Evaluating the Effectiveness of Qlik AutoML in Predictive Analytics

How Qlik AutoML takes predictive analytics beyond the boundaries of traditional machine learning, making it accessible to everyone.

Welcome to Tech Station, SDG Group’s hub for uncovering the latest innovations in data and analytics! In this article, we explore Qlik AutoML, a tool that democratizes machine learning, allowing business users to create, understand, and act on predictive models without technical complexity. Discover how this solution shifts the focus from retrospective analysis to proactive decision-making, transforming historical data into a strategic guide for the future.

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In today’s work environment, focusing on the technical complexity of developing models for decision-making can become a limitation, taking away time and resources that could instead be devoted to the actual analysis.

In this context, Qlik AutoML comes to the rescue by enabling the application of data science to everyday problems in a simple and efficient way. It offers machine learning models and predictive analytics through an intuitive and straightforward experience that leverages artificial intelligence to enhance human decision-making capabilities.

In this article, we will analyze what it consists of and, through a case study, understand how its features ensure the reliability and performance of the models, thereby improving the effectiveness of decision-making processes.

What is Qlik AutoML?

Qlik AutoML transforms AI into actionable value by enabling organizations to shift from retrospective analysis to proactively driving future outcomes.

It not only creates models that predict future trends by analyzing historical data but also helps explain those predictions.

In fact, it is not a “black box” system: the “compare” page shows the overall performance of the various models tested, while the “analyze” page reveals which information was most useful in generating the predictions. Finally, Qlik AutoML enables action based on those predictions, offering interactive tools to test different scenarios and assess their potential impact on the business.

Qlik AutoML focuses on three main areas:

• Predict: Qlik AutoML takes your historical data and creates models to forecast future trends, requiring no code. You simply provide your data, and Qlik AutoML takes care of generating reliable predictions.
  
  
• Explain: As previously mentioned, users have access to both the compare page, which displays the overall performance of the different models tested, and the analyze page, which dives deeper into individual models to identify the most important information used in generating predictions. This transparency helps build trust in the predictions and allows you to explain the underlying reasoning to your team and stakeholders.
  
  
• Act: Qlik AutoML allows you to easily integrate insights into existing Qlik analytics applications and daily operations. It is also possible to set up alerts to notify your team when changes in key metrics are predicted, enabling proactive intervention.
  
  

Below, we can see the Qlik AutoML workflow:

Qlik AutoML workflow

After connecting to the data source and preparing the dataset, it is necessary to define the outcome to be predicted or the problem to be solved.

After that, the model is automatically created and optimized, and it will then be used to generate the prediction.

Once the key factors influencing the prediction are identified, it becomes possible to make the best decision based on one’s specific objective.

One of the many advantages of using Qlik AutoML is precisely the ability to obtain models and predictions without writing a single line of code, thereby expanding the audience capable of using it.

As mentioned, it also clearly explains how these predictions are made, and it is possible to integrate the insights into daily operations and existing Qlik analytics applications, as well as to test different scenarios and understand their potential impact on the business.

A Real-World Example

Below, we present an example of a predictive problem aimed at detecting fraudulent transactions, based on historical data such as transaction amount, the time of occurrence, the frequency of operations, etc.

For the training phase, it was sufficient to download the dataset containing the historical data, define the variable to predict — in this case, fraud (indicated by the binary class 0/1) — and select the most relevant features, then run the model.

Within the Models section, it is possible to identify which of the tested models has the best performance and to see the contribution of the individual features to the prediction.

In the Compare section, you can compare models and apply filters to make the analysis as precise and personalized as possible, enabling users to contrast specific algorithms tested.