Machine Learning: Is it all Hype?

by HSG on May 17, 2018 in

Let’s face it, fad or not, companies are starting to ask themselves how they could possibly use machine learning and AI technologies in their organization. Many are being lured by the promise of profits by discovering winning patterns with algorithms that will enable solid predictions… The reality is that most technology and business professionals do not have sufficient understanding of how machine learning works and where it can be applied.  For a lot of firms, the focus still tends to be on small-scale changes instead of focusing on what really matters…tackling their approach to machine learning.

In the recent Wall Street Journal article, Machine Learning at Scale Remains Elusive for Many Firms, Steven Norton captures interesting comments from the industry’s data science experts. In the article, he quotes panelists from the MIT Digital Economy Conference in NYC, on businesses current practices with AI and machine learning. All agree on the fact that, for all the talk of Machine Learning and AI’s potential in the enterprise, many firms aren’t yet equipped to take advantage of it fully.

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Panelist,  Michael Chui, partner at McKinsey Global Institute states that “If a company just mechanically says OK, I’ll automate this little activity here and this little activity there, rather than re-thinking the entire process and how it can be enabled by technology, they usually get very little value out of it. “Few companies have deployed these technologies in a core business process or at scale.”

Panelist, Hilary Mason, general manager at Cloudera Inc., had this to say, “With very few exceptions, every company we work with wants to start with a cost-savings application of automation.” “Most organizations are not set up to do this well.”

No doubt there have been striking breakthroughs in machine learning in recent years and some of the most powerful institutions are leveraging the technology to match or surpass human-level performance. However, we are still very far from matching human intelligence across the board, thereby raising the question: “where does machine learning work best and where is it ineffective?”

With very few exceptions, the main feature companies look for is improving existing processes and predictive analysis based on years of data that has always been available but minimally used. Small enhancements to forecasting, for example, can generate big improvements to the bottom line. But how can you know which problems in your business are amenable to machine learning?

In her Harvard Business Review article, How to Tell If Machine Learning Can Solve Your Business Problem, Anastassia Fedyk poses the question, “Is your problem the kind of problem where getting things right 80% of the time is enough? Can you deal with a 10% error rate? 5%? 1%? Are there certain kinds of errors that should never be allowed?” Take, for example, the machine learning algorithms for predicting mortality rates from pneumonia mentioned in the article.

“The algorithms recommended that hospitals send home pneumonia patients who were also asthma sufferers, estimating their risk of death from pneumonia to be lower. It turned out that the dataset fed into the algorithms did not account for the fact that asthma sufferers had been immediately sent to intensive care, and had fared better only due to the additional attention.”

There are many reasons for high failure rates in analytics projects. According to Gartner, more than half of all analytics projects fail because they aren’t completed within budget or on schedule, or because they fail to deliver the features and benefits originally agreed upon.  The biggest reason though is that companies still treat these projects as just another IT project. Big data analytics is neither a product nor a computer system. Instead, senior managers should carefully consider it as an evolving strategy, vision and architecture and align the organization’s operations with its strategic business goals.  Hence, rather than hiring a bunch of smart data scientists and letting them loose with the company’s data, the wiser approach is to have a deep understanding of the problem to be solved and support the analysts that understand business risks as well as system engineering who can make improvements in processes.

Finding a great data scientist or consultant involves finding someone who has somewhat contradictory skill sets. LinkedIn lists the following as part of the top ten technical skillsets for data scientists: Machine Learning, R, Python, Data Mining, Data Analysis, Data Science, SQL, MatLab, Big Data, and Statistical Modeling. More importantly, the person(s) in charge of implementing changes in your business should have an intuitive understanding of the business problem you are trying to solve as well as be able to handle data processing and be able to create useful models.

As far as potentially executing machine learning in your business or simply addressing core business needs in other ways, begin simply with traditional statistics and clean data. From there, you can start to consider if it is worth consulting with someone in the industry who can help you put together solutions such as implementing a system for managing tasks, information recovery, data storage, customized reporting software and identifying competitive advantages.


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