Finally, all data were cleansed and ready to analyze. Andy started overenthusiastically to visualize the data to get a first impression of the data. He had many dimensions and variables such that he spent several days visually analyzing them and determining the best methods to apply. At the end of that week, the team manager told him that he would need a draft presentation about the outcomes next Tuesday because the team manager had to present it in one week to a steering committee.
Andy told him that he has no results yet. But there was no space for negotiations. On Tuesday, conclusions had to be delivered and integrated into a PowerPoint presentation.
Hastily, Andy produced some regression analyses and integrated them into the presentation.
After the steering committee meeting, the team manager told him that the project would not be carried on.
Andy was very frustrated. That was his second project, and the second time it ended with the same decision. He has chosen this position because of the potential for doing great data science work on a large amount of data available.
This story is a real case, and it is not an atypical situation in corporations. I assume that some of you have already experienced a similar situation, too.
The reason that this happens is not your skills.
When thrown into a data science project in a corporate environment, the situation is different from the previous learning context.
My experience is that most data scientists struggle to manage the project, given the many corporate constraints and expectations.
More than a few data scientists are disappointed and frustrated after the first projects and looking for another position.
Why?
They are trained in handling data, technical methods, and programming. Nobody ever taught them in project, stakeholder, or corporate data management or educated them about corporate business KPIs.
It is the lack of experience with unspoken corporate practices.
Unfortunately, there are more potential pitfalls in that area than with all your technical skills.
If you know the determining factors, you can plan your data science tasks accordingly, pursue satisfying projects, and steer your work.
In the following, I give you the eight most important drivers for the model approach selection in the corporate environment and how to mitigate them.
1. Time, timelines, and deadlines
What you need to know
Corporations have defined project processes. Stage-gate or steering committee meetings are part of that where outcomes must be presented. Presentations have to be submitted a few days in advance and must contain certain expected information. Also, corporates are always under pressure to deliver financial results. That leads to consistently tight deadlines. These processes are part of the corporate culture, unspoken, and supposed that the employee knows them.
How to address it?
Ask, ask, ask. Ask about the milestones, e.g., the meeting dates where project decisions will be made.
Set up a time budget. Start at the milestone’s date and calculate backward a project schedule.
Include not only your tasks but also the surrounding actions, like coordination meetings, presentations, and deadlines for submitting the presentations. Do not forget that there is a review round for each presentation, and you have to consider adding a few days in advance of submission. Include time margins for unexpected tasks and troubleshooting.
#data-science #data #project-management #model #machine-learning
