IT Innovation | February 28, 2018

3 Tips for Success with Machine Learning

By: Sherry Tiao


Machine learning is the big buzzword right now. If you’d like to learn more about it, check out our blog series on machine learning techniques. If you’re ready to use your big data for machine learning and start a project, this is the blog post for you.

We talked to Brian Macdonald, data scientist on Oracle’s Information Management Platform Team, about tips for success for your machine learning project.

1. Identify a Specific Business Problem

We most often see success with machine learning projects when the company has a very specific business problem and they’re willing to do anything to solve that problem.

“Now one of the companies I worked with wanted to create a better churn model because they were losing customers rapidly, which was affecting profits. They had a lot of data, and they believed they could use machine learning to help them identify the customers who were about to leave. If you have a $100 million problem, spending $30 million isn’t a big deal.”

That’s the kind of company that tends to see success. Having that laser focus on a solution helps ensure success because there's no other option. 

When you’re going into your project, you need to keep the end goal in mind at all times.

2. Gain Executive Support

This is related to the first tip. But the main reason for success is having high-level executives who support your machine learning initiative. You want a C-suite that says machine learning analytics and data are important to your business. If you have that support and vision, your program is much more likely to be successful.

On the other side, when your program is driven by IT, what you tend to hear is something like, “We’ve never done this internally and I don’t know how to sell it.” This type of approach is less likely to be successful, not because the technology isn’t working, but because of internal issues.

For example, because machine learning is in essence automated decision-making, sometimes people can view it as a means to replacing their own jobs. If employees at your company are worried about replacing jobs or lowering the headcount they, you’re not going to get a reception that’s as strong. And keeping this in mind is important, because then you can decide how to counter this kind of attitude.

But that’s another reason why having executive support is so important. It becomes a way to go around that attitude, more easily. 

In essence, you often need backing and money to make a machine learning initiative a true success.

3. Identify Short-Term, Measurable Business Benefits

When you’re starting out, you’ll want to start with a concrete business benefit like increasing sales. That’s an example of a business benefit that’s tangible, that everyone can see, and which won’t take too long to identify. The length of time it will take really depends on your goal, but it should be less than a year.

If you don’t have a business value that’s measurable, question why you’re doing this because at some point you’re going to have to justify your project.

Some people might say things like, “We think machine learning is the future” or “We need to develop those skills.” Well, that’s investing in building skills and R&D for the future, and that’s a business benefit. However, whether you have the assets to spend on that kind of research really depends on your company size and corporate strategy, and you should really try to align with that before you start.

Real-Life Machine Learning and Big Data

Here at Oracle, we’ve been fortunate enough to see many success stories with machine learning. But here’s one example from the energy industry that stands out.

This company is a leading supplier of systems for power generation and transmission, and is one of the world's largest producers of energy-efficient, resource-saving technologies.

Business challenge

  • Using data to potentially predict future failures in power generation units. These predictions can then be used to sell services to their customers, who are the owners of those units.

Technology challenge

  • The company wanted to differentiate themselves from their competitors by making the power generation equipment better serve their customers. One approach they’re taking is to use the data from the power generation units to predict future failures and help customers improve maintenance schedules to eliminate outages and costly expenses.

This company purchased Oracle Advanced Analytics, which is also available in the cloud and part of the Autonomous Data Warehouse, to help it add predictive modeling capabilities to the services it offers to customers. 

The company was successful in large part because it was so focused.

The company had a very specific business problem, got their executives behind the goal, and identified short-term, measurable business benefits. There’s another item you might want to add to that list: purchasing the right machine learning technology, which can often contribute greatly to the success of your project.

Choose carefully and wisely, and contact us if you’re interested in our machine learning capabilities. We're here to help you make your machine learning project successful. 

And, if you'd like to try building a data lake and use machine learning on the data, Oracle offers a free trial. Register today to see what you can do.


At Oracle, Sherry explores the realm of big data. Whether it's new uses for big data or the challenges in making it successful, she's passionate about enabling organizations to achieve more with their data.

More about Sherry Tiao
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