Today, nearly every organization is looking to drive business value through the application of machine learning and artificial intelligence. We’re bombarded with stories about fascinating models, built on millions or billions of data points, helping to solve even more fascinating problems. But what should we do if our identified business cases don’t have much data to support them? Can we still find value?

In reality, we don’t always get the luxury of huge datasets to pair up with our most valuable business cases. Perhaps we need to start small, demonstrate value early, and evaluate the potential of the wider initiative as an input into further investment in the data. Or perhaps the domain itself is the limiting factor, and we’ll never expect to hit data volumes which some might consider to be “big data.”

In any case, in this recorded webcast Thorogood Consultants Scott Stieritz, Utkarsh Panchal and Mani Singh explore what approaches we can take to get the most value out of the data that we do have, while maintaining a clear understanding of the limitations and challenges we may face along the way.

What to expect:

• What does insufficient data look like, and why might this cause issues in your machine learning initiative?
• What approaches can we take to drive business value despite these limitations?

Is it for you?

• Do you have a business use case identified and are looking for ways to build machine learning models despite smaller dataset sizes?
• Have you ever thought to yourself, “Machine learning sounds great, but we really don’t have enough data in my area of the organization to make it a reality”?