Common mistakes when working with data
What are some common mistakes people make when working with data?
When working with data, one should always keep in mind that data plays a very crucial role and ignoring it will just be doing “Garbage in-Garbage out”. We’ll find many people without the necessary real-world experience to fully understand the potential pitfalls we can encounter when working with data.
Data professionals can succumb to many pitfalls, as with any profession. Here are the common mistakes that ultimately cause them to fail:
- Not looking beyond numbers: Falling under the spell of big numbers is a common mistake that so many people commit.
- Not defining the problem well: If we can’t define the problem well enough then reaching its solution will be a mere dream. Most of the issues which arise are due to fact that the problem for which solution needs to be found out is itself not correctly defined.
- Forgetting the basics: Any data professional who has model building skills without fundamentals is like a pilot who can fly an airplane without knowing what the cockpit dials mean.”
- Picturing the Algorithm Is more Important than Domain Knowledge: If you want to build a good model, it is crucial to know and understand the data you will be using, the purpose behind your model, and the basic domain knowledge.
- Spending less time on exploring and visualizing data: When we are eager to finish building a model and complete the task while ignoring the exploring and visualization part can cause serious damage to the model.
- Concentrating more on accuracy rather than context: Accuracy isn’t always everything.