Mar 3, 22 | 6 min read
successful data scientist
Reading time: 4 minutes
The growth of data seems indisputable. In 2020, each person generated 1.7 megabytes of data per second , and today it would take a person more than 180 million years to download all the data on the Internet .
As you know, technology allows us to use this data to make decisions, which is why 94% of companies say that data is important for their business growth and digital transformation .
In this context, the field of data science emerges with the mission of making the most of the data generated. But how is this amount of data managed to provide information and recommendations to the business?
To answer this fundamental question, data scientists are hired: The U.S. Bureau of Labor Statistics predicts that the number of jobs in the data science field will grow by approximately 28% through 2026 .
However, have you ever thought about what kind of problems data scientists can work on?
Here at Rock Content, we use data to predict when a customer is going to cancel a contract, so we can use retention techniques before they make that decision.
When we detect this possibility, othe r tunisia email list teams proactively engage with customers to salvage this revenue.
This is not the only application of data science . From challenges related to acquiring new customers to cross-selling opportunities in business, data scientists focus on consuming data to solve problems .
It is natural for data scientists to approach those business problems with different strategies. While it is healthy, especially when a team is full of professionals from different backgrounds, there is one characteristic among the most successful ones that I would like to discuss.
Real-life data science projects are not exactly the same as those found in learning environments or on data competition websites, such as Kaggle .
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.
How different is it to handle data in real life and in a learning environment?
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 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 about 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?
I agree that data science project is not an individual activity, however, I strongly believe that data scientists can contribute to hypothesis design .
It is relevant to bring to the table the fact that in a field with a talent gap, the balance between industry knowledge and hard data skills can be crucial for successful projects.
Data may be just the tip of the iceberg
Deep diving into business understanding should not be seen as a data scientist going above and beyond their job description. This is not true.
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This kind of behavior is an inspiration to finally design the dataset needed for the data science project and also to start another effort on other technical challenges.
Keep in mind that data is just the tip of an iceberg that requires much deeper reflections on business objectives. If you don't engage deeply, you can miss thousands of opportunities.
The effort to frame the business problem is probably the most visible characteristic I have noticed in various data scientists from different backgrounds.
Of course, it is not just up to the data scientist, but also to the leadership to bring them to the decision-making stage.
