Data Science Tactics vs. Strategy

In general, tactics refer to specific actions that can be taken and strategy refers to how those actions are assembled towards the end goal.

In the data science context, data science tactics primarily map to statistical or machine learning methods. Data visualizations, analysis writeups, code documentation fall under tactics too. Less commonly spoken about, persuasion skills also can be considered part of data science tactics. Data Science strategy maps to the culture how those methods are used to solve data science problems.

Data Science Tactics

All data scientists know that they need to train up on statistics and machine learning tactics. These are tools like logistic regression, deep neural nets, sampling theory, hypothesis testing, mixed effects models. Being competent with these tools is table stakes for data scientists. It’s common for data scientists to want to focus 80% of their time on this area —it’s the fun stuff.

Most data scientists also eventually come to understand that 80% of what others perceive about your work is in the form of documents and presentations. That means that to be an effective data scientist, it’s important to develop your own principles for making docs and presentations that communicate what you want effectively. The principles serve as an algorithm, your own human algorithm for producing solid docs and presentations consistently and quickly.

Some data scientists also eventually realize that there are tactics of persuasion that can make your life easier. Example: suppose you develop a model that outperforms someone else’s, what do you say to them? One way would simply be: “hey my model is better than the one we have, let’s launch it!” That can work among close colleagues but can backfire especially when you’re new to the team. For example, your model can be killed with responses like “Oh your model is overly complicated and the gain isn’t that big so we prefer the simpler one” or “Your model is good but we’re working on a new version that will subsume what your model does, thanks anyway.”

One issue with the direct approach of saying your model is better is that it can feel threatening to others. So, something you can use that can feel less threatening is something that I call the “collateral damage” principle. The idea is to start by recognizing the strength of the current approach (example: the current approach is simple and works pretty well), then to show the collateral damage of the technique (example: here are some misclassifications from the model), and then to unveil how your approach eliminates or mitigates the weakness. By recognizing the merits of their current approach, you earn a better chance at persuading them.