By Kristi Smith
Effective data project management is key to the success of any data project.
Over the past five years of managing these projects at Red Pill Analytics, I’ve learned that having the right strategies in place makes all the difference. Here’s what I’ve found works best:
1. Define Clear Objectives
Start with a clear understanding of the project goals. Identify the key questions you want to answer or the problems you aim to solve with your data. In my opinion, this should always be driven by the business. Sure engineers will likely have some ideas on improvements that can be made to the data pipeline or a certain query or process, but in general projects and the high-level objectives should come from the business.
2. Assemble the Right Team
Bring together a diverse team with skills in data analysis, engineering, and visualization. Collaboration between data scientists, developers, and stakeholders is vital.
3. Choose the Right Methodology
Agile, Waterfall or Hybrid. Adopting the correct methodology that works best for the team is imperative. Sometimes this direction will come from the PMO and for the fortunate few this will be left up to the team and project manager to decide what works best for them.
At Red Pill Analytics we offer an approach called “Elastic Delivery.” This approach combines Agile methodology with flexible, scalable team structures. We provide a team with skilled professionals to perform the project work and when necessary we can quickly deploy an “elastic surge”, which supplements existing teams with additional capacity. This allows us to quickly respond to changing demands and provide responsiveness without long-term resource commitments. Check out more info at Red Pill Analytics.
4. Use Project Management Tools
Leverage tools like Trello, Asana, Jira or Microsoft Project for tracking tasks and progress. These tools enhance communication and provide visibility into the project status. Team members should undoubtedly have access to what ever tool you are using to track progress, so they can be kept in the loop without have to go through the project manager.
5. Communication
Maintain open lines of communication within the team and with stakeholders. Regular updates and feedback sessions are crucial for alignment. Whether this is daily, or bi-weekly stand-ups or weekly meetings with the Director of the team. It is the PM’s responsibility to ensure everyone is updated and aware of all project happenings.
6. Measure and Analyze Progress
Set key performance indicators (KPIs) to measure success. Use data analytics to evaluate project milestones and make informed decisions. In my opinion this will come with the maturity of the team. Giving the team a chance to gel and to figure out roles, etc is priority #1. After the team has established that they have a solid foundation that is when I think KPIs can come into to ensure the team is trending in the right direction in terms of productivity and efficiency.
7. Post-Project Review
Conduct a thorough review after project completion. Analyze what worked, what didn’t, and how processes can be improved in future data projects. These can be very helpful for all project team members including the project manager if people communicate openly and honestly. The purpose is not to place blame or point fingers, but to have an honest discussion on how the team can move the needle forward to improve efficiency and overall team performance.
Successfully managing data projects requires a blend of structured planning, flexible execution, and continuous evaluation. By focusing on team buy-in, collaboration, and effective communication, project managers can navigate the complexities of data projects and drive impactful outcomes.
If you have any questions on the services we provide or want to chat more about our Elastic Delivery please feel free to drop me a line at kristi.smith@redpillanalytics.com.
Kristi Smith, PMP, MS
For more data tips and tricks, check out our blogs or browse the RPA blogs at Medium.
