Turn Your Data's "What" Into a Powerful "Why"
Most organizations already know what is happening in their business, the real competitive edge lies in understanding why. This eBook walks business leaders and analysts through how Qlik AutoML automates machine learning to surface hidden patterns, predict outcomes, and recommend actions without requiring a data science background. If your team is ready to move beyond dashboards and into true predictive and prescriptive intelligence, this is your roadmap.
Published
2024-06-19
7 • Pages
IPC Global
Technology
AUTHOR
Igor Alcantara
PUBLISHED
2024
AUDIENCE
Business Leaders & Data Analysts
INDUSTRY
Technology (Data Analytics)
TOPIC
Qlik AutoML - Predictive & Prescriptive Analytics
White Paper Snapshot
Everything you need to know in under 30 seconds
Key Topics
From Descriptive to Prescriptive: The Analytics Maturity Leap
Most organizations are stuck at the "What" stage: Descriptive analytics, dashboards and historical reports only tells you what happened. Qlik AutoML enables the critical next steps: predicting what will happen and prescribing what to do about it.
SHAP values unlock the "Why" behind every prediction: Qlik AutoML's integration of SHapley Additive exPlanations (SHAP) gives analysts row-level visibility into which factors drove a specific outcome, turning black-box models into actionable insights.
Prescriptive analytics becomes achievable by combining AutoML with Generative AI: Diagnostic outputs from Qlik AutoML can be fed into Generative AI models to produce specific recommended actions, elevating analytics from insight to decision support.
The maturity journey is supported end-to-end within a single platform: Qlik covers all four analytics stages: descriptive, predictive, diagnostic, and prescriptive so organizations don't need to stitch together disparate tools as they advance.
Highlights
Automated Machine Learning That Saves Weeks of Work
No deep technical expertise required to build production-grade models: Qlik AutoML handles the full ML pipeline null imputation, categorical encoding, feature scaling, data holdout, and five-fold cross-validation automatically, so analysts can focus on interpreting results rather than engineering pipelines.
Multi-algorithm experimentation runs in parallel: Rather than manually testing one model at a time, Qlik AutoML trains multiple algorithms simultaneously across experiment versions and selects the best-performing model, compressing weeks of data science work into hours.
Seamlessly integrates into existing Qlik workflows: AutoML sits natively within Qlik Cloud, meaning ML-powered predictions can be embedded directly into dashboards, alerts, and automated reports without additional infrastructure or hand-offs.
Industry-spanning use cases are ready out of the box: From patient readmission in healthcare to fraud detection in finance and inventory optimization in retail, Qlik AutoML applies across verticals with minimal configuration, accelerating time-to-value.
Results & Impact
Weeks of ML Work Automated
By handling data preprocessing, algorithm selection, and model validation automatically, Qlik AutoML compresses what would typically take a data science team weeks into a streamlined, repeatable process accessible to business users.
Decisions Backed by Explainable AI
SHAP value integration gives every prediction a clear explanation, users can see exactly which variables drove a specific outcome, enabling confident, defensible decisions at every level of the organization.