powered by the Pecan Agent, it turns business questions directly into production-ready predictions without requiring users to manually engineer features, build training datasets, or manage machine learning workflows.
Approach
How each solution is fundamentally designed to work.
Turns business questions into predictions and actions, powered by the Pecan Agent
Analytics automation platform with data preparation, workflow automation, and AutoML capabilities.
Business intelligence platform with predictive capabilities integrated into Qlik Analytics.
Who it’s for
Who can successfully use the product.
Data analysts and business teams
Data analysts, analytics engineers, and technical business users.
BI analysts, business analysts, and Qlik users extending existing dashboards with predictions.
Framing the Business Question
Predictive use cases aren’t as simple as they sound. Poorly defined use cases can lead to misleading predictions
Guided by the Pecan Agent to define the right predictive goals
Assumes users have already translated the business question into a predictive modeling problem.
Assumes the prediction problem has already been defined by the user.
Picking the Right Data
Selecting the correct data is critical because your predictions rely on it.
Automatically identifies and prepares the right data from raw sources
Users select, clean, transform, and prepare datasets through visual workflows.
Users prepare and model data within Qlik before building predictive models.
Creating the Training Set
A good training set isn’t just about combining tables, it’s where most ML work actually happens, and it’s easy to get wrong.
Start with raw data, no training dataset needed. The Pecan Agent builds a complete training set based on the predictive question, including all required aggregations and feature engineering
Requires users to prepare datasets, define targets, and structure training data before AutoML begins.
Requires users to prepare and structure training datasets before modeling.
Enhancing the training set
Simply using raw data isn't enough. Additional insights significantly improve predictive accuracy.
Pecan automatically extracts behavioral patterns and key insights from the historical data, improving model's accuracy
Supports feature engineering and AutoML, but users configure transformations and iterate on the workflow.
Limited automated feature engineering. Users create transformations and derived features manually within Qlik.
Protecting Against ML Pitfalls
Issues like data leakage and overfitting can make models look accurate but fail in production.
Built-in safeguards maintain reliable, production-ready predictions that stay that way over time, by proactively identifying data leakage, overfitting, and data drift
Provides model validation and evaluation tools, but preventing leakage and overfitting depends on user configuration.
Basic model evaluation available, but users remain responsible for validation and preventing modeling errors.
Evaluating Your Model's Performance
Evaluating an ML model goes beyond statistical scores—you need to understand its real-world impact.
The Pecan Agent evaluates predictions and provides clear guidance with actionable insights
Provides model metrics and visualizations, but interpreting results and improving models is left to the user.
Provides prediction metrics within Qlik, but offers limited guidance on improving business outcomes.
From Prediction to Action (Operationalization)
Predictions only create value when used in workflows.
Predictions are deployed directly into business systems and workflows
Predictions can feed analytics workflows, but operational deployment often requires additional integrations and automation.
Predictions remain closely tied to Qlik dashboards and analytics workflows. Integrating them into operational business systems requires additional development.
Pricing
Multiple iterations may be required to reach production quality, making cost efficiency essential.
Built for cost-effective experimentation, allowing quicker iterations toward production
Enterprise pricing based on platform capabilities and licensing. Costs increase as additional automation and AI functionality are added.
Enterprise subscription pricing tied to the broader Qlik platform and analytics stack.
Training and support
Predictive modeling isn’t just about the tech. There is know how on how to take predictive models and drive actual impact
Dedicated success teams, with vast experience and domain expertise
Documentation, community resources, training, and enterprise support focused on analytics workflows and platform adoption.
Documentation, Qlik training, and enterprise support focused primarily on analytics and BI adoption rather than predictive AI best practices.
Most analytics platforms help you analyze the past. Pecan helps you predict what happens next. Instead of building workflows to prepare data, engineer features, train models, and connect predictions back into the business, Pecan automates the entire predictive AI process, from raw business data to production-ready predictions that teams can immediately act on.
