Data science management aims to align work with business goals and make the team accountable for the outcome. It ensures that each team is allocated to its place within the same department or distributed group. Hence, in this blog, we dive into the key areas of managing data projects effectively.
Key Concepts of Data Science Management
1. Know Data Science
Being a data scientist does not mean having expertise or complete knowledge about all previous experience. All you need is a better understanding of the work that can lead you toward completing the project. Although more than knowledge of data science projects is required, you must also comprehend the challenges while working on the project. For example, when you are working on a project, it may be completed quickly or take up 70% of the effort. So, to solve its issues, you need to set up the project timeline before starting to work on it.
2. Engage Stakeholders
To make any project successful, the team should comprehend and follow the rule of “work smarter rather than harder.” The first and foremost step for any data science management process is demonstrating the project goal and metrics to team members. However, explaining the goals and metrics will enable the workforce to provide the correct value to the product and client.
3. Never Assume a Good Data Scientist Is a Great Manager
There is a vast myth surrounding data management that having excellent technical knowledge improves the chances of the data science management process. But, the reality is the opposite. Instead, it is noticed that data scientist repeatedly needs to translate their
technical knowledge into excellence. Moreover, not all team managers will lead and work as project managers.
4. Manage Workforce
A good data scientist manager is responsible for managing the project and the people on the team. Good data management is curious, humble, and willing to listen and discuss problems and successes with others. Everyone on the team must understand that no matter how well-informed they are, they can only solve some issues. The collective team approach will yield better solutions and insights into the problems that must tackle than an individual.
5. Define the Process
A data science process that is effective is essential for data science management. The team that is working on the project will always discuss and approve the final approach. Discussions should also include frameworks like CRISP-DM that will help to structure communication between the data science team and stakeholders.
Tip: Explain the Accuracy Required and How to Handle “Wrong” Answers
At the beginning of every data science project, accuracy is always a topic of discussion. We spend a lot of time and energy determining “somewhat better” than a coin-flip accuracy, but this is insufficient when we risk lives in medical prediction applications by introducing false negatives. Each data science project will have something that surprises you; whether it’s an entirely wrong answer or something new, we learn about the world. You only need the plan to review results by humans and escalate the issue when they seem unfair.