Data insights are the value organizations gain by using analytics, machine learning models, and artificial intelligence. Decision-makers use these insights to make strategic decisions to grow their business and identify opportunities to build a competitive advantage.
Obtaining data insights allows organizations to understand trends and patterns in customer behavior and make predictions about what may occur based on real-time data.
For example, a business can use a machine predict learning model to predict customer churn. By identifying what is driving churn rates, the company can retain the customer and make changes to their strategy to reduce this figure in the future.
Data insights can also be communicated using visualization tools – providing even more opportunities for a company to understand the underlying patterns that drive customer decisions and other critical business factors.
The best insights focus on trends rather than specific data points since the goal is to use the information to predict a target variable. It is also necessary to look at these trends over time, but make sure to keep ranges comparable since other months have a varying number of days.
When trying to gather data insights, look for healthy relationships between variables, and consider different perspectives – this will allow your organization to make accurate assumptions.
Data insights allow users of machine learning models to understand what the algorithm is doing behind the scenes. This is essential for regulated businesses like those in the financial and healthcare industries – before leaders can rely on models to make significant decisions, they have to trust the outputs that are coming from them.
Consider a scenario where a medical professional uses artificial intelligence to determine the best way to treat a patient. If the model recommends that the patient undergo major surgery, the doctor must understand how it arrived at that conclusion so they can explain it to the patient and their loved ones.
This concept is also known as prediction explanations. It is when the inner workings of the model are explained so that the end-user can confidently rely on the outputs.
It doesn’t matter if you only have BI analysts, IT leaders, or executives wanting to leverage data insights or if you have a team of in-house data scientists looking to be more impactful with their analytic output. Any organization looking to start or advance their AI journey can benefit from data insights.
At LogicPlum, we give you access to some of the most sophisticated models in the industry – allowing your business to gain valuable insights and make data-driven decisions. We understand that prediction explanations and data insights are essential to trusting our platform, so we provide a wide variety of model interpretation tools.
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