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 for the task at hand so users can work smarter and more efficiently in the applications they use every day.
Common Analytics Capabilities within Software Applications
- 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 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
How is Embedded Analytics Different than Business Intelligence?
One of the questions we’re often asked is how embedded analytics is different than traditional business intelligence. 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. Instead, embedded analytics puts intelligence inside the applications people use every day to improve the analytics experience and make users more productive by combining insight and action in the same application.
Real World Examples of Embedded Analytics
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. 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 four levels of embedded analytics, how to go to market with your analytics offering, how to calculate ROI for embedded analytics, and more!