How to Improve Machine Learning Over Time: Build Data Flywheels

machine learning

Digital businesses and workflows create data 24/7 through repeatable processes. This  data is either religiously maintained or gathers dust. Varying from useful personal identification data to neglected customer engagement or sales information, all of which can be used to build a data flywheel.

The consequence is that data becomes nothing more than a costly black box hidden in a data center.. almost a liability! But what’s even worse news is that this can seriously lag the growth of your business. The inability to handle data can be a real struggle for organizations.

In this blogpost, you will learn how to harness your company’s data using what is known as a data flywheel. This paradigm “unboxes” your data and generates exponential growth for businesses.

You will also learn about how data streams generate new insights and deliver new types of products, when combined with modern ML.

Don’t create algorithms, make a data flywheel.

In mechanics, a flywheel is a machinery that keeps the whole system in a cycle or rotation even without the need for stimulus. According to thermodynamics, perpetual motion is impossible. But who said the laws of physics have to apply to business? When applied properly, data can generate self-sustaining, perpetually accelerating momentum for your organization.

Data Flywheels begin and end with data: the data flywheel is constantly fueled with new data as it spins. While spinning, the flywheel generates more momentum and more value for your business.

Creating data flywheels means creating a better Machine Learning life-cycle.

It starts with better datasets, continues with improved ML algorithms, and ends with high value delivered to your business. Indeed, every day, even when you are sleeping more data will be created by your business processes. This is the same data that will be used to make your Machine Learning better!

Stand on the shoulder of giants – proven success with data flywheels

The flywheel model is not a new idea. Popularized by Jim Collins, and applied in the early 90s by Jeff Bezos’ Amazon flywheel. It is a self-reinforcing loop: you pursue projects that feed and drive each other. The aws flywheel has been key to Amazon’s continued success!

Let’s take Google translator for instance. Launched in 2006 to translate websites, it’s by far the best technology and product on the market to do so. How? by asking users feedback about the quality of their translation. The more they translated, the more feedback they collected. The more feedback they could collect – you guessed it! the higher the performance. The result? users pouring more and more data into an always improving translation service.

The perfect data flywheel!

Final Thoughts

When properly applied, data flywheels generate self-sustaining, perpetually accelerating momentum for your organization.

Leaving your data unused is not only a missed opportunity, it also becomes a threat to your business. The market is competitive! and as competitors adop data flywheels it may erode your competitive edge. So if you are interesting in building your own data flywheel, drop us a message at info@gemmo.ai.

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