Adobe Systems has for the past several years been steadily investing in an ambitious strategy that aims to centralize any number of workflow applications around an Adobe Cloud service. At an Adobe Summit 2017 conference today, Adobe advanced that strategy by unveiling a common data language, dubbed the Experience Data Model, which enables organizations to tightly integrate data and processes across multiple applications.
In addition, Adobe unveiled a revamped tag management system called Launch, as well as additional application programming interfaces (APIs) and event services that developers can invoke to build applications around Adobe Cloud. As part of the effort, Adobe is making available integrations with a variety of Microsoft applications and services, including Microsoft Office 365 and Microsoft Azure, as well as applications from AppDynamics, Dun & Bradstreet, Mastercard, SapientRazorfish, Clicktale, Ooyala, Acxiom and Zendesk.
At the same time, Adobe expanded the Adobe Sensei machine learning framework it makes available via Adobe Cloud to include capabilities that make it simpler to identify anomalies in data, make content available on specific types of devices, and automate the testing of personalized content.
Rather than requiring developers to build these capabilities on their own, Kevin Lindsay, director of product marketing and strategies for Adobe Target, says Adobe is actively recruiting developers to programmatically invoke a variety of Sensei services.
“We want to make Sensei machine learning available to third-party developers,” says Lindsay.
Finally, Adobe unfurled Adobe Experience Cloud, a service through which organizations can manage marketing campaigns alongside an Adobe Advertising Cloud that specifically addresses the management of advertising.
As more organizations begin to better understand the potential business value of the data and content they produce, interest in applications that help them tap into the value is rising across the enterprise. The challenge they all face, however, is figuring out the simplest way to achieve that goal without having to build all the required complex enabling technologies, including machine learning algorithms, from the ground up, themselves.