Thanks to the efforts of the those who are leading the industry in these areas, I believe that the Age of Perception is just around the corner. Editors Note: Robotics Business Review would like to thank SLAMcore for permission to reprint the original article (found HERE).

For decades, data and analytics have played an important role in our economy. The process of analyzing data, however, remains labor intensive. Even with the most advanced techniques, data scientists spend countless hours developing, testing and retooling analytic models one step at a time. Worse yet, most organizations cannot find enough data scientists to complete this labor-intensive work. The impact is that we have not yet fully realized the promise of continuous intelligence; until now.

Customer experience initiatives are getting stuck with quite a few weighty labels lately: next key battleground, primary competitive differentiator, table stakes. And momentum for this topic is not slowing anytime soon. My ongoing personal survey of CMOs and industry analysts continues to land customer experience within the top three marketing and digital transformation priorities this year, as it did last year and the year before that. So it's no wonder companies are looking for every advantage to make their customer experience processes "smarter." Enter machine learning (ML) and cognitive computing.

Machine learning has simplified the heavy lifting of collecting and analyzing data. Even with the most advanced techniques, data scientists spend countless hours developing, testing and retooling analytic models one step at a time. Worse yet, most organizations cannot find enough data scientists to complete this labor-intensive work. The impact is that we have not yet fully realized the promise of continuous intelligence; until now. The field of machine learning offers promises to streamline the application of analytics and create a new era of autonomous data. It adds massive efficiencies to the process by automating the construction of these models.

For decades, data and analytics have played an important role in our economy. The process of analyzing data, however, remains labor intensive. Even with the most advanced techniques, data scientists spend countless hours developing, testing and retooling analytic models one step at a time. Worse yet, most organizations cannot find enough data scientists to complete this labor-intensive work. The impact is that we have not yet fully realized the promise of continuous intelligence; until now.