5 Things Companies Should Know About Machine Learning

Machine learning used to be the sort of thing more at home in sci-fi novels than real life.

It’s now a driving force behind some of the most iconic business success stories in the here and now—and that trend is likely to continue going forward. From product recommendations on retail websites to factory predictive maintenance, machine learning is transforming the competitive environment for businesses in all sectors.

Businesses need to identify their weaknesses and strengths. Machine learning is not a plug-and-play innovation. It’s a robust tool, and it’s worth investing the time to discover what machine learning can and can’t do for your company.

Here are five things that businesses should understand about machine learning.

1. Machine Learning Is Only as Good as Your Data

Machine learning algorithms learn patterns and make decisions based on the input received. That means that the quality, quantity, and relevance of your data will dictate the performance of any machine learning model.

Faulty, biased, or incomplete data will lead to poor predictions, wrong impressions, or even ethical issues. For instance, if your training data is less diverse than your users, your machine learning models will mirror bias. This can manifest as suggesting incorrect products or flagging users as suspicious more often.

You must verify their existing data to keep it complete and up-to-date, and adhere to data privacy laws.

^{Read more: Three Key Steps to Prepare for Data and AI Leadership}

2.  Define Your Business Goals First

It is a misconception that machine learning is a silver bullet that’ll figure things out and solve all business problems. Successful machine learning projects in the real world rely on good objectives. You need to identify them to get good machine learning outcomes.

Do you need to reduce churn, price optimally, detect fraud, or forecast inventory needs? All of those are achievable with machine learning, but the problem must be stated in a form that can be solved with patterns in the available data.

Without a well-defined goal, machine learning projects can get derailed or fail to yield a significant return on investment.

3. You Need the Right Talent or the Right Partner

Building machine learning systems is different from building websites or developing mobile applications.

Data science, statistics, software engineering, and domain expertise are required. So, if you don’t have the right skillset in-house, you need to hire a professional experienced in providing machine learning solutions.

4. Model Explainability and Transparency Are Critical

Machine learning models range from simple-to-understand linear regressions to complex deep neural networks. 

You need machine learning results that are not only accurate but also explainable. That’s particularly critical in highly regulated industries like finance, healthcare, and insurance.

5. Machine Learning Needs Ongoing Monitoring and Maintenance

Machine learning models aren’t one-and-done in nature. With your data, users, or business environment changing, models can degrade over time. It’s something known as mode drift.

Best practices include rerunning models against new data on a recurring schedule, monitoring model performance on dashboards in real time, and taking notice when predictions stray into unacceptable regions.

Just like with any other corporate asset, machine learning models need to be finely adjusted from time to time to remain effective.

^{Supplementary reading: AI Authenticity: Staying Human in a Machine-Made World}

Machine learning is transforming industries and altering the way businesses make decisions. But it doesn't just require embracing new technology—it requires vision for what's next, ethics, and a passion for data-driven processes and procedures.

Data quality, goal-setting, talent requirements, explainability, maintenance, and ethics are vital considerations. If your business is considering venturing into machine learning, go small, be open, and build for purpose. The benefits are vast for companies that do it right.

^{Leaderonomics.com is an advertisement-free website. Your continuous support and trust in us allow us to curate, deliver, and maintain our website. When you support us, you enable millions to continue reading for free on our website. Will you give it today? Click here to support us.}