How different is it to handle data in real life and in a learning environment?

[asikurrahmanshuvo]

This is not to say that such data skills are bad, but dealing with such challenges does not mean that the same success will be achieved in real-life projects.

Data is the primary driver of results, but in your daily routine, you may not have a ready-to-use data set for every scenario.

From this reality, it is important to reinforce: data science definitely starts long before the data .

That's why I always strongly recommend that data scientists also put a lot of energy into defining the problem and not just think about the analytical product that will be delivered at the end. The business concept always comes first.

This is very similar to when marketers do their annual planning, for example. It's tempting to launch your presence into the metaverse just because everyone is talking about it, for example.

But you should also ask yourself first: why do you namibia phone number list want to be in the metaverse? What business problems do you want to solve? Remember: strategies always come before tactics .

And when it comes to data, using this same approach will ensure that you aren’t thinking of a solution before exploring what you really need to solve. It’s important for leaders to engage with data scientists early in the process .

Despite the fact that 38% of data professionals are involved in decision-making , they may not feel that their insights are accurately considered. Several questions may arise from this, but certainly a group of them are related to the difference between understanding data and understanding the business itself .

With this in mind, we can explore a deeper question: how can data scientists think about business problems if they don’t deeply understand the business?