What are Data Insights?
“Insight is the value obtained through the use of analytics. The insights gained through analytics are incredibly powerful, and can be used to grow your business while identifying areas of opportunity.” – Localytics Blog
Data insights refer to the understanding of a particular business phenomenon you are able to achieve by using machine learning and artificial intelligence (AI) technology to analyze a dataset. For example, a machine learning model that estimates the likelihood of a customer to churn will reveal what factors drive churn rates, allowing decision-makers to make changes to business strategies and processes.
One of the best ways to understand and communicate meaningful insights from data is with tools that help visualize a model’s outcomes and give different ways to explore and understand your data. This translates to real business value in the form of increased ROI on advertising efforts, more accurate loan default predictions, and much more. The clarity of vision from data insights allows users to make better decisions based increases model interpretability, allowing analysts and other users to explain model outcomes to key stakeholders.
Why are Data Insights important?
Insights allow users of all skill levels to understand what the model is doing “behind the scenes,” which is especially important when it comes to highly regulated industries like banking and healthcare. If you don’t understand why your model is drawing the conclusions it does (i.e., you don’t have any insight into the inner workings), the model’s practical usefulness is limited.
Data visualization tools help users understand and explain insights from machine learning model outcomes. Whether it is through simple graphical representations like word clouds or more complicated and flexible data visualization tools like Tableau dashboards, these tools make it easier to understand and communicate the value uncovered by the model and drive better business decision-making.
Data Insights + DataRobot
DataRobot’s Insights functionality has a host of tools for model interpretation, all of which allow users to easily understand the models without requiring extra work on the user’s part. For example, if a hospital’s dataset includes a feature with doctors’ notes in the form of free text, DataRobot automatically generates a word cloud that indicates which words in those notes are related to the hospital’s target variable.
The example below shows words from just such a dataset are related to patient readmission rates. The red words are correlated with high-risk and the blue words with low-risk patient notes. The size of the word represents how frequently it occurs in the dataset.
DataRobot also includes tools that measure and rank the impact of individual features (see Feature Impact), provide details of how the model works and the processes it runs on input data, and Prediction Explanations that give the top reasons for the model’s outcome for each individual record.