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Machine Learning Steps into the Limelight

October 26, 2017


Machine learning (ML) is a subset of artificial intelligence (AI). In recent years, however, ML has taken center stage in the business world. Bernard Marr (@BernardMarr), a strategic performance consultant, writes, “At its most simple, machine learning is about teaching computers to learn in the same way we do, by interpreting data from the world around us, classifying it and learning from its successes and failures. In fact, machine learning is a subset, or better, the leading edge of artificial intelligence.”[1] David Teich (@Teich_Comm), a Senior Analyst at TIRIAS Research, believes machine learning is more than “the leading edge of artificial intelligence.” He explains, “Complex analytics are able to discover data, run far more complex and advanced mathematical analysis than in previous decades and provide information visualizations that are informative and stunning. It took me a while to wrap my head around it, given my earlier AI biases, but I’ve concluded that machine learning is now its own discipline.”[2]


The Many Faces of Machine Learning


Jeff Bodenstab writes, “Machine learning relies heavily on the availability of data and computing power. Rather than make a priori assumptions, machine learning enables the system to learn from data. Rather than following preprogrammed algorithms, it uses the data to build and constantly refine a model for making predictions. It helps understand demand volatility by capturing and modeling attributes that shape the demand. It learns from the data, and modifies operations accordingly.”[3] To some people, machine learning sounds like magic. Martin Willcox (@Willcoxmnk) and Frank Säuberlich (@FrankSauberlich), executives with Teradata, caution, “You can’t ‘machine learn everything’ — and even if you could, there would still be quicker routes to solve some problems. The most successful data-driven organizations tend to think first in terms of the business problem that they are trying to solve; second about the data that are — or that could be — available to solve it; and only then about the methods, techniques, algorithms and technology that they should employ.”[4] In a follow-on article, Willcox and Säuberlich explain there a number of different ML approaches.[5] They write:

“Machine learning algorithms mainly fall into two categories: supervised learning algorithms and unsupervised learning algorithms (for the purposes of simplicity, we will ignore additional categories like semi-supervised learning and reinforcement learning). There are many algorithms available in each of these categories that can be used for either prediction/classification (in the case of supervised learning) or clustering/segmentation (in case of unsupervised learning). With supervised learning, labeled data from the past is used to train a model that can then be used to predict future, similar events. If the label is a continuous variable (e.g., the revenue of a certain product or the number of products sold) algorithms like regression, special decision trees, random forests or neural networks can be used. If the label is a categorical variable (e.g., true or false), techniques like logistic regression, naïve Bayes classifiers, decision trees or the k-nearest neighbor algorithm are useful. Unsupervised learning, on the other hand, operates on unlabeled data. Typically, we use unsupervised methods to identify structures and patterns in data that we didn’t know existed before — a process that is often termed ‘discovery analytics’. If the input data is numerical, the most common set of techniques is cluster analysis. If we are looking at categorical inputs, algorithms like association or affinity analysis can be used, for example, to discover which products are frequently bought in combination with one another in the course of different shopping missions. But how do we decide which of these algorithms is most useful for a given problem? The answer is to start with the problem — or rather with the business question that we want to answer through the application of machine learning. What is it that we want to achieve? And how will we measure the success — or otherwise — of our analysis?”

Trying to decide which model to use can often be difficult. Massive Dynamics™, a partner of Enterra Solutions®, has developed a Representational Learning Machine™ (RLM) that helps select the right model for the problem at hand and the data available. Additionally, the RLM operates in a “glass box” fashion. In traditional machine learning, predictions are generated in “black box” fashion. The RLM allows users to see through the system and understand the drivers and the foundation for any given prediction. The point I’m trying to make is that there are now a number of ML approaches that can be used to help businesses solve problems.


Business and Machine Learning


SAP executives, Dan Wellers (@DanielWellers), Timo Elliott (@timoelliott), and Markus Noga (@mlnoga), note, “Today’s leading organizations are using machine learning–based tools to automate decision processes, and they’re starting to experiment with more-advanced uses of artificial intelligence for digital transformation.”[6] They list 8 ways organizations are already leveraging machine learning. They are:


1. Personalizing customer service. “By combining historical customer service data, natural language processing, and algorithms that continuously learn from interactions, customers can ask questions and get high-quality answers.”


2. Improving customer loyalty and retention. “Companies can mine customer actions, transactions, and social sentiment data to identify customers who are at high risk of leaving.”


3. Hiring the right people. “Software quickly sifts through thousands of job applications and shortlists candidates who have the credentials that are most likely to achieve success at the company.


4. Automating finance. “AI can expedite ‘exception handling’ in many financial processes. … This lets organizations reduce the amount of work outsourced to service centers and frees up finance staff to focus on strategic tasks.”


5. Measuring brand exposure. “Automated programs can recognize products, people, logos, and more. … Corporate sponsors get to see the return on investment of their sponsorship investment with detailed analyses, including the quantity, duration, and placement of corporate logos.”


6. Detecting fraud. “By building models based on historical transactions, social network information, and other external sources of data, machine learning algorithms can use pattern recognition to spot anomalies, exceptions, and outliers.”


7. Predictive maintenance. Machine learning can detect patterns and anomalies indicating equipment is about to fail.


8. Smoother supply chains. “Machine learning enables contextual analysis of logistics data to predict and mitigate supply chain risks.”


Contextual analysis is becoming so important Chris Stone, a tech industry veteran, predicts, “Organizations will soon realize … applying machine learning to content — physical documents, images, presentations and even conversational UIs — removes the cap on who machine learning impacts, and how far its value extends across the enterprise.”[7]




Stone concludes, “As words and phrases start to fuel machine learning algorithms, each and every member of an organization can reap the benefits. More often than not, these benefits will manifest in ways we never thought possible.” Teich agrees. He writes, “Today, we’re looking at machine learning, our AI child, growing up. Machine learning is stepping out on its own, with two key parents [AI and Natural Language Processing]. Expect to see it continue to grow.”


[1] Bernard Marr, “What Is Machine Learning – A Complete Beginner’s Guide In 2017,” Forbes, 4 May 2017.
[2] David Teich, “Machine Learning: The Evolution From An Artificial Intelligence Subset To Its Own Domain,” Forbes, 20 September 2017.
[3] Jeff Bodenstab, “Traditional Statistics versus Machine Learning. What’s the Difference?” ToolsGroup, 19 September 2017.
[4] Martin Willcox and Frank Säuberlich, “No, you can’t machine learn everything,” Teradata, 2017.
[5] Martin Willcox and Frank Säuberlich, “Occam’s razor and machine learning,” Teradata, 2017.
[6] Dan Wellers, Timo Elliott, and Markus Noga, “8 Ways Machine Learning Is Improving Companies’ Work Processes,” Harvard Business Review, 31 May 2017.
[7] Chris Stone, “Why the future of machine learning will be crunching words,” CIO, 12 September 2017.

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