Читать книгу Self-Service Data Analytics and Governance for Managers - Nathan E. Myers - Страница 28

Machine learning (ML) is the subset of artificial intelligence (AI) that is focused on the study of computer algorithms that improve automatically through experience. Machine learning algorithms build a mathematical model based on samples of data observations or training data, to make decisions or predictions, without being explicitly programmed to do so. Above, we introduced RPA, which relies on very regimented coding of specific operations, depending on explicit variables. With machine learning, a number of samples are analyzed to understand the relationships of inputs and to determine how outcomes are derived. The more training data that is pumped through the model, the better the algorithm should get at predicting the “right” answer. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms or code to predict and specify needed tasks.

This morning, one of your authors arose an hour earlier than normal to get in a jog on this pleasant June day. As he pulled the phone off the charger, a message on the screen read that the phone was scheduled to be fully charged at 5:45am. The change in schedule had clearly thrown off the charging fairy, as the battery power read only 94% charged. One hour and a good jog later, the author then launched his thermostat app to turn down the AC, before getting in the car to begin the commute. Once in the car, the same phone displayed a message indicating that there was light traffic to the train station, a journey which was predicted to take 8 minutes. A prompt came up to allow for quick directions to the train station, which was hastily declined. Afterall, the authors are brilliant not only with data analytics and governance books, but with getting themselves to the train station, even without the benefit of GPS-assisted instructions.

It is easy to see machine learning in action, in just the first 90 minutes of the day. It is not exactly clear which of the author's observed activities or features were used to trigger his phone to make suggestions, but it is clear that routine daily actions observed and logged over time had served as training data and had ultimately resulted in a number of predictions about subsequent activities or labels. Clearly, the phone knew the time the alarm was set for, at what time the commute begins, and where your author's car is left for the day.

There is a scale of maturity for machine learning capabilities, beginning with descriptive analytics to look at what has happened in the past with data aggregation and mining, moving forward to diagnostic analytics, to understand the drivers of the target outcomes. Moving further along the continuum, we get to predictive analytics to help us to project from past observations what will happen in the future, based on statistical forecasting models, and on to prescriptive analytics, which uses optimization and simulation algorithms to advise on possible outcomes and to determine what actions should be taken. In the phone example above, the machine learning model is far out to the right, even approaching prescriptive analytics. The model was able to predict the next actions and to prescribe what to do about them – launch the driving directions app, as you are in the car and headed to the train station!

It is likely that analysts will encounter less mature models, where they would be pleased just to draw correlations that are difficult to uncover with simplistic traditional analysis tools. The analyst may be looking for descriptions of the Xs observed in the case of Y outcomes, or perhaps explanations of the Ys from observed Xs. However, it is thought that the true value can come in an algorithm understanding a large number of observations such that it can make predictions about the future. Taking it a step further, by tying the predicted outcomes to prescribed action steps, we approach true artificial intelligence, enabling us to best deal with encountered scenarios in a data-driven and methodical way.