I frequently have conversations with business executives who manage custom-built applications in their organizations. When the topic converges on modernizing these custom and often legacy apps, they often proudly claim modernization is underway and explain to me that they are moving to the cloud, perhaps containerizing the app, and if ambitious, re-architecting the app to take advantage of microservices.
While these are worthwhile efforts, I rarely observe that these efforts move the needle with respect to business outcomes. Being on the cloud does not improve personalization in a marketing application, or reduce fraudulent claims in an insurance system, or optimize the supply chain of a manufacturer. The applications may definitely be more agile. And yes, those container orchestration and automation advances and microservice re-factors may be able to make developers more productive, but they rarely, if ever, end up fundamentally changing or improving the application itself. So while cloud migration and containerization is are important, what is a more impactful modernization that can truly move the needle of business outcomes?
For that, I believe it is necessary to take advantage of the wealth of data sources available and supercharge applications with new external data, then use this data to train Machine Learning models that make applications adapt with experience. This next generation of modernization injects predictive models into the application to predict a certain future outcome, so that the application can then take action accordingly.
But here’s the rub. When companies try to modernize with AI and ML, they often organize their teams poorly.
In fact, when you mention AI or ML to anyone on an IT application team, they immediately pivot you to the data science team or the data lake team. This is the first sign of a silo. That usually means the people who can manage large volumes of data and “do the math” of Machine Learning are sitting in their silos. They are away from the action — where the application interacts with customers, suppliers, employees, etc. They are one step removed from the business. Recently, when I met with the head of a data science team for an insurance company, they said the one thing holding back operationalizing their work was a lack of engagement from the application teams.
This has a profound negative impact on modernization. Some of this we recently talked about from a technology perspective (see our blog on What happened to Hadoop?). But here I want to focus on people and process. How do silos affect modernization from a people and process perspective?
My view is that the status quo for injecting AI into an application is usually initiated by the AI team. They get their data from some data lake. They create a thesis and experiment with many models. Sometimes they create these models in a vacuum based on the data they have available. They run many permutations of features, algorithms, and parameters and if done well, they measure the experiments properly with accuracy metrics that can objectively measure how well the model predicts new examples in a test set. One of the best reads on experimental best practices for for machine learning is Andrew Ng’s new book, Machine Learning Yearning.
But here is the punchline. The AI or data science team is ill-equipped to get the job done independently. They simply do not have enough deep knowledge about the business or the applications that will deploy the models to lead to production operations that deliver business outcomes. This is not a slight on data scientists at all. I’ve been one. But the secret sauce to a successful team is diversity. Data science is a team sport. Data scientists need to work side by side with people who know the business and the application. Here’s why.
