Behavior analysis is a type of data analysis that tries to understand how and why people behave that way. Behavior analysis measures how consumers relate to digital applications (e-commerce, mobile, social media, gaming, IoT). Data such as demographics or past activities are collected to generate insights and predict future trends. The principles of behavior analysis are applied not only to the behavior of people, but also to the interactions and dynamics between processes, machines, equipment, and even macroeconomic trends.
Behavioral analytics combines typical event tracking with audience segmentation to associate users with triggered events and map customer journeys, using the large amount of raw data collected by the analytics platform.
Behavior Analysis is an important subset of business analytics that helps us understand how and — why — consumers and apps behave this way. It is particularly prevalent in e-commerce, online gaming, web, mobile and IoT applications — and provides the data needed to improve the customer journey towards transformation.
Behavior analytics measure data such as navigation paths, clicks, social media interactions, and purchase/cart abandonment. A/B testing is an example of using analytics to make data-driven decisions.
MLOps is, in simple terms, a set of applications aimed at improving communication and collaboration between your employees in data science and operations of your brand. Machine learning is also defined as a combination of data engineering and development activities.
Data security refers to the process of protecting corporate data and preventing data loss through unauthorized access.
It is difficult to make a clear definition of data quality. The truth is that your data quality is good if the data achieves its purpose of using it. For example, showing the right values on a management board to guide the organization ensures that management is also consistent and the process is managed correctly.
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