Case in point: Facebook, which of course began as a desktop app, struggled to keep up with emerging mobile-first experiences like Instagram and WhatsApp, and ended up acquiring them for billions of dollars to play catch up.
The Predictive-First Revolution
Recent events like the acquisition of RelateIQ by Salesforce demonstrate that we’re at the beginning of another shift toward a new age of predictive-first applications. The value of data science and predictive analytics has been proven again and again in the consumer landscape by products like Siri, Waze and Pandora.
Big consumer brands are going even deeper, investing in artificial intelligence (AI) models such as “deep learning.” Earlier this year, Google spent $400 million to snap up AI company DeepMind, and just a few weeks ago, Twitter bought another sophisticated machine-learning startup called MadBits. Even Microsoft is jumping on the bandwagon, with claims that its “Project Adam” network is faster than the leading AI system, Google Brain, and that its Cortana virtual personal assistant is smarter than Apple’s Siri.
The battle for the best data science is clearly underway. Expect even more data-intelligent applications to emerge beyond the ones you use every day like Google web search. In fact, this shift is long overdue for enterprise software.
Predictive-first developers are well poised to overtake the incumbents because predictive apps enable people to work smarter and reduce their workloads even more dramatically than last decade’s basic data bookkeeping approaches to customer relationship management, enterprise resource planning and human resources systems.
Look at how Bluenose is using predictive analytics to help companies engage at-risk customers and identify drivers of churn, how Stripe’s payments solution is leveraging machine learning to detect fraud, or how Gild is mining big data to help companies identify the best talent.
These products are revolutionizing how companies operate by using machine learning and predictive modeling techniques to factor in thousands of signals about whatever problem a business is trying to solve, and feeding that insight directly into day-to-day decision workflows. But predictive technologies aren’t the kind of tools you can just add later. Developers can’t bolt predictive onto CRM, marketing automation, applicant tracking, or payroll platforms after the fact. You need to think predictive from day one to fully reap the benefits.
Recent events like the acquisition of RelateIQ by Salesforce demonstrate that we’re at the beginning of another shift toward a new age of predictive-first applications. The value of data science and predictive analytics has been proven again and again in the consumer landscape by products like Siri, Waze and Pandora.
Big consumer brands are going even deeper, investing in artificial intelligence (AI) models such as “deep learning.” Earlier this year, Google spent $400 million to snap up AI company DeepMind, and just a few weeks ago, Twitter bought another sophisticated machine-learning startup called MadBits. Even Microsoft is jumping on the bandwagon, with claims that its “Project Adam” network is faster than the leading AI system, Google Brain, and that its Cortana virtual personal assistant is smarter than Apple’s Siri.
The battle for the best data science is clearly underway. Expect even more data-intelligent applications to emerge beyond the ones you use every day like Google web search. In fact, this shift is long overdue for enterprise software.
Predictive-first developers are well poised to overtake the incumbents because predictive apps enable people to work smarter and reduce their workloads even more dramatically than last decade’s basic data bookkeeping approaches to customer relationship management, enterprise resource planning and human resources systems.
Look at how Bluenose is using predictive analytics to help companies engage at-risk customers and identify drivers of churn, how Stripe’s payments solution is leveraging machine learning to detect fraud, or how Gild is mining big data to help companies identify the best talent.
These products are revolutionizing how companies operate by using machine learning and predictive modeling techniques to factor in thousands of signals about whatever problem a business is trying to solve, and feeding that insight directly into day-to-day decision workflows. But predictive technologies aren’t the kind of tools you can just add later. Developers can’t bolt predictive onto CRM, marketing automation, applicant tracking, or payroll platforms after the fact. You need to think predictive from day one to fully reap the benefits.
by Vik Singh, TechCrunch | Read more:
Image: Holger Neimann
