machine learning

Learning is an ongoing process for people and machines. That means that a repeatable process, for people and data, needs to be in place for an enterprise to benefit from machine learning.

People listen to news or check websites daily to keep up with the world around them. What do machines do to stay up to date?

Machines are computers running algorithms that look for patterns. They learn about behavior from the data that is provided to them. If machine learning is to keep up with a changing world, then it needs to continually get new data.

When companies begin using machine learning, they give computers samples of past customer activity and task the machines with finding certain patterns. Using the patterns, companies make predictions about what people will do so they can improve business decision-making and results.

In the financial services industry, machine learning can offer insights into how people use their money. Armed with that information, banks and fintech companies can help customers save money, invest more wisely, or at least get the best value from a planned purchase. As machine learning is incorporated into more complex systems, we’ll start online payment with cardto see AI that functions as your personal banker, or perhaps, your personal shopper, depending on your mood.

The first project that a company undertakes can be difficult because data needs to be used in a new way. Groups of people gather the data, but these people may not be used to working together. And the data probably needs to be modified from how it was originally stored. It’s tempting to use the first model so the project team doesn’t have to recreate the data input process.

But this human (and business) process friction is easier to smooth out if the repeatable data gathering and machine learning processes are resolved sooner. And machines are hungry learners, so companies are more likely to succeed if they build processes that let the machines keep up with the world.

If your customer behavior is coming from a high volume website, then you probably have Big Data. Websites often represent behavior across a whole country, and conditions might change rapidly depending on memes or news. Data scientists can help you stay current by setting up specialized data feeds and models that keep up with this continuous flow of information.

Johnson Controls is using AI to reduce churn and identify over $100M a year of protectable revenue


Johnson Controls had no usable data sets, no data science team or data engineers.

How could they rapidly build a global data team with new AI/ML capabilities to improve business outcomes on a major scale?

Is your company struggling with how to implement predictive analytics?