How to Maintain Focus and Deliver Business Value in Data Science Projects
Starting a new data science project often comes with some excitement and the temptation to immediately dig into data sets to discover interesting insights and to apply techniques one has recently learned. Over time, the excitement quickly wanes off when faced with recurring challenges involving code bugs and data anomalies.
To reduce waning inspiration when executing a data science project, it is important to maintain the right mindset and the principle of progress over perfection to ensure 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 time commitment involved and the challenges encountered in the planning process. Structuring how to execute a data science project, whether an official work assignment or a pet 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 since some processes occur 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, analyses, modeling 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 tip for 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 who invest time in planning key stages and resources of the data science process before executing, increase their chances of remaining motivated to provide valuable actionable insights that influence business decisions and strategies.
