Eight Reasons Machine Learning Isn’t Mainstream in the Enterprise

Machine learning has made huge strides in recent years. It’s helped Netflix perfect binge watching, taught Siri how to sound more human and matched people’s selfies with famous pieces of art. But when it comes to machine learning use cases for the enterprise, it gets a whole lot more complicated. It’s easy to apply an algorithm to a one-off use case, but comprehensive enterprise applications of machine learning don’t exist today. In this eWEEK slide show, J.F. Huard, CTO of Data Science at AppDynamics, outlines the top eight challenges standing in the way of widespread adoption of machine learning in the enterprise.

Part of the problem is a lack of understanding around what machine learning is. Machine learning is really about applying mathematics to different domains. It locates meaning within extremely large volumes of data by canceling out the noise.

Machine learning algorithms don’t enter chess tournaments. What they are really good at is adapting to changing systems without human intervention while continuing to differentiate between expected and anomalous behavior. This makes machine learning useful in all kinds of applications—think everything from security to health care—as well as classification and recommendation engines and voice and image identification systems. 

Machine learning is usually introduced into an enterprise in one of two ways. The first is that one or two employees start applying machine learning to gain insight into data they already have access to. The second is by purchasing a solution, such as security software or an application performance management solution, that uses machine learning. 

Machine learning isn’t as easy as simply collecting data and running it through some algorithms. Once you collect the data, then you have to aggregate it, determine if there are any problems with it and make sure it’s able to adapt to missing data, outlying data, garbage data and data that’s out of sequence.

The availability of publicly labeled datasets would make it much easier for companies to get started with machine learning. Unfortunately, these do not yet exist, and without them, most companies are looking at a “cold start.” 

At its best, machine learning represents the perfect marriage between an algorithm and a problem. This means domain knowledge is a prerequisite for effective machine learning, but there is no off-the-shelf way to obtain domain knowledge. It is built up in organizations over time and includes not just the inner workings of specific companies and industries but the IT systems they use and the data that is generated by them. 

Most data scientists are mathematicians. Depending on their previous job experience, they may have zero domain knowledge that is relevant to their employer’s business. They need to be paired up with analysts and domain experts, which increases the cost of any machine learning project.

One of the challenges encountered by organizations with machine learning initiatives is the lack of conventions around communicating findings. They end up with silos of people, each with their own definition of input and their own approach to sampling data. Consequently, they end up with wildly different results. This makes it difficult to inspire confidence in machine learning initiatives and will slow adoption until it is addressed.