The rise of machine learning-based personalized discovery features over the past few years is one of the biggest stories of IT. The statistics are truly staggering. For example, even as of several years ago, over 30% of Amazon’s sales were reportedly due to their personalized recommendations—the figure is no doubt even higher now. LinkedIn has reported that fully 50% of their users’ connections, group memberships, and job applications are driven by their personalized discovery features. And 75% of what people watch via Netflix is due to personalized recommendations. In addition, of course, targeted advertisements can be considered a form of personalized recommendations, as can personalized search, both of which have largely replaced their non-personalized precursors.
So in the consumer world automatic personalization has become an indispensable feature for users and a competitive imperative for providers. What about in the enterprise? Not so much—until now, that is. I wrote The Learning Layer to lay out the path toward making adaptive, personalized discovery a core feature of enterprise IT, and we at ManyWorlds are excited that our Synxi-brand technology is now making that vision a reality!
We are delivering adaptive discovery apps for the major social platforms that continuously learn from users’ experiences and apply this learning to provide users with real-time, personalized recommendations of knowledge and expertise, and which are sensitive to the context of their current activities. Even better, we also have connectors among these apps that extend a layer of learning across platforms. That means users can receive cross-contextualized and personalized recommendations of knowledge and expertise from one platform (e.g., SharePoint) based on what they are doing in another platform. And finally, we have booster products for enterprise search that enable search results to be personalized and/or additional personalized content to be recommended based on the context of a specific search result. That provides users, for the very first time, an enterprise search experience that tops the search experience internet search providers can deliver.
These learning layer technologies are collectively leading toward enterprises becoming truly personalized. And an enterprise personalized is an enterprise that is more productive, as well as being an enterprise that is more compelling to be a part of and to work with.
Some of us from ManyWorlds recently attended the Rackspace Software as a Service (SaaS) Summit. ManyWorlds is a long-time Rackspace customer—we are fortunate to have chosen Rackspace to power our Epiture® learning layer platform back in Rackspace’s early days, well before they became the leader in computing infrastructure and cloud computing services that they are today.
We were particularly interested in the SaaS applications part of the conference, and it was amazing to see how the number of highly valuable enterprise applications being served from the cloud has absolutely exploded over the past few years. It is clear that delivery of applications from the cloud is inevitably becoming the standard model, even for the largest enterprises—and Rackspace is orienting its entire business around this reality.
As a still-recovering big company CIO who spent a lot of time worrying about such things, however, perhaps the most intriguing topic at the conference for me was with regard to integration—how applications served from the cloud can be effectively integrated with existing enterprise applications. Most cloud apps have a relatively narrow scope—they do one thing, and they do it very well. But they need to fit within the context of an existing application environment, which is often the province of broad-scope applications such as enterprise resource planning (ERP) systems (e.g., SAP) and customer relationship management (CRM) systems. As the number of cloud-based applications grows, this integration issue becomes front and center for the CIO. Legacy applications generally cannot simply be ripped out and replaced—so integration approaches, whether ad hoc, or better, through standardized APIs, promises to increasingly dominate the CIO’s agenda.
The Summit served to reinforce for me that the good news is that the learning layer can play a strong role in ameliorating this integration problem. The “ethereal network of learning” that I discuss in the book can be applied to integrate together myriad cloud applications with complex legacy application environments. Think of it as an “adaptive middleware” layer sitting between the cloud-based applications and the legacy applications, providing a single, coherent environment that includes a capability for integrative workflow, and that automatically adapts and personalizes based on usage.
When we first conceived of the learning layer, we primarily thought of it as providing an integrated layer of learning over existing applications and content—it is now clear that, perhaps even more importantly, it will serve as an adaptive bridge between the new and the old of the application world.