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.

In his keynote at the recent AWS re:Invent conference, Amazon vice president and chief technology officer Werner Vogels said that the cloud had created a "egalitarian" computing environment where everyone has access to the same compute, storage, and analytics, and that the real differentiator for enterprises will be the data they generate, and more importantly, the value the enterprises derive from that data. For Rob Thomas, general manager of IBM Analytics, data is the focus. The company is putting considerable muscle behind data analytics, machine learning, and what it calls more generally cognitive computing, much of it based on its Watson technology. That includes the Watson Data Platform and its Data Catalog, Data Refinery and Analytics Engine. But when it comes to data analytics, Thomas takes what's been called an "attitude before aptitude" approach, with the idea being that enterprises need to create a "culture of data" before they can take full advantage of analytics. They need to have in place a belief that data and facts are what's important when making business decisions rather than instinct, beliefs and what's been done in the past. And it's an approach that's got to come from the top and become part of how the business operates.

Business dynamics are evolving with every passing second. There is no doubt that the competition in today's business world is much more intense than it was a decade ago. Companies are fighting to hold on to any advantages. Digitalization and the introduction of machine learning into day-to-day business processes have created a prominent structural shift in the last decade. The algorithms have continuously improved and developed.

Business dynamics are evolving with every passing second. There is no doubt that the competition in today's business world is much more intense than it was a decade ago. Companies are fighting to hold on to any advantages. Digitalization and the introduction of machine learning into day-to-day business processes have created a prominent structural shift in the last decade. The algorithms have continuously improved and developed.

Business dynamics are evolving with every passing second. There is no doubt that the competition in today's business world is much more intense than it was a decade ago. Companies are fighting to hold on to any advantages. Digitalization and the introduction of machine learning into day-to-day business processes have created a prominent structural shift in the last decade. The algorithms have continuously improved and developed.

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.

Co-authored by Jean-Francois Puget (@JFPuget) Machine Learning represents the new frontier in analytics, and is the answer of how many companies can capitalize on the data opportunity. Machine Learning was first defined by Arthur Samuel in 1959 as a "Field of study that gives computers the ability to learn without being explicitly programmed." Said another way, this is the automation of analytics, so that it can be applied at scale. What is highly manual today (think about an analyst combing thousand line spreadsheets), becomes automatic tomorrow (an easy button) through technology. If Machine Learning was first defined in 1959, why is this now the time to seize the opportunity?