Qlik Predict is Qlik Cloud’s no-code machine learning module for building classification, regression, and time series forecasting models without writing code. Its SHAP-based explainability layer shows which factors drive each prediction, and outputs feed directly into dashboards, automation workflows, and business decisions. Analyst teams can generate churn, demand, or risk predictions without waiting on a data science team.
The question analytics leaders face today isn’t whether machine learning works, it’s which team should use it, when, and under what controls. According to Grand View Research, the global automated machine learning (AutoML) market was valued at 3.5 billion dollars in 2024 and is projected to reach 61.23 billion dollars by 2033, growing at an average annual rate of 38 percent over that period. That growth signals no-code predictive tools have moved from niche experimentation to a standard part of the enterprise analytics stack. It also means the pace of adoption is outrunning many teams’ readiness on governance and explainability.
What is Qlik Predict?
Qlik Predict is a no-code machine learning environment embedded directly inside Qlik Cloud Analytics. A user selects a target variable, and Qlik Predict automatically determines the appropriate model type, classification, regression, or time series.
Once training completes, the system returns performance metrics like accuracy, precision, and recall alongside a SHAP-based feature importance chart. That chart shows exactly how much each variable influenced the prediction, so the model doesn’t remain a black box. Predictions flow directly into Qlik Cloud dashboards, automation pipelines, or what-if scenario analysis.
What sets Qlik Predict apart is that it isn’t a separate machine learning platform bolted onto an analytics stack. Data preparation, visualization, and prediction all run under the same governance layer, which makes the model’s full lifecycle easier to trace and audit.
What business problems does Qlik Predict solve?
Qlik Predict supports three core prediction types that turn backward-looking reporting into forward-looking action: category prediction, numeric value prediction, and time series forecasting. Each maps to a concrete decision problem in a different department.
On the sales and customer side, the most common use case is churn prediction. The model looks at variables like contract length, support ticket frequency, or usage intensity to flag which customers carry elevated risk of leaving. Finance teams use regression models for revenue forecasting and cash flow projections, while operations teams rely on multivariate time series models for demand planning and capacity forecasting.
What these scenarios share is that the prediction itself isn’t the value, the action it triggers is. Qlik Predict’s what-if analysis shows instantly how a prediction changes when you adjust a variable such as price, inventory level, or headcount. That shifts planning conversations from “what might happen” to “what should we do.”
Does no-code machine learning replace a data science team?
No, no-code machine learning doesn’t replace a data science team, it frees up a limited data science headcount from routine, repetitive prediction work so it can focus on harder problems. This is where the difference between AutoML and the classical data science process becomes clear.
In a classical machine learning workflow, a data scientist handles feature engineering, manually compares multiple algorithms, and manages hyperparameter tuning directly. AutoML automates most of these steps, which lets analysts or so-called “citizen data scientists” build reliable models without deep technical expertise. But that automation has a limit: if data quality is poor, if the business problem isn’t clearly defined, or if the model’s output feeds into a high-stakes decision (credit approval, hiring, medical triage), an experienced data scientist still needs to review it.
For that reason, mature organizations treat no-code machine learning not as an alternative to their data science team, but as a multiplier that spreads that team’s standards across business units. The data science team sets model architecture standards and risk thresholds; analysts meet day-to-day prediction needs within that framework.
Why is explainable AI (XAI) becoming mandatory for model approval?
Explainable AI (XAI) is a set of methods that makes a model’s reasoning understandable to people, rather than leaving it as an opaque black box. The SHAP (SHapley Additive exPlanations) technique that Qlik Predict uses quantifies exactly how much each feature contributed to a given prediction.
This has stopped being purely a technical preference and is increasingly a regulatory expectation. Under Turkey’s KVKK, individuals have the right to object to automated decision-making and to request an explanation, which means a model’s output needs to be documentable, not just accurate. According to KPMG’s AI Risk Survey, 73 percent of surveyed organizations report some degree of regulatory oversight of their predictive models, and 84 percent believe auditing of these models will become a requirement within the next one to four years.
