More organizations are using machine learning for competitive reasons, but their results are mixed. It turns out there are better -- and worse -- ways of approaching it. If you want to improve the outcome of your efforts in 2019, consider these points.

1. Start with an appropriate scope

Many organizations are so focused on the potential benefits of machine learning that they forget to consider the potential risks. In glossing over the details, it’s tempting to transform the entire business when it’s wiser to start with a use case or two, learn from the experience and build on the successes.

2. Approach machine learning holistically

Machine learning is often approached as a technical problem when it’s really a multi-disciplinary effort. While the details of machine learning can get quite technical, the goals tend to focus on business impact. In addition, machine learning may impact business processes, the company’s culture, and the organization’s operating model.

BCG considers technology only 10% of the machine learning equation. The rest is data work (20%) and change management (70%).

3. Make the connection between data and machine learning

People have been hearing about the importance of data for the last couple of decades, particularly as it relates to business intelligence and analytics. However, the data may also serve as training data for machine learning purposes, which means things like data quality and bias need to be taken very seriously.

There’s a huge amount of work that needs to be done because the data may not be machine-readable . Machine learning cannot be done without solving the data problems.

4. Don’t expect too much “out of the box”

Despite the fact there are no silver bullets, enterprises keep looking for them. The shortage of data science talent has resulted in a flurry of product and service announcements designed to make machine learning easier for a broader base of prospects including data scientists, developers and even business users. However, transformative results are not necessarily just a few clicks away.There are models and those algorithms may provide a 60 – 70% solution, but they have to be retrained. Understand that is critically important.”

5. Don’t forget infrastructural requirements

Machine learning involves several considerations, including speed, accuracy and ROI, all of which can be negatively impacted by infrastructural shortcomings such as the inability to store and process the requisite amount of data.

Some of the deep learning neural networks require tremendous amounts of computing power to train the systems. Hence , contemplating the desired end state and the machine learning architecture that will be required to achieve it and then as individual use cases are addressed, build toward those long-term goals to avoid creating problems that need to be fixed later.

Bottom line

Don’t “just do” machine learning, consider what you want to do with it first and why, and then start experimenting. While all the possibilities may have you thinking on a grand scale, it’s better to start small and grow, working toward longer-term goals while accomplishing shorter-term ones.

Taking a holistic approach to machine learning is a cross-functional effort that requires analyses and cooperation at both business and technical levels. Without that, machine learning efforts might be interesting, but less impactful and successful than intended.