Published
Jun 11, 2026
12 • Pages
Turn Qlik Into a Full Scientific Research Engine
Most organizations use Qlik for business dashboards, but its analytical depth goes far beyond reporting. This white paper introduces IPC Global's methodology for conducting rigorous scientific research entirely within Qlik, covering everything from ETL and hypothesis testing to machine learning deployment and SHAP-based model explainability, all grounded in the three fundamental pillars of science: falsifiability, replicability, and generality.
IPC Global
Technology
AUTHOR
Dr. Priscila Rubim, Igor Alcantara
AUTHORED YEAR
2024
AUDIENCE
Data Scientists & Research Analysts
INDUSTRY
Cross-Industry / Research & Analytics
TOPIC
Scientific Research Methodology in Qlik
White Paper Snapshot
Everything you need to know in under 30 seconds
Results & Impact
End-to-End Research in One Platform
From raw data ingestion through ETL, statistical testing, machine learning deployment, and interactive reporting, IPC Global's methodology enables the complete scientific research lifecycle to run inside Qlik, eliminating tool fragmentation and accelerating time from hypothesis to validated insight.
Explainable ML with SHAP Values
Every machine learning model deployed through Qlik AutoML returns per-row SHAP values alongside predictions, giving researchers and stakeholders a transparent, scientifically rigorous explanation of which features drove each outcome, turning black-box models into trusted, auditable decision tools.
Highlights
Replicability at Scale: Why Science Needs Qlik's Associative Engine
Replicability, the ability to repeat a study with the same methodology and obtain consistent results which is a foundational pillar of science, and Qlik directly strengthens it by enabling reproducible analyses, transparent documentation, and consistent handling of large data volumes across different datasets.
Qlik's correlation toolkit goes beyond basic Pearson R² through the MutualInfo function, which quantifies how much information one variable reveals about another, enabling non-linear dependency analysis and feature selection that standard correlation tools miss entirely.
Interactive dashboards with real-time filters and drill-down capabilities transform static research outputs into explorable data environments, allowing collaborators to validate findings, surface anomalies, and challenge assumptions without requiring access to raw data or scripting tools.
Model evaluation within Qlik AutoML uses the R² coefficient of determination to assess how well models explain variance in the target variable, providing researchers with a rigorous, statistically grounded measure of model fit before any deployment decision is made.
By centralizing the entire research workflow, from data ingestion to final reporting, within a single governed platform, Qlik eliminates the fragmentation between tools that typically undermines reproducibility in data science projects.
Key Topics
From Hypothesis to Validated Model — All Inside Qlik
IPC Global's six-stage methodology, Hypothesis & Objective, Methodology, Evidence-Based Analysis, Transparency, Control of Variables, and Research Data, provides a structured scientific framework that maps directly onto Qlik's native capabilities, making the platform a complete research environment rather than just a visualization tool.
Qlik supports the full ETL pipeline from source extraction through transformation and validation, with Qlik Talend offering a no-code alternative for data engineers who need to build and maintain data pipelines without scripting expertise.
Hypothesis testing runs natively inside Qlik, researchers can configure t-tests to compare group means and chi-squared tests to analyze categorical frequencies, then interpret results through p-values and confidence intervals without leaving the platform.
Qlik AutoML enables the full machine learning lifecycle within a single environment: data preprocessing, cross-validation, multi-algorithm training, model evaluation, and deployment, returning not just predictions but also per-row SHAP values that explain exactly which variables drove each outcome.
Built-in distribution functions covering Normal, Student's T, Beta, Binomial, Chi², F, Gamma, and Poisson, plus scripted Monte Carlo simulation support, give researchers the statistical depth typically reserved for dedicated scientific computing environments.