An increasing number of organizations are bringing data scientists on board as executives and managers recognize the potential of data science and artificial intelligence to boost performance. But hiring talented data scientists is one thing; harnessing their capabilities for the benefit of the organization is another. By Rama Ramakrishnan.
The article main parts:
- Point data science teams toward the right problem
- Decide on a clear evaluation metric up front
- Create a common-sense baseline first
- Manage data science projects more like research than like engineering
- Check for truth and consequences
- Log everything, and retrain periodically
It is important to subject results to intense scrutiny to make sure the benefits are real and there are no unintended negative consequences. The most basic check is making sure the results are calculated on data that was not used to build the models. Data science models, like software in general, tend to require a great deal of future effort because of the need for maintenance and upgrades. They have an additional layer of effort and complexity because of their extraordinary dependence on data and the resulting need for retraining. Nice one!
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