- Artificial Intelligence
- Internet of Things
- Big Data
- RT Analytics
- Machine Learning
- Use Cases
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tories about machine learning successes are great — but there aren't enough of them. Machine learning ROI for too many firms needs to get better. And that's why my discussion with Ryan Trollip and Charlotte DeKeyrel of Decision Management Solutions is so timely. ML is powerful but payoffs for poorly integrated deployments are low. The secret to better results with ML is very much about integrating machine learning with what you and your subject matter experts already know. How you do that is the focus of this podcast. It's all about decision modeling as the key to real ROI for machine learning - and ultimately, digital transformation. Bonus Topic: the black box problem! Give it a listen.
[02:01] A lot of practitioners and venders have convinced themselves that it (machine learning) is good for everything — and that's not necessarily the case.
[03:09] And that takes interacting with subject matter experts. It takes interacting with business leaders and understanding the knowledge within the organization and the context to which you're applying those decisions because you're trying to move the needle, some sort of KPI.
[03:50] Well, we begin with the decision in mind. So in reality only after framing the problem can we identify the areas where machine learning makes sense.
[05:54] And we found that the value was really in externalizing those decisions and not so much in the fancy algorithms initially.
[09:23] They had recently done a project with an AI firm that came in and did a bunch of machine learning and they decided not to implement it at all because it was a black box . She was like, "If I can't see what's going on in there, I'm not doing this."
[09:41] Charlotte went in and modeled that out, she kept full visibility into everything that was going on. We could see every single sub-decision that was executed and we could also trace even the features or variables and how they contributed to machine learning outcomes as well.
[13:19] Decision models are ideally built in direct collaboration with the subject matter experts themselves. So this helps to ensure that the dependencies that you've identified are real and the logic reflects how the business should act.
[13:54] By embedding their knowledge and experience within the model and the rules in the first place, you raise the corporate IQ of everyone.
[15:36] And volume is the key there because the more volume you have in a decision, the more value you're going to have automating that in terms of just an additive value because you make so many of them every day.
[18:07] We can look at the output from the dashboards and that associated with those decisions and say, wow, if we plugged the machine learning decision in here we could boost the numbers substantially.
[21:53] Don't be scared of going into a machine learning projects. I think you can get real ROI from them if you focus on a few key things.
Analysis By Data Decisioning
Out Of The Lab And Into Production: ROI From Machine LearningThe Editors
Digital transformation has been on every business executives' agenda since at least 2014, when the term was coined by Capgemini's Didier Bonnet (a Data Decisioning podcast interviewee) and George Westerman.
But despite a consensus on what needs to be done, the "how" of digital transformation is still a bit of a mystery. Certaintly, the overall approach is clear: Didier Bonnet emphasizes that digital transformation must be led from the top. But that still leaves open the details of "how".
What's this got to do with machine learning? For many, AI-as-machine-learning is at the core of digital transformation. Success at digital transformation very much depends on success with machine learning. But ROI on machine learning has turned out to be difficult to achieve, at least consistently.
That makes today's podcast very timely because it concerns the single most important thing you can do to improve the ROI of machine learning. It's not complicated: to improve the ROI of machine learning, you must have a very strong focus on the technology and discipline of decision-making, especially "decision modeling".
The claim for decision modeling is bold (and it's a view that is strongly held by Data Decisioning). You may say "I'm already focused on decision making, it's what I do", and that would be true. But just as real accounting is different from keeping receipts in a shoe box, decision-making supported by the discipline of decision modeling is a huge step upwards in decision-making capacity and quality.
The wonderful thing is, decision modeling is all about levering your existing knowledge and business practices, but more systematically. Decision modeling is very doable — and it creates lasting value for any company. As Charlotte DeKeyrel says, "Decision modeling increases your corporate IQ."
It's still early days. While adoption rates for decision modeling are climbing, decision modeling is still an opportunity for competitive advantage! Deploy your machine learning in the context of decision models, enjoy the synergy and tally a real ROI.
Lab experiments are fun; but it's even nicer when you can transform the work we do every day for the better ♦
Ryan has spent his entire career focused on delivering value through digital automation and has over 25 years of experience in leading business automation delivery, and more than 15 years of specialized overseeing and building decision automation services, practices, and software. These practices delivered large, complex decision automation solutions. Ryan is currently the founder of DecisionAutomation.ORG, CTO at Decision Management Solutions, and formerly the head of Enterprise Architecture, Advisory consulting (Business Architecture), and Decision Management Practices.
Charlotte DeKeyrel is a lead Decision Modeler with Decision Management Solutions. She has years of experience modeling decisions for automation all over the world in a wide variety of industries ranging from insurance to military planning. Charlotte has a background in engineering, a degree in Mathematics, and an analytical outlook that helps her to approach problems from a logical standpoint while always on the look-out for opportunities for improvement.