Amalgam has just posted a new report: The Roadmap to Multi-Million Dollar Machine Learning Value with DataRobot. I’m especially excited about this report for a couple of reasons.
First, this report documents multiple clear value propositions for machine learning that led to the documented annual value of over a million dollars. This is an important metric to demonstrate at a time when many enterprises are still asking why they should be putting money into machine learning.
Second, Amalgam introduces a straightforward map for understanding how to construct machine learning products that are designed to create multi-million dollar value. Rather than simply hope and wish for a good financial outcome, companies can actually model if their project is likely to justify the cost of machine learning (especially the specialized mathematical and programming skills needed to make this work.)
Amalgam provides the following starting point for designing Multi-Million dollar machine learning value:
Stage One is discovering the initial need for machine learning, which may sound tautological. “To start machine learning, find the need for machine learning…” More specifically, look for opportunities to analyze hundreds of variables that may be related to a specific outcome, but where relationships cannot be quickly analyzed by gut feel or basic business intelligence. And look for opportunities where employees already have gut feelings that a new variable may be related to a good business outcome, such as better credit risk scoring or higher quality supply chain management. Start with your top revenue-creating or value-creating department and then deeply explore.
Stage Two is about financial analysis and moving to production. Ideally, your organization will find a use case involving over $100 million in value. This does not mean that your organization is making $100 million in revenue, as activities such as financial loans, talent recruiting, and preventative maintenance can potentially lead to billions of dollars in capital or value being created even if the vendor only collects a small percentage as a finder’s fee, interest, or maintenance fee. Once the opportunity exists, move on it. Start small and get value.
Then finally, take those lessons learned and start building an internal Machine Learning Best Practices or Center of Excellence organization. Again, start small and focus on documenting what works within your organization, including the team of employees needed to get up and running, the financial justification needed to move forward, and the technical resources needed to operationalize machine learning on a scalable and predictable basis. Drive the cost of Machine Learning down internally so that your organization can tackle smaller problems without being labor, cost, and time-prohibitive.
This blog is just a starting point for the discussion of machine learning value Amalgam covers in The Roadmap to Multi-Million Dollar Machine Learning Value with DataRobot. Please check out the rest of the report as we discuss the Six Stages of moving from BI to AI.
This report also defines a financial ROI model associated with a business-based approach to machine learning.
If you have any questions about this blog, the report, or how to engage Amalgam Insights in providing strategy and vendor recommendations for your data science and machine learning initiatives, please feel free to contact us at firstname.lastname@example.org.