Why Embedded Analytics Will Change Everything

Analytics are being embedded in all kinds of software. As a result, the ecosystem is changing, and with it so is our relationship to analytics. Historically, analytics and BI have been treated as something separate -- we "do"analytics, we're "doing" ad hoc reporting -- but increasingly, analytics are becoming an integral part of software experiences, from online shopping to smart watches and to enterprise applications.

"We're creating whole industries that are centered around data and analytics that are going to challenge the status quo of every industry," said Goutham Belliappa, Big Data and Analytics practice leader, for Capgemini North America. "Analytics will become so ubiquitous, we won't even notice it.  From a business perspective, it's going to transform entire industries."

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Three drivers are collectively changing how we experience and think about analytics. The first, as previously mentioned, is embedding analytics into all kinds of software. The second is automation, and the third is a shift in the way software is built.

 Automation is Fuel

Modern software generates and analyzes more data than ever, and the trend is going to accelerate. The resulting glut of data is outpacing humans' ability to manage and analyze it, so some analytics necessarily have to be automated, as do some decisions. As a result, analytics has become invisible in some contexts, and it's going to become invisible in still more contexts soon.

"Frictionless" is a good way to describe what people are striving for in effective user experiences.  Certainly, with more automation and more behind-the-scenes analytics, how we think of analytics will change," said Gene Leganza, VP & research director at Forrester Research. "We'll be thinking about the results -- do we like the recommendations of this site's or this app's recommendation engine or is that one better?  We'll gravitate towards the services that just work better for us without knowing how they do it."

That's not to say that automated analytics should be implemented as black boxes. While humans will apply less conscious thought to analytics because they are embedded, they will still want to understand how decisions were made, especially as those decisions increase in importance, Leganza said. Successful software will not just automate data management and analytics and chose the right combination of microservices to achieve a particular result, it will also be able to explain its path on demand.

Microservices Will Have an Impact

Software development practices are evolving and so is the software that's being built. In the last decade, monolithic enterprise applications have been broken down into smaller pieces that are now offered as SaaS solutions. Functionality is continuing to become more modular with microservices, which are specific pieces of functionality designed to achieve a particular goal. Because microservices are essentially building blocks, they can be combined in different ways which impacts analytics and vice versa.

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Tableau has embraced microservices so its customers can combine B2B tools in a seamless way.  For example, Tableau is now embedded in Salesforce.com, so a sales rep can get insights about a customer as well as the customer details that were already stored in Salesforce.com.

"The more embedded you get, APIs and developer extensions become more relevant because you need more programmability to make [analytics] more invisible, to be seamless, to be part of a core application even though it comes from somewhere else," said Francois Ajenstat, chief product officer at Tableau.

Software continues to become more modular because modularity provides flexibility. As the pace of business accelerates, software has to be able adapt to changing circumstances quickly and without unnecessary overhead.

"In order to automate more and more actions and to enable adapting to a myriad of conditions, we'll be having software dynamically cobble together microservices as needed.  The granularity of the services will have to be synched to the patterns in the data.  For the near future, the task will be to make the software flexible enough to adapt to the major patterns we're seeing," said Forrester's Leganza.

What's Your Take?

How are your opinions of and experience with analytics changing as analytics become embedded into more types of software?  Are your experiences becoming "frictionless" yet or is there room for improvement?  How are automated analytics impacting you?  We'd love to hear about your experiences and your opinions in the comments section.