Leading companies agree that investing in data science is important, but few actually see significant value from their efforts. That’s because data science workflows are rarely organized in a way that truly maximizes value. Data scientists aren’t collaborating or utilizing engineering resources effectively, and the results of their work often don’t make it to stakeholders who can use these insights to affect change in your business. Let's take a deeper dive into why that is.
Your work isn’t reproducible and your data scientists aren’t collaborating
As a data science team grows, it becomes more and more challenging to keep track of the work that different contributors are producing and for what purpose. Organizing related code, data, and model outputs in one place, such as a data science platform, can help ensure the visibility of your team’s work. This makes data science collaboration a lot more intuitive. And when it’s time to bring a new contributor on board, he or she can easily reuse existing code to get up to speed from the get go.
Your data scientists are hogging engineering resources
Even in the most data-savvy companies, it’s common for data scientists to turn their completed data models over to the engineering team for deployment. That's because getting a model into production usually requires an engineer to rewrite a model into a production stack language, deploy the model into the production environment, and test its performance, all before rolling it out to users. This hand off from data scientists to engineers introduces significant lags between model completion and the moment relevant stakeholders first see results. Setting up infrastructure that empowers data scientists to deploy models on their own as APIs can eliminate these lags.
Your insights aren’t getting to decision makers
The goal of predictive modeling is to generate key insights about your business, but sometimes this information doesn’t get to decision makers. Let's say one of your data scientists is building an individual-level predictive churn model that assigns a cancellation risk score to each of your customers. Without the right process in place to put it into production, this potentially valuable model becomes more of an R&D effort that lives and dies on your data scientist’s laptop. Effective data science workflows include strategies for putting insights into action, whether that means using an organized reporting system or integrations with dashboards your team already uses to inform their work.
As companies increasingly rely on data modeling to make decisions, inefficiencies in data science workflows have an ever greater impact on a business as a whole. Paying attention to how your data science team collaborates and interacts with your engineers and decision makers can help you get the most of your data science efforts.