Nick Jordan | 11.15.18 | Community Insights

Making the Business Case for Machine Learning Part 2


  • ai
  • artificial intelligence
  • machine learning
  • next matters most
  • triangle tech

Machine Learning

On October 24, the Next Matters Most MeetUp held a panel discussion on Machine Learning (we streamed it on Twitter if you’re interested). The event brought Machine Learning experts together with members of the Triangle Community who (like me) wanted to learn more. While people know Machine Learning is out there and can explain the tech value of it, the current challenge is how to demonstrate the business value. The panelists shared different ways of doing this effectively – and we even got some great ROI stats courtesy of Richard Boyd.

 This is the second part of a series around Machine Learning that recaps thoughts shared by our talented panelists (their full bios are available here): 

  • Moderator Ken Wood is a machine learning practitioner and founder of Roar Marketing Concepts LLC, a data-driven marketing strategy consultancy.
  • Serial Entrepreneur Richard Boyd is CEO and co-founder of AI and machine learning company Tanjo, Inc. and simulation learning company Ultisim Inc.
  • Eric Reifsnider, Ph.D. is Data Science Manager at Validic, the leading platform for patient-generated health data integration and analysis.
  • Dude Solutions’ Brooks Adcock is Director of Innovation at the operations management software provider.
  • Dawn Code is co-founder of Unspecified, LLC, which provides higher quality software and specializes in machine learning.

Skills, Resources & Team Members

Ken asked how business owners could be successful with Machine Learning. What resources and skills are needed? This question yielded a lot of great advice from the panelists that came as a result of several cautionary tales.

For Richard, executive buy-in for the project is a must. People are threatened by Machine Learning and often fail to realize how it actually benefits them (frees them up to concentrate on high value tasks by automating mundane, tedious or repetitive work, etc.). To be successful, you need to have top level stakeholders that understand how Machine Learning will benefit the company and push through barriers.

Eric recommends appointing someone to “own” Machine Learning internally and act as the interface with any outside providers. Ideally, this person has some existing understanding of ML but they only need to be able to “talk the talk” (not do the heavy lifting).

Brooks shared a detailed blueprint for success. First, start with a narrowly-scoped, practical business strategy and then put together an internal “dream team” comprised of:

  • One project owner with executive backing to spearhead the project
  • A strong DevOps person to securely penetrate internal data silos while keeping CSO happy
  • A Data scientist to correct missing info and get to the point of millions of records
  • One solid product person to build externally facing system with the data for end consumers to use

Lastly, have a centralized infrastructure like AWS as the foundation of the project.

Cost of Getting It Wrong

One concern that Ken shared is the fear of getting it wrong. Business owners worry about the cost of errors – what if something is classified incorrectly or a wrong decision is made. This thinking can derail Machine Learning projects.

The panel agreed that the ROI in Machine Learning is so strong that you don’t need to get it right all the time for it to still be extremely beneficial. And that the chances that human trained machines have a stronger chance of getting it right than an individual human.

Brooks advises using the following calculation to ensure you are still coming out ahead:

Take the number of instances that were wrong and multiply them by the cost of the errors. As long as your overall cost savings exceed this figure – you are still making money with Machine Learning.

Challenges to Anticipate: Data Issues & Questions

When it comes to what business owners should expect in the way of obstacles the entire panel agreed that data issues are a given.

Machine Learning brings a reality check to your data. It sheds light on bad and missing data and identifies data entry issues. Sometimes it can give you a a bit of a wake-up call. However, there is value in learning what you actually have. Just be ready for some hard conversations around rectifying data issues and enacting a sound data governance plan.

Dawn regards Machine Learning as a platform that actually creates more questions. It allows you to explore what is really going on with your data all at once (in a way humans can not).

What knowledge is gleaned from Machine Learning, a what does it mean? According to Dawn the answer varies. It could mean nothing or it could point you in a completely different direction. So be open to questions (not just answers) when you get started.

Machine Learning 101: How to Get Educated

Machine Learning is an extremely broad field. When it comes to learning and education, Eric shared some of the night’s best advice. Focus on problems you care about. Machine Learning is the solution to a problem and there are lots of different problems to solve. Identify a specific problem you want Machine Learning to solve and then work backwards to determine exactly what you need to learn.

This is solid advice and gives you a concrete way to tinker and apply your knowledge.

Some of the tools and courses recommended by panel includes:

I am amazed that all of that info was shared in a 45-minute panel discussion. I would like to extend a huge thank you to Ken, Richard, Eric, Brooks and Dawn for sharing their time and expertise.

It was great to meet so many curious, like-minded people that evening. I know everyone who attended came away with a better understanding of the business impact of ML and the different strategies needed to be successful.

Interested in learning even more? We’ll be holding a seminar on UX For Machine Learning on November 28th at 6pm. Slots will fill up fast, so sign up today!

Nick Jordan is the CEO of Smashing Boxes, a company he founded in 2010 to help companies create disruptive software solutions that impact the world.

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