That shifts model explainability from a nice-to-have into a standard step in model approval. Deploying a predictive model without an explainability layer like SHAP may look fast in the short term, but it leaves the organization exposed the moment an audit or a customer objection arrives.
When is your organization ready for no-code predictive modeling, and when isn’t it?
Your organization is ready for no-code predictive modeling when it meets three conditions at once: clean, accessible data, a clearly defined business question, and a governance mechanism to approve the model. If any one of these is missing, closing that gap should take priority over investing in a tool.
Data readiness is the first filter. A model built on data that’s scattered across systems in inconsistent formats won’t produce reliable predictions no matter how polished the interface is. The second condition is a clearly scoped use case; a vague goal like “let’s predict customer behavior” also makes it impossible to evaluate whether the model is actually performing. The third condition is the one most often skipped: McKinsey’s 2025 State of AI survey found that 78 percent of organizations use AI in at least one business process, but governance maturity lags well behind that pace of adoption. In other words, the tooling is spreading fast at enterprise scale while oversight mechanisms are falling behind.
Once those three conditions are met, the real distinction becomes whether to use a platform-embedded solution like Qlik Predict or a separate point solution. With an embedded solution, data preparation, model training, and governance all run in the same environment, so the model’s full lifecycle can be traced from one place. With a standalone point solution, data movement, authentication, and audit logging become integration overhead that has to be managed separately. If your organization doesn’t have the capacity to own that integration overhead, the embedded model is the better fit.
Frequently Asked Questions
Do you need coding knowledge to use Qlik Predict?
No, Qlik Predict works entirely through a no-code interface. You connect a data source and select a target variable, and the system automatically determines the right model type. Coding isn’t the skill that matters here, correctly framing the business problem and interpreting the result is.
What is model drift and how is it detected?
Model drift is the decline in a model’s prediction accuracy that happens when real-world data gradually diverges from the data it was trained on. Shifts in customer behavior or market conditions can trigger this. Regular performance monitoring, combined with retraining on fresh data when needed, is the main way to catch drift early.
What’s the difference between classification and regression predictions?
Classification predicts a category, such as whether a customer will renew a contract. Regression predicts a continuous numeric value, such as expected revenue. Which one you use depends on whether your target variable is a class or a number.
How long does it take to integrate Qlik Predict into an existing BI setup?
Because Qlik Predict is embedded inside Qlik Cloud Analytics, it doesn’t require a separate integration project and works directly through existing data connections and dashboards. Timeline mostly depends on data quality and how clearly the target is defined, model validation and approval typically take longer than the technical setup itself.
TL;DR
Qlik Predict is a no-code predictive modeling tool embedded in Qlik Cloud, with SHAP-based explainability built in.
Core use cases are churn prediction, revenue forecasting, and demand planning.
No-code machine learning doesn’t replace a data science team, it frees up its capacity from routine tasks.
Explainability is no longer just a technical preference, regulations like KVKK make it a standard part of model approval.
An organization should only move to no-code predictive modeling once clean data, a clear business question, and a governance mechanism are all in place.
Conclusion
No-code predictive modeling is no longer an experimental add-on to the analytics stack, it’s a standard component of it. But the speed at which these tools are spreading doesn’t mean governance and explainability capacity is keeping pace, and McKinsey’s data makes that gap clear. The advantage of a platform-embedded solution like Qlik Predict is that it closes exactly that gap: training, explanation, and audit all happen in the same environment.
To find out whether your organization is ready for this step, test one existing prediction need against three criteria: is your data source clean, is your target variable clearly defined, and who will own the model’s approval process. If the answers are clear, start with a pilot model. If they aren’t, invest in data readiness and governance ownership first.
Sources
McKinsey & Company, The State of AI in 2025: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai