In today’s data-centric world, the significance of data science in project management is immense. Despite its initial appeal, data science is often misunderstood. Its influence spans various industries and daily activities.
However, organizations frequently face challenges in applying effective project management strategies within the realm of data science. According to a 2021 report by Fortune Business Insights, the data science platform market experienced rapid growth, valued at USD 64.14 billion in 2021 and expected to surge to USD 484.17 billion by 2029. This impressive growth highlights the critical importance of data science. Nonetheless, despite its recognized potential to offer a competitive edge, many businesses still face difficulties in effectively integrating data science into their operations.
A recent MIT study reveals that only 20% of enterprises are actively engaged in AI initiatives, with only a small percentage of these having widespread implementation.
The key challenges are in effectively managing data science projects and aligning technical and business goals. This is where a data science project manager becomes crucial. A data science project manager can enhance project success by applying the right level of expertise to data science initiatives.
For a better understanding, Data Science is a field that utilizes scientific principles to derive strategies from data, encompassing computer science, hacking skills, statistics, mathematics, and domain expertise. Project management involves the planning, execution, completion, and oversight of projects.
In agile terms, project management emphasizes delivering substantial value through continuous feedback and rapid development cycles. Data science project management is a specialized approach that blends technical expertise with business objectives to ensure successful outcomes in data science projects. Key features of data science project management include an iterative and agile approach, reflecting the dynamic nature of data projects. Collaboration among various teams, including data scientists, business stakeholders and IT professionals, is also essential.
A major challenge in data science project management is ensuring that complex algorithms and models align with strategic business objectives. Data science project management demands a thorough grasp of both technical and business aspects to ensure that the project achieves its intended outcomes and provides access to relevant data science resources for knowledge advancement.
A data science project manager
typically supervises projects, overseeing the delivery of analytics and AI/ML initiatives. One of the roles of a data science project manager managing and overseeing the daily tasks and workflows of the project team.
Another role is outlining and defining tasks that align with the project vision; overseeing and documenting scope using a project management tool like Jira, Atlassian, or Rally.
Translating technical jargon into clear terms so the team understands the path to success and fostering a collaboration is another function.
He also identifies and addresses potential roadblocks, ranging from data quality issues to evolving requirements.
He or she clarifies the complexities of project roadmaps for data science initiatives.
Some positions may also require a technical background, where the data science project manager is expected to be skilled in Python, SQL, and visualization tools such as Tableau. These roles are often termed “technical project managers.” Regardless of the title, a solid understanding of databases, analytics, AI/ML, engineering, cloud systems, and the data science life cycle is essential for any project manager in the field.
Data science project managers possess the expertise needed to navigate uncertainties and deliver valuable insights. Some of the fundamental project management principles to consider when managing data science projects include
planning and scope definition. Here, the project manager clearly outlines the problem, deliverables, and success metrics before developing a plan of action.
Another principle is keeping it real. Instead of attempting to tackle the entire complex problem at once, set realistic timelines and clearly defined tasks to avoid chaos and ensure the project doesn’t become overwhelmed by its complexity.
A successful team fosters a collaborative spirit, bringing everyone together to achieve project goals collectively.
A data science project manager could also deploy the agile methodology. He employs agile methods, iterating and adapting as they acquire new insights. Here, it is advisable to learn from minor mistakes and adjust swiftly to prevent them from escalating into major issues.
The principle of drawing the map is also essential to create a plan before plunging into a project.
A data science project manager should designate someone to define the project scope and ensure that the team is clear on their objectives and destination.
He or she should also anticipate or identify potential risks such as shifting requirements or data quality issues so that they can be addressed early and prevent them from causing significant damage.
The plan should equally remain flexible and adaptable to accommodate sudden changes in data science projects. If new information emerges, the project manager should adjust the strategy as needed to ensure the project stays on track.
Risk management is another key principle. Develop contingency plans to address potential issues proactively and handle them effectively.
For proper data science project management, managers should be responsible for guiding the planning, execution, and completion of data-related projects such as data migration, integration, analysis, and governance. They handle compliance, security, and data quality while coordinating the efforts of analysts, engineers, architects and other key stakeholders.
Ikponmwosa, a data science project manager, writes from Abuja
– Ikponmwosa , writes from the Department of Political Science, Benson Idahosa University, Nigeria
