Digital transformation: How machine learning could help change business

Machine learning (ML) based data analytics is rewriting the rules for how enterprises handle data. Research into machine learning and analytics is already yielding success in turning vast amounts of data—shaped with the help of data scientists—into analytical rules that can spot things that would escape human analysis in the past—whether it be in pursuit of pushing forward genome research or predicting problems with complex machinery.

Now machine learning is beginning to move into the business world. But most organizations haven’t truly grasped how machine learning will change the way they do business—or how it will change the shape of their organizations in the process. Companies are looking to ML to automate processes or to augment humans by assisting them in data-driven tasks. And it’s possible that ML could turn enterprises into vendors—turning lessons learned from their own vast stores of data into algorithms they can license to software and service providers.

But getting there will depend on how machine learning capabilities evolve over the next five years and what implications that evolution has for today’s long-time hiring/recruitment strategies. And nowhere is this more crucial than in unsupervised machine learning, where systems are given vast datasets and told to find the patterns without humans having first figured out what the software needs to look for. With minimal pre-task human efforts needed, the scalability of unsupervised machine learning is much higher.

David Dittman, director of business intelligence and analytics services at Procter & Gamble, explained that the biggest analytics problem he sees today with other large US companies is that “they are becoming enamored by [machine learning and analytics] technology, while not understanding that they have to build the foundation [for it], because it can be hard, expensive and requires vision.” Instead, Dittman said, companies mistakenly believe that machine learning will reveal the vision for them: “‘Can’t I have artificial intelligence just tell me the answer?'”

The problem is that “artificial intelligence” doesn’t really work that way. ML currently falls into two broad categories: supervised and unsupervised. And neither of these works without having a solid data foundation.

Supervised ML requires humans to create sets of training data and validate the results of the training. Speech recognition is a prime example of this, explained Yisong Yue, assistant professor of computing and mathematics at Caltech. “Speech recognition is trained in a highly supervised way,” said Yue. “You start with gigantic data—asking people to say certain sentences.”

But collecting and classifying enough data for supervised training can be challenging, Yue said. “Imagine how expensive that is, to say all these sentences in a range of ways. [Data scientists] are annotating this stuff left and right. That simply isn’t scalable to every task that you want to solve. There is a fundamental limit to supervised ML.”

Unsupervised machine learning reduces that interaction. The data scientist chooses a presumably massive dataset and essentially tells the software to find the patterns within it, all without humans having to first figure out what the software needs to look for. With minimal pre-task human efforts needed, the scalability of unsupervised ML (particularly in terms of the human workload upfront) is much higher. But the term “unsupervised” can be misleading. A data scientist needs to choose the data to be examined.

Unsupervised ML software is asked to “find clusters of data that may be interesting, and a human analyzes [those groupings] and decides what to do next,” said Mike Gualtieri, Forrester Research’s vice president and principal analyst for advanced analytics and machine learning. Human analysis is still required to make sense of the groupings of data the software creates.

But the payoffs of unsupervised ML could be much broader.