Why it’s time to consider bringing machine learning to your business

For years, machine learning — along with big data, data analytics, visualization, and artificial intelligence (AI) — has been at the forefront of discussions about how managers can make informed decisions or improve and automate business processes.

While the technology for machine learning has existed since the 1980s, the breakout of machine learning, and its benefit for businesses, came about in 2006. Since then, the technology has matured to the point where incorporating machine learning into a business has never been easier or yielded greater returns.

Indeed, machine learning has become so ubiquitous in certain areas that choosing not to incorporate it into your operations could make it harder for your company to stay competitive.

What is machine learning?

Machine learning is often seen as a subset of AI, and it overlaps with statistical learning; both attempt to make predictions by finding and learning from patterns and trends within datasets.

However, machine learning uses mathematical models based on passive data sampling to make predictions or decisions automatically — that is, without being explicitly programmed to do so. Generally, it could be considered whenever it isn’t feasible to accomplish the same objective with discrete conventional rules or programming algorithms.

Finding a place for machine learning in your business

There’s a common misconception that certain companies or use cases are too small, or not complex enough, to benefit from machine learning. However, the contrary — even for single-owner operations — is more often true.

A large staff of PhDs and data scientists are no longer required to employ machine learning. Even though the underlying technology and math is complex, companies don’t have to start from scratch when implementing this strategy thanks to recent improvements in data storage, computing power, and software.

Open-source algorithms and tools are widely available, and companies like Microsoft, Amazon, and Domo offer these tools as part of their cloud-based services. The availability of these tools makes machine learning well-suited to analyses and the improvement of routine processes for smaller companies.

Example

Hershey licorice machines had a tendency to run too hot. This caused the company to overproduce licorice at a rate of 100 grams per minute, all of which ended up being given away or considered waste.

By adding sensors — linked to Microsoft’s Azure Machine Learning — that could better respond to production changes and regulate the machine’s temperature, Hershey was able to reduce its wasteful licorice production from 100 to 25 grams per minute.

Without data scientists, Hershey was able to use Azure Machine Learning to predict machine operating temperatures and enable production changes;  the result was a reduction in waste and an increase in cost savings as reported by PC Magazine on April 28, 2017.

Businesses benefits

Basic machine-learning algorithms can identify correlations and make recommendations by learning from transaction histories. They can also make experiences more contextual and personalized by testing and developing better social media ads and automating and improving customer service requests.

Customer-support logs can also provide insight into customer issues including the amount of time it takes to repair a problem. Data may also be stored in text that indicates what customers are looking for in the future.

Other uses

Financial companies use machine learning in a variety of other ways including:

• Robot advisory retirement planning
• Underwriting
• Credit scoring
• Algorithmic trading

Human resources departments leverage machine learning for recruiting and retaining top talent while smart assistants — like Google’s Mini and Amazon’s Alexa — use it to provide answers and suggestions.

Next steps

Machine learning has the potential to help you look beyond obvious issues and anticipate future changes your business will need. Incorporating a machine learning algorithm in to operations is an iterative process; data changes, preferences evolve, and competitors will emerge. When implementing machine learning in to your operations, it’s critical to consider three key items:

1. What problems need solving, and how can machine learning help solve them?
2. What’s the end goal of implementing machine-learning software?
3. How will deployment of the system be managed?

With these questions in mind, you can begin the processes of strategically integrating machine learning capabilities into your organizational operations.