The right way to Be A Successful Data Scientist

Data growth seems undiscussable. In 2020, everybody generated 1 . 7 mega bytes of data per second and today it would take a person more than 180 million yrs to download all the data from the internet . There is certainly an opportunity to consume this information to make decisions, and this is the reason why 94% of the companies say that data is essential to their business growth plus digital transformation .  

In this context, the data science industry comes up with the mission of making data useful . How does one deal with this amount of data to deliver insights plus recommendations to the business? This really is one of the golden questions data scientists have been hired in order to answer: The U. S. Bureau of Labour Data predicts that the number of jobs in the data science field will develop about 28% through 2026 .

But have you ever thought about what type of problems data scientists can work on? Just to provide you with one example, here in Rock Content, we work in a way to predict when a customer will churn before this decision based on data.  

From this acquiring, it is possible to have other teams interacting with the customers and conserve this revenue in a positive way.   This is not the only application of data science. Through challenges related to acquisition of new customers to cross-sell opportunities in operation: data scientists focus on consuming data to solve problems.

It is natural to have data researchers approaching those business difficulties with different strategies. Even though it is healthy – especially when a team is filled with experts from different backgrounds – there is one characteristic among the most successful ones I would like to discuss.

Data Science projects in real life are not exactly the same that people find in learning environments or even in data competition internet sites, such as Kaggle . It does not mean those data competitions are bad, but dealing with those problems does not mean the same success will be achieved in real life projects.

How different is it to dealing with data in actual life and in a learning atmosphere?

In your daily routine, you might not have a ready-to-go dataset for every scenario. If you do, maybe you can consider reflecting on it. Certainly, it will be the main drive of the outcome from your delivery. The particular reflection on this point can be: how can you answer for a derive from your main drive, if you failed to work on it?

From this issue, it’s important to reinforce: data science certainly begins way before the information . I highly motivate data scientists to put lots of energy into the problem description, and not just think about the analytical item that will be delivered at the end. The business comes first. Always.  

This is very similar to when online marketers do their annual preparing, for example. It’s a temptation to launch your existence in the metaverse just because everybody is talking about, for example. But , wait: why do you want to take the metaverse? What company problems do you want to solve? Remember: strategies always come prior to tactics.

When we talk about information, using the same approach will ensure the solution is not becoming thought of before exploring what really might be solved. It is relevant for leaders to interact with data scientists the moment they can.

Despite the fact that 38% of data professionals are involved in the decision making , they may not feel that their insights are accurately considered. Several questions can come out of this, but certainly a group of them are associated with the difference between understanding the information versus understanding the business alone.

With this in mind, we can explore a deeper question: how can data scientists reflect on business issues if they do not heavily be familiar with business? I definitely concur that the data science project is not an individual activity, however I strongly believe that data scientists can contribute to designing hypotheses.  

It is relevant to bring to the table the fact that in a industry with a talent gap , the balance involving the industry knowledge and hard data skills can be important for successful projects.

Information might be just the top of the iceberg

The deep dive into the business understanding should not be seen as an data scientist going outside of his job. This is not true. This kind of behaviour is an motivation for finally designing the particular dataset needed for the data science project, and also to initiate one more effort on other technical challenges.

Notice that data is just the tip of an iceberg concerning much deeper reflections on the business goals. When you assume your work starts from the tip of the iceberg, you must have lost a large number of opportunities.  

The effort of framing the business problem probably is the most visible characteristic I use noticed across multiple data scientists from different backgrounds. Naturally, it does not depend only on the data scientist, but additionally on leadership to bring these to the decision making stage.

Summary: bring the data scientists jointly to discuss the business

To summary, there are discussions related to data scientist functions being killed by new tools that may automatically apply machine learning. I strongly disagree. These tools probably are eliminating roles that are just writing code without interpretation – or dealing with the tip of the iceberg.  

Those in a position to go deep in the ocean may not be easily replaceable. It takes a mix of skills that goes into action way before the information. Data Science is much more compared to discussing algorithms and tuning models. This is the answer meant for understanding why we have a lot of data scientists doing an excellent job and coming from different areas other than science, technology, executive and management: it is about exploring and using hard skills as learnable tools that will help to deliver business improvements.

In the end those points, a heavy recommendation is to ensure data professionals understand the business itself prior to dealing with technical issues. Connection with the product is one of the best gifts a leader can give to a data scientist and most of the time this particular effort will lead to valuable insights and an accelerated pace of projects soon. The nearer the technical team is to the product, definitely the bigger the opportunities that might be seen simply by them.

On the other hand, keep in mind that the data wrangling magic is just a ‘how’ to reach business objectives. By understanding it, we can infer that there is nothing better than business experts to support traveling data projects.

I ask you to reflect on what kind of data scientist may be a true sport changer for your business, and also recommend that you subscribe to the newsletter, so you can keep updated on fresh themes associated with marketing and business.

The post How To Be A Successful Information Scientist made an appearance first on Rock Content .

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