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Data Science Projects

Published June 12, 2017
Doug Rose
Author | Agility | Artificial Intelligence | Data Ethics

The heartbeat of most organizations can be measured in projects. Various teams across the organization set goals and objectives, develop plans for meeting those goals and objectives, and then implement those plans in the hopes of executing their missions on schedule and on budget. Project management has been the shiny hammer that has helped to nail down costs and meet deadlines throughout the process. It has been so successful that organizations often rely on project management even when it’s poorly suited for a given activity, as is the case with creative endeavors.

Data science is one area in which project management is a poor match. Data science projects often operate without clearly defined goals or objectives. Their primary purpose is to explore — to mine data for organizational knowledge and insights. Of course, sometimes, they have a clear objective — a specific question to answer or problem to solve or a data-driven software solution to develop, such as developing a machine learning algorithm to automate a specific task. To accomplish clearly defined tasks, project management may help even in the realm of data science, but for the most part, data science functions better with less goal-oriented management.

An Empirical Process

By its very nature, data science is empirical; that is, it relies more on observation and experience than on theory and logic. Data science projects are primarily exploratory and data-driven, not schedule- or budget-driven. One day, a data science team may be mining the data to identify new opportunities. Another day, it may be looking for ways to better understand the organization’s customers or to more accurately detect signs of data breaches or fraud. These efforts don’t fit into a typical project management framework. Data science teams are often operate outside the scope of other functions in the organization and often explore data that’s outside the scope of what the organization captures on its own.

When you set out on an exploratory mission, you don’t know specifically what you’re going to find. The entire purpose of the mission is to uncover what is currently unknown — to unlock the secrets hidden inside the data. Data science teams celebrate those eureka! moments, when they stumble upon unexpected discoveries. To maximize their discoveries, data science teams must be able to react to the data. They must be allowed to follow where the data leads and change course when questions point them in a new direction. If they knew exactly what to expect, they wouldn’t be gaining any new knowledge.

In general, data science looks for new opportunities or challenges current assumptions. It focuses on knowledge exploration and tries to deliver insights. It’s not about cranking out deliverables on a predetermined schedule.

Exploring Versus Planning

The difference between data science and project management is like the difference between exploring and planning. Imagine yourself exploring an unfamiliar area to find a restaurant. This would be an empirical process, similar to the approach a data science team would take. You would tour the area checking out different restaurants and their menus. You might even step inside the restaurants to check out their ambience and cleanliness and the friendliness of the staff and compare prices.

While you are exploring restaurants, you work up an appetite. You’re famished. Now, you need to decide what you’re hungry for, where and when you want to eat, how much you want to spend, and so on. You may even want to contact someone you know to meet you at the restaurant. In this scenario, you have a specific goal in mind — enjoying your next meal. To achieve that goal, some degree of planning is required. You switch from learning to planning, from data science to project management.

A Common Mistake

I once worked for an organization that tried to apply sound project management practices throughout the organization. The data science team was no exception. The team tried to adhere to the new policies by creating knowledge milestones and insight deliverables. Unfortunately, this particular experiment was a disaster. The knowledge milestones were imaginary constructs based on what the team already knew. They kept the team from exploring anything outside the scope of those milestones. Time constraints drove the team to focus on hypotheses that were easily proved or bordering on the obvious. Whenever someone ventured to ask an interesting question or attempted to challenge an assumption, that person was shut down because the team was afraid of missing a milestone.

Keep in mind that project management is beneficial to most organizations. Unfortunately, the same approach can have a chilling effect on a data science team. Project management discourages curiosity and uncertainty. It forces the data science team to merely try to verify what is already known. If they find anything unexpected, they dismiss it as a minor anomaly or a glitch instead of as a sign that they need to change direction or dig deeper for the truth.

By setting milestones and defining specific deliverables, you gamify the data science process in a counterproductive way. You end up rewarding the data science team for the wrong achievements. Instead of rewarding curiosity, questioning, and experimentation, you’re rewarding the team for verifying what’s already known.

Bottom line: Don’t think of data science as a project delivering a product. Think of it as exploration for driving discovery and innovation.

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