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Organizations generate a vast amount of data on a daily basis, and understanding it all is becoming increasingly difficult. To gain a competitive edge, companies need to harness the power of analytics to gain insights from their data. One of the most effective ways to do this is through application-driven analytics.

What is application-driven analytics?

Application-driven analytics is the process of using analytics tools and techniques within specific applications to gain insights and make better decisions. It involves embedding analytics capabilities directly into business applications, so users can access real-time data without leaving the application they're working in. It eliminates the need to switch between different tools and interfaces to access analytics information. This approach makes it easier for users to consume and act on data insights, leading to better decision-making and improved business outcomes by enabling users to make real-time data-driven decisions based on the application's available data.

What are the benefits of application-driven analytics?

  • Improved decision-making: By embedding analytics capabilities directly into business applications, users can access real-time data and analytics without switching between multiple tools. Embedding analytics capabilities at the data source makes it easier for them to make informed decisions based on the most up-to-date information.
  • Increased efficiency: With application-driven analytics, users can access the data they need within the context of their work without spending time searching for information. This can increase efficiency and productivity as users focus on their work, solving the business problem without getting distracted by data analysis.
  • Better collaboration: Application-driven analytics makes collaborating and sharing insights easier for teams. By embedding analytics capabilities within collaboration tools like chat and email, team members can share data insights and collaborate on solutions in real-time.
  • Improved customer experiences: By using application-driven analytics to gain insights into customer behavior and preferences, organizations can personalize customer experiences and improve customer satisfaction. For example, a retailer might use data analytics to recommend products to customers based on their previous purchases or browsing history.
  • Greater agility: Application-driven analytics enables organizations to respond quickly to market or business environment changes. Organizations can make faster decisions and adjust their strategies by accessing real-time data and analytics.

Examples of application-driven analytics

Some examples of application-driven analytics include:

  • In-app dashboards: Dashboards embedded within an application provide users with real-time analytics and visualizations.
  • Embedded analytics tools: Analytics tools integrated directly within an application, allowing users to analyze data within the context of their work.
  • Predictive analytics: Machine learning algorithms integrated within an application to provide predictive insights based on historical data.
  • Real-time monitoring: Real-time monitoring of data and systems within an application to detect and respond to issues as they arise.

Building apps with embedded analytics: OpenShift and MongoDB

Developing modern apps on Red Hat OpenShift with MongoDB Atlas provides a powerful and flexible platform for building, deploying and managing applications with embedded analytics using MongoDB and related services.

MongoDB Atlas is a fully-managed cloud database service, certified on OpenShift, that provides a scalable and flexible platform for building and deploying applications with sophisticated analytics. When building your application in OpenShift, you can use any programming language or framework that supports MongoDB, such as Node.js, Python, Java or .NET. You can also use MongoDB Atlas's built-in features, such as Atlas Search, to build search functionality into your application.

In addition to using MongoDB Atlas as a database in OpenShift, developers can use other services and tools from the MongoDB Atlas operator, such as MongoDB Atlas Data Lake, MongoDB Charts and MongoDB Realm, to build more complex and robust applications.


Application-driven analytics is a powerful tool for organizations looking to gain insights from their data and improve business outcomes. By embedding analytics capabilities directly into business applications, organizations can make it easier for users to consume and act on data insights, leading to improved decision-making, increased efficiency, better collaboration, enhanced customer experiences and greater agility.

Visit the Red Hat booth at MongoDB.local NYC on June 22nd to learn more about modernizing applications with embedded analytics with OpenShift and MongoDB!

Sobre o autor

Adam Wealand's experience includes marketing, social psychology, artificial intelligence, data visualization, and infusing the voice of the customer into products. Wealand joined Red Hat in July 2021 and previously worked at organizations ranging from small startups to large enterprises. He holds an MBA from Duke's Fuqua School of Business and enjoys mountain biking all around Northern California.

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