How to Maintain Focus and Deliver Business Value in Data Science Projects
By: Yvette Kondoh
Tags: Career Advice, Data Science, Blogathon

Photo by Prateek Katyal on Unsplash

Starting a new data science project often comes with some excitement and the temptation to immediately dig into data sets to discover interesting insights within them and to apply techniques one has recently learned. However, in reality, the excitement may quickly wane off when faced with recurring challenges involving code bugs and data anomalies which may take a while to resolve especially if one has not experienced them before. For this reason, it is important for a data scientist to develop a mindset and a process workflow that will ensure a sustained motivation from the onset to the completion of the project.

From my work experience, I have learned the important role structuring one’s thoughts and process flow plays in maintaining inspiration during a data science project, no matter the challenges encountered in the process. Structuring how to execute a data science project has the advantage of keeping the data scientist focused, motivated, and prepared to show the progress made at any point in time during the project. Some events in the process flow may happen sequentially, in parallel, and even for most, iteratively.

In executing a data science project, I apply a 20/60/20 rule. By this rule,

20% of the project time is dedicated to thinking through and planning the methodology for analyses while understanding the business context, business problem, project requirements, and background of stakeholders I will be reporting to,

60% of the project time to actually do the data science work which often involves data cleaning, transformation and analyses and

the last 20% to think through the results from the analyses in order to accurately and clearly communicate the business value of key insights to the project stakeholders.

Another trick to staying motivated during a data science project is to leverage techniques, tools, past project experiences, resources, user-defined functions you or a team member has created and used on past projects. By doing this, there is less time spent trying to reinvent the wheel to solve a problem that has already been solved and more time invested in discovering and translating key insights to recommendations that have a business impact.

Whereas staying motivated, focused, and inspired during a data science project is important, more crucial is the ability of a data scientist to effectively communicate relevant results of analyses done to project stakeholders and decision-makers to guide business strategy and actions. The art of crafting and telling compelling data stories using techniques such as the Pyramid Principle and the Rule of 3 help a data scientist refine and summarize the many insights generated from the data into a few main takeaways that stakeholders can act on to resolve the business problem the data science project attempted to solve.

Data scientists that invest time in planning key stages of the data science process and developing the mindset of a business owner seeking to solve a business problem increase their chances of remaining motivated during data science projects and providing valuable actionable insights that influence business decisions and strategies.