From as early as 2013, we have seen reports of “embedded analytics catching fire”, and these have increased in popularity in recent years, as it has been estimated that as many as two thirds of software and SaaS providers look to include an embedded analytics feature in their apps.
While it is recommended to exercise caution in relying on these types of numeric estimates, the increasing prevalence of embedded analytics is certainly something to take note of. In recent years, there has been a trend away from a “traditional” BI package and more towards a white-labeled embedded analytics option. And it is not merely the “usual suspects”, such as app developers, who are looking for an embedded solution, but a wide variety of companies in other industries ranging from competitive intelligence to gift card distributors.
Apparently, embedded BI is becoming more prevalent, and it has the potential to present new business models and possibilities. Before going on to describe these, let’s first try to define the scope of the discussion by asking, “What are embedded analytics/BI?”
What is Embedded Analytics/BI?
A useable definition of the term can be found here: “…the use of reporting and analytic capabilities in transactional business applications. These capabilities can reside outside the application… but must be easily accessible from inside the application, without forcing users to switch between systems.”
Indeed, what differentiates “embedded” data analytics software is that it becomes an organic part of the application in which it resides, and it is typically accessed via single sign-on, so that the end user does not feel that he or she is leaving the ‘comfort’ of their native app to access it.
While many companies opt to develop these reporting and analytic features in-house, in the process of producing their own application, others find it preferable to purchase an external solution and plug it into their own system with the required adjustments and relabeling. This is often the case when business intelligence is not part of the developing team’s core competencies, and even more so when dealing with large or complex datasets.
Traditional Use Cases
The typical use case for embedded analytics seems straightforward enough: implementing such a system adds value to the native app by allowing users to easily utilize the data it collects, essentially offering them potential insights rather than raw data.
For a simplified, B2C-type example, let’s think of a jogging app for wearable devices: it would collect biological data, running time, speed, distance covered, etc. So instead of just presenting this data as it is, the app provider could show the jogger his or her data via a visual dashboard, which would clearly indicate improvement over time and allow for some educated guesses as to the causes behind it. Furthermore, the app could combine this information with external data sources, such as weather or terrain conditions, and allow for further potential insight.
Of course, businesses tend to deal with much larger amounts and more complex data. But the point remains: most apps collect data, and embedded analytics makes it easier for casual users to see their data while enabling more advanced users to reach operative conclusions with it.
A similarly interesting way to use embedded analytics is to actually show the customers how they are using the product, and what they are gaining from it.
Proving Value to Customers
In addition to the type of data described above, companies are beginning to realize the potential that lies within a different dataset that their applications often collect, such as usage statistics.
It’s no secret that most applications monitor and collect diverse statistical information regarding the way consumers utilize their product. This information could include usage time, amount of data processed, most used features, bandwidth, and more. This data is typically collected for internal purposes to understand how the product is actually being used and how it can be improved, but companies can now combine integrated data analytics with reporting features and use embedded BI to show customers how they are using the app itself.
By doing so, the provider can give the customer a much more transparent picture of what he or she is using the application for, and hence show them the value they are getting out of it. As a result, the end user can gain a better understanding of what their actual ROI from the product is, and make a more informed decision regarding whether and how much they are willing to pay for it.
This could have a significant positive impact on the B2B software market. Enterprise software often does not come with a single sticker price, and quotes are given depending on several different factors. Pricing is developed from a clear, fact-based analysis, and making the data more visible and available to the customers helps them understand the price that is being requested. This consequently creates more trust and transparency in the relationship between the vendor and the customers.
Needless to say, usage data should be available, not only during fiscal negotiations, but in real-time during the customer’s ongoing usage of the product. This will give providers a clear picture of how customers are using the product and how it affects their business. In addition, an embedded BI feature should present the data in full detail and allow for different views and analyses to be performed on it. It should also analyze the information alongside other potentially relevant data sources in order to present as comprehensive a picture as possible.
This is merely one new and interesting use case for embedded analytics, and many more will appear in coming years as the trend continues to expand. As embedded analytics “goes mainstream”, there will surely be more fringe use cases.
About the Author
Adi Azaria is the Co-Founder and Chief Evangelist of SiSense, the award-winning business analytics and dashboard software that lets non-techies easily analyze and visualize big data sets from multiple sources.