February 15, 2017 | Written by: Rob Thomas
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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.
The field of machine learning promises to streamline the application of analytics and create a new era of autonomous data.
Instead of simply helping data scientists crunch the numbers, organizations will use machine learning to automatically understand and learn from data. The result will move us from the current state of predictive analytics, where organizations guess what will happen next, to cognitive business, where organizations develop a deep understanding of markets based on continuously updated data.
Think about the possibilities with continuous intelligence. Cars will not simply drive themselves; they will know where you want to go next. Grocery stores will not just cross-promote products; they will fill your cart before you enter the store. Doctors will not just write prescriptions; they will create holistic health plans based on data constantly updated from activity trackers, eating patterns and medical tests.
So how does machine learning make this possible?
Machine learning simplifies the heavy lifting of collecting and analyzing data. Today, sophisticated data analysis is largely confined to specialists who have mastered the right tools for building predictive models. In auto industry terms, it is like a craftsman building one car at a time. These analytic models produce great insights, but the insights are often delivered too late to act on or they are so general that they only provide a rough look at overall market trends.
Machine learning adds massive efficiencies to the process by automating the construction of these models. While the human mind can only comprehend a handful of data points at one time, computers can sort through millions of pieces of data to surface connections and present new scenarios. What’s more, machine learning systems improve over time by continuously updating models based on new data.
With machine learning, insights can be developed instantly and more people within an organization can take advantage of analytics because special skills will not be required. Analytics can be used more widely to improve services and products because the systems can handle larger amounts of data and find connections more quickly. Instead of making predictions for the masses, businesses can refine their models to tailor solutions for the individual.
Machine learning will only reach its full potential, though, by being infused where data resides. This means making it both a service for the public cloud and bringing it to private clouds, which protect the most important corporate data.
IBM is taking a major step toward making this a reality with the release of IBM Machine Learning, a new cognitive solution for creating, training and deploying analytic models on a private cloud. IBM has extracted the core machine learning technology from IBM Watson and will initially make it available where the world’s most valuable business data resides: the mainframe. As the core processing system for major banks, retailers, insurers and transport companies, the opportunities for transformation with machine learning on the mainframe are endless.
Machine Learning is just the beginning of what will be a dramatic shift that leads us into this new era of autonomous data. Once an organization is infused with continuous intelligence, it will be empowered with a new way of thinking and working, by data scientists and business leaders alike.
Just as the assembly line became the model for manufacturing, machine learning will become the model for data analysis and decision making. Machine learning is to the 21st century what the Industrial Revolution was to the 18th century. The time is now. Welcome to the era of autonomous data.