Companies are investing millions into machine learning (ML) and artificial intelligence (AI) tools that can give them data insights that translate into real business value.

Data insights refer to the understanding of business phenomena by analyzing a dataset using ML and AI technology. For example, an ML model that estimates customer churn rate will reveal the factors that cause churn, and with this information, business managers can change their processes and strategies.

Insight generation through analytics and business intelligence (BI) has been around for more than five decades, but trained analysts usually did the task. Many analysts of the past relied on experience and intuition as opposed to the data. In addition, the technologies available were difficult to use, which made it hard for most people to find and analyze data.

Today, several machine learning tools offer users automated insights. These tools overlap three broad software categories, business intelligence, predictive analytics, and insight engines.

Also read: Gaining a Competitive Edge Using BI and Data Management

Features of ML Tools for Automated Insights

Before we get into the tools we found useful, below are some features to look for when investing in an ML tool for automated insights.

  • Self Service Analytics: Self-service analytics with regard to data preparation, visualization and creation, and collaboration and sharing is a must-have feature. The tool should help users to tap into insights without waiting for IT to provision resources. Users can combine several data sources for visual analysis and create reports and dashboards through self-service analytics capabilities.
  • Analytics Apps and Dashboards: Large groups of less-skilled users require more than basic reports. They want a mechanism to search and examine data, discovering patterns, connections, and insights that lead to sound decisions. Interactive dashboards and guided analytics applications allow you to do so, benefiting a wide range of business users, executives, and managers.
  • Custom and Embedded Analytics: With open and standard APIs, you may construct completely customized applications for almost any scenario or purpose. Embedded analytics also allows you to embed dashboards and visualizations into operational apps, allowing even more people to discover insights into your data. Software and application developers will develop custom and embedded analytics, but everyone in your company will benefit…

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