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There is no denying the tremendous market opportunity right now for businesses to provide applications that are more intelligent—apps that that provide cognitive predictive capability derived from the analysis of their ever-growing sources of data. Technology has evolved to the point where businesses of all sizes can—and must—get into the game. Machine learning and AI are no longer only for those resource rich organizations that can afford an army of data scientists. That’s why we recently acquired predictive analytics pioneer DataRPM, a company that is focused on making analytics accessible to more companies. We are determined to do for analytics what we have already done for app dev—make it easy for companies to bring solutions to market fast. It’s all part of continuing our mission to develop and deploy the mission-critical applications you’ll need to succeed moving forward.
Given the wealth of behavioral, environmental and machine data (IoT) at our disposal, if we don’t put it to use, its value and potential competitive advantage will go to waste. Therefore, the need to build more compelling apps with intelligence delivered by machine learning and AI is quickly becoming a business imperative. At the same time, we cannot forget about user experience. These predictive outcomes must become inherent in multi-channel apps to deliver optimal user experiences critical for employee and customer engagement. We call this new era of mission-critical applications cognitive-first.
The current development approach where analytics/reporting is often a separate discipline from application development just won’t cut it anymore. Based on conversations with our customers and extensive research that we have performed, we believe organizations need to leverage an automated approach to analytics to succeed. While this automated approach will not eliminate the need for analytics skills, it will not only decrease the number of data scientists that are required, it will improve the accuracy of predictions while speeding predictive results—all while reducing the infrastructure costs required to train, test and deploy analytical models.
In addition to an automated approach to analytics, organizations need a way to use the results to drive their business processes—they need the predictive results integrated into their business applications.
Analyst Matt Aslett of 451 Research agrees with this approach in his March, 2017 report, “Progress Accelerates Cognitive Apps Strategy with DataRPM Buy.”
“…The combination of application development, data connectivity and business rules management software with machine learning and predictive analytics makes sense in terms of providing a platform that will enable ISVs and enterprises to develop operational applications that take advantage of the intelligence being generated by big-data and Internet of Things (IoT) projects.”
Making cognitive-first a reality for our customers drives everything that we do at Progress, including driving our product strategy and go-to-market plans. The acquisition of DataRPM brings automated analytics to our customers in the form of a meta-learning approach that automates tasks that currently require human input, such as data tagging, finding patterns and anomalies and identifying key signals.
And it’s driving what we do with our application development capabilities, including the ability to create compelling user experiences that transcend a specific user channel or device. We continue to invest in a micro services approach that allows applications and systems of any type to consume predictive analytics results, business logic and data so that a cognitive-first strategy is attainable by our customers.
While The 451 Research report discusses our cognitive-first approach from an industry analyst perspective, we hope you’ll see how our strategy will help you deliver cognitive-first applications. Read the full report here.
Mark Troester is the Vice President of Strategy at Progress. He guides the strategic go-to-market efforts for the Progress cognitive-first strategy. Mark has extensive experience in bringing application development and big data products to market. Previously, he led product marketing efforts at Sonatype, SAS and Progress DataDirect. Before these positions, Mark worked as a developer and developer manager for start-ups and enterprises alike.
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