This blog post is part of a series of guest publications by Excelion Partners. Check out their blog and read about their projects here.
Your data team is typically comprised of individuals from different backgrounds, with a variety of experience, skill, and knowledge levels, as related to data science and artificial intelligence. This makes communication and collaboration absolutely imperative to a successful data science project.
Get the Team on Board
It’s important to have a strong foundation for your data team to build off of. Ensure your team understands what you’re doing and why you’re doing it. You want to get rid of any idea that AI is magic — it’s a science.
Make sure you’re on the same page about the insights you are looking to create and the outcomes you are looking to achieve. As a result of your data science project, you should be saving money or making money. Ensure this is clear with your team before moving forward.
Be Transparent
No one on your team benefits when you don’t share what’s going on with your analytics activities. Instead, communicate openly about it. Discuss what you’re doing to find results, why you’re looking for those results, and what the outcome of this project will be.
Everyone needs to have a voice in order for the model to be successful. Encourage your team to share any input they may have throughout the process.
Utilize Recurring Quick-Hit Meetings
Quick-hit meetings are 30-minute, repeating meetings that happen weekly to bi-weekly, to create a feedback loop and ensure that collaboration occurs. This is not a project checkpoint for budget, scope, and timeline discussion; it’s a collaboration checkpoint to be utilized for data quality and business value.
As part of these meetings, your team should be checking if the numbers look right. Do they make logical sense? If not, why might these numbers not be what you expected?
This is a crucial step. In this example from Excelion Partners, they were working on a dataset that showed when a specific operator was working on a machine, the machine had the most failures. If they took the data without talking to the business and built an algorithm that predicted machine failures, that operator would have been a key variable in predicting those failures.
However, knowing that collaboration is important, prior to building an algorithm they worked with the business on the data. What they discovered is that the specific operator was doing twice the work of any other operator! Thus, of course, that operator would have more machine failures and should not be considered in predictive failures without accounting for that volume disparity.
When working on enabling a data team, a question that often comes up is how to influence or better create a data-driven culture. This method is one of the most effective ways to educate your organization on data-driven processes and promote a data-driven culture while realizing business benefits.
Encourage Non-Data Scientists to Get Involved
The non-data scientists on your team just might be your most valuable asset. Take advantage of this and explore how you can upskill employees with a business background to enable enhanced analytical activities on their end.
These individuals are called Citizen Data Scientists and when you bring them into your project, you benefit from the diverse background and set of experiences they have, unrelated to data science.
Transparent communication and overall collaboration across your data team are both critical to a successful data project. Enable these by communicating thoroughly at the beginning and throughout a project, scheduling recurring quick-hit meetings, and getting all of the right people involved at the appropriate stages.
This, however, is by no means an easy task. According to Gartner, "Data science and machine learning initiatives are increasingly popular, but they frequently fail to realize business value due to mistakes in project execution." Get a copy of the report "Follow 4 Data Science Best Practices to Achieve Project Success," compliments of Dataiku, to read about the four best practices to ensure the success of data science projects.