5 Best Practices in Analytics
As part of our #take5 series, here is our first post, where we look at some of the best practices, one must follow when pursuing a career in analytics.
In the last decade, data science projects have become more complex, extensive and expensive. So the implementation of successful data science solutions has also in turn become more intricate and complicated. It requires a combination of strong processes, delivery methodologies and technologies. Lets have a quick look at some of the best practices you can follow in the real world implementation of analytics.
- Build for the Future – Keep in mind the changing landscape of the data science platform, and always ensure that whatever you do, is current, future thinking and dynamic.
- There may never be one final model – Iterate, reiterate and reiterate again! You may never have a final model. Compare results of different models over time continuously and consistently to see what works best for your purposes.
- Data Science is Continuous – Taking off from point 2, remember that data analysis is never over! The analysis can be continually improved, and results can change depending on the requirements.
- Monitor Your Solutions – Understanding where your models and solutions succeed and fail is essential to be business-ready. Keep checking to see how accurate and efficient your models are. An efficient solution today can be a disastrous one tomorrow!
- Think Smaller, Fail Faster and Be Ready to Reiterate – Not everything needs a massive canvas or a global perspective. Sometimes it is easier to think smaller when it comes to solving big problems. Smaller analyses also mean faster results – so you know whether something is working or not. So its easier to pick up where you left off last, and start reiterating until you reach a model and a solution that fits your problem.
Come back on Friday, for our next #take5 article – 5 Tips to Ace an Analytics Interview