Predictive assets aren’t academic exercises
The biggest challenge that confronts predictive modelers and data scientists is that it doesn’t matter how good any model is, if it isn’t used by other people in your organization to improve the way they run your business.
A recent Harvard Business Review article noted at a recent industry conference, that out of 150 data scientists, roughly a third had developed a model in the past year, but not a single one had deployed it into production, nor monitored that model’s effect on business value. That’s just sad.
We believe there are some common reasons that predictive models struggle to get used. They’re complex, they’re arcane, and there is no common process or vocabulary in most organizations that enables people across the domains of data science, customer-facing representatives, and technology to make daily business decisions using models. That’s a problem we are committed to solving.
Corios Tempo: our process for building an analytic model factory
We’d like to help the industry define the discipline of rapid model development and deployment. Our own internal name for this discipline is Corios Tempo, which reveals that we think maintaining a stable rhythm of activity is an important design element.
In this presentation, we’ll discuss:
- the problem we aim to solve,
- the design principles of a robust solution,
- the benefits of doing so,
- the roles and responsibilities inside the analytics organization that need to be harnessed to solve this challenge,
- the way we organize data,
- how we build models,
- how we validate and deploy these models,
- how we administer the platform on which all this asset development and management activity takes place, and
- the best practices that we have developed over my 30-year career in analytics as well as the decades of collective experience developed by the Corios team.
If you’d like to read more about how Corios Tempo solves this problem by providing enterprises with a repeatable process for building and operating an analytic model factory, you can get in touch with us to find out how you can build an analytics model factory.