As any reader of this blog will know, the data science field is hot and only poised to grow hotter as AI, ML, edge computing, and related technologies mature. Companies are clamoring to harness the wealth of opportunities data science presents, investing in new solutions and hiring new data science talent. As organizations look to build out their data science teams, it is crucial that they also take the proper steps to enable these individuals to reach their full potential. 

With that in mind, we’ve uncovered the following six best practices for getting the best out of your data science team. 

  1. Point Them Toward the Right Problem

It’s up to business leaders to define the problem they want their data science team to solve, and it’s important to do this right. The training data, modeling approach, and level of effort will all vary based upon the nature of the problem, as will the ultimate business impact. If you need help identifying the right problem, take cues from what other companies in your industry are doing, particularly early data science adopters. 

  1. Decide on a Clear Evaluation Metric Up-front

This step can be more difficult than it seems, as most complex business situations generally have many relevant metrics that may conflict with one another. If you’re unsure which one is right for your project, ask your data scientists to provide guidance on the metrics typically used in the industry to evaluate models for similar problems. 

  1. Start with a Common-sense Baseline 

Once you’ve completed the previous steps, it’s time to create a common-sense baseline or, in other words, determine how the team would solve the problem at hand if they didn’t know any data science. This forces them to get the end-to-end data and evaluation pipeline working and also enables them to identify any issues with data access, cleanliness, and timeliness. In addition, evaluating the baseline provides insight into how much benefit the project may deliver. 

  1. Manage Data Science Projects like Research Projects

Experienced practitioners know that data science involves a fair amount of trial and error and that it’s difficult to predict when a breakthrough may occur. Business leaders should regularly meet with data scientists to understand the project and review its improvements to determine whether they are sufficient or if it’s time to consider putting the project on pause. 

  1. Check for “Truth and Consequences” 

Another critical step is scrutinizing results to ensure the benefits are real and there are no unintended negative consequences. 

  1. Log Everything, and Retrain Periodically

Logging every input and output in detail makes it significantly easier to investigate and fix any problems faster. As time progresses, the nature of the data being fed into the model will begin drifting away from the data used to build the model. t’s important to have automated processes that track model performance over time and retrain as necessary to ensure continued efficacy.

Click here for more on the above considerations and how they can increase the success of your data science initiatives.