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What Is Embedded Analytics?

By Alvin Wong | October 1, 2013
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Let’s start with a definition. Embedded analytics is the integration of analytic content and capabilities within business process applications. It provides relevant information and analytical tools designed so users can work smarter and more efficiently in the applications they use every day.

Common Embedded Analytics Capabilities within Software Applications

Successful embedded analytics projects have more than appealing data visualizations. Embedded dashboards and reports should generate new insights and actions. The most common analytics capabilities include:

  • Dashboards and data visualizations: charts and graphs that display performance metrics
  • Static and interactive reports: tabular views of data without or without parameters and scheduling capabilities
  • Self-service analytics and ad hoc querying: enables users to ask their own questions of the data by exploring a set of data
  • Benchmarking: comparing performance metrics against best practices from external data
  • Mobile reporting: ensures interactive functionality on mobile devices and takes advantage of capabilities specific to mobile devices
  • Visual workflows: incorporating transactional capabilities directly within the analytic user interface, sometimes referred to as write-back
  • Predictive analytics: an area of machine learning and artificial intelligence that lets users answer the question, “What is most likely to happen based on my current data, and what can I do to change that outcome?”

How Is Embedded Analytics Software Different than Business Intelligence Software?

One of the questions we’re often asked is how embedded analytics software is different than traditional business intelligence software. The answer is, “It’s all about context.”

Business intelligence is a set of independent systems (technologies, processes, people, etc.) that aggregate data from multiple sources, prepare the data for analysis, and then provide reporting and analysis on that data from a central view point. It is most optimized for supporting managerial level decisions that require highly aggregated views of information from across a department, function, or entire organization. These systems are specifically developed to operate within the silo of someone who solely performs data analysis.

Embedded analytics, on the other hand, is a set of capabilities that are tightly integrated into existing systems (like your CRM, ERP, marketing automation, and/or financial systems) that bring additional awareness, context, or analytic capability to support decision-making related to very specific tasks. These tasks may require data from multiple systems or aggregated views, but the output is not centralized overview of information. It is targeted information to support a decision or action in the context in which that decision or action takes place.

Said another way, business intelligence is a map that you utilize to plan your route before a long road trip. Embedded analytics is the GPS navigation inside your car that guides your path in real time.

While traditional BI has its place, the fact that BI applications and business process applications have entirely separate interfaces forces users to switch between multiple applications to derive insights and take action. Nucleus Research estimates that switching from one application to another to get business insights wastes up to two hours per employee each week.

Unlike traditional business analytics software, an embedded analytics platform puts information and insights inside the applications people use every day. When advanced analytics capabilities are embedded in existing software, users get a better application experience and can be more productive by combining insight and action in the same application.

>> Discover the latest embedded analytics trends in the State of Embedded Analytics Report <<

Real-World Examples of Embedded Analytics Applications

Salesforce: If you work for a company that has a sales team, chances are you’re familiar with Salesforce.com. When people think of embedded analytics in a B2B context, they’re usually thinking of the Salesforce model, where analytics are sprinkled throughout the application in a few key places like the homepage and an analytics tab. Salesforce has two tabs – Dashboards and Reports – where users can consume information and create their own reports all within the platform. Users can also customize their homepage to show key performance indicators, such as quarterly sales and leads against quota. While this is a good start, we’d like to propose that embedded analytics can be more than this such that it’s integrated into the core workflows of the application, not just the UI.

Amazon: Amazon is the Gold Standard for providing relevant analytics to encourage on-site conversion. Fundamentally, Amazon exists to sell books. They have great processes to support the ecommerce experience – including fast shipping, low prices, and Buy Now with 1-Click. But Amazon also satisfies customers’ informational needs by providing product ratings, video reviews, and suggested products. They’ve added tremendous value by providing relevant analytic information at the point of transaction to create a superior customer experience.

This is the first post in a new series on embedded analytics, so stay tuned for info on the five levels of embedded analytics maturity, how to go to market with your analytics functionality, advice for getting started with your analytics projects, how to calculate ROI for embedded analytics, and more!

Improve Productivity and the User Experience with an Embedded Analytics Platform

Embedded analytics improves the user experience while increasing end-user adoption and growing revenue. Unlike business intelligence software, embedded analytics keeps users in the applications they use every day and puts valuable data in their hands that they can use to make decisions or glean insights quickly and easily. If you’re interested in learning how an embedded analytics solution works, you can see a demo of Logi’s embedded analytics platform here.

Learn how to improve the analytics offering in your business application in the Definitive Guide to Embedded Analytics. 

 

About the Author

Alvin Wong has an extensive background in solution architecture and implementation of SaaS and business intelligence applications. Alvin earned his MS in Engineering Management from Stanford University and BS in Electrical Engineering from Cornell University.

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