If you’ve ever paid someone with PayPal, watched a movie recommended by Netflix or misspelled a word in a search engine and received the correct results anyway—you’ve benefited from machine learning. For years, forward thinking businesses have been exploring new ways to harness machine learning to improve the ways they serve their customers. Should your organization join them?
What is machine learning, exactly?
Before we dive into machine learning and the benefits to your business, let’s briefly cover what machine learning is. In practice, machine learning is simply about understanding data and statistics. And loosely speaking, it’s a process where computer algorithms find patterns in data, then predict the probable outcomes. This is how your email program can scan the messages you receive and determine whether a particular email is SPAM or not depending on words in the subject line, the links included in the message or patterns identified by looking at a list of recipients.
What makes machine learning really useful is that the algorithm can “learn” and adapt its outputs based on new information. Which means that when spammers change tactics, the machine will quickly pick up on the new patterns and again correctly identify dubious messages as SPAM.
How organizations use machine learning
However, machine learning goes well beyond what’s listed above. It can be used to predict transportation traffic patterns, outbreaks of disease, stocks and commodities or hardware failures or spikes in web traffic—all of this so you and your organization can plan and react accordingly.
Challenges of machine learning
As exciting as all these uses are, there are challenges with implementing machine learning in any organization. The first is simply understanding what kind of algorithm to use for the problem you need to solve. A clustering algorithm could be used to classify a restaurant customer as more likely to dine in than take out, but it can’t be used to predict how raising menu prices would impact sales. Likewise, a regression algorithm would be able to address the effect of price changes on sales, but can’t predict one of a closed set of outcomes.
There’s also a risk of “overfitting” the data, which is simply training the system to understand a set of data so well that it loses the ability to generalize, learn, and make predictions based on new data. In this case, the model tends to make inconsistent predictions and becomes worthless.
In addition, some problems may not be solvable with machine learning. Unfortunately, you can’t always predict which can be solved, so the process of applying machine learning to the data never ends, leading an organization to chase the problem but never develop a functional model. In this case, the solution is knowing when to quit trying.
Should your organization adopt machine learning?
When implemented correctly, machine learning can help you solve enormous problems and predict user behavior in ways that will help your organization grow. As you know, organizations are drowning in data, today. And yours is probably no different—everything from web analytics, customer demographics and usage information to purchase behavior, pricing, inventory and delivery systems—all of it impacts customer behavior and organizational growth. And even if you have a data scientist on staff, analyzing and understanding what it all means is next to impossible.
Fortunately, the massive increases in computing power and cloud-based server capacity in the past 20 years make it possible for machines to analyze the data, make helpful predictions, then learn and adjust based on how accurate the initial predictions are. As more data is analyzed by the system, the prediction model improves.
So if you can use machine learning to understand data and make predictions that will help your organization grow, why not do it? To make a good machine learning system for your business, you need four things:
1. An understanding of the machine learing process
2. An understanding of the different algorithms available and the kinds of problems to which they can be applied
3. Data (the more, the better)
Feel free to contact E-SPIN for machine learning infrastructure and application security, infrastructure availability and performance monitoring solution.
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