What is Data Analytics in Project Management?
Data analytics is a powerful feature in project management software that enables project metrics to be collected and evaluated in detail.
Businesses that use the data analytics feature in their project management software are able to detect and correct issues early on in a project.
Data analytics helps project managers with the 5 phases of project management:
- Conception and initiation
- Definition and planning
- Launch or execution
- Performance and control
- Project close
How Data Analytics is Used in Project Management
Conception and initiation
The conception and initiation phase is the step in project management when the expectations of project stakeholders are established, and the determination is made whether or not a project should be given the green light to go ahead.
As part of the conception and initiation phase, feasibility testing might need to be performed.
Data analytics is useful in feasibility testing to learn whether deliverables meeting specifications can actually be produced in a certain time. In feasibility testing, data analytics can be used to develop accurate forecasts and predict outcomes based on historical data.
If feasibility testing indicates that a project should proceed, then a project initiation document, or PID, should be prepared.
The PID should clearly set forth the purpose and requirements of a project. It is not necessary in the PID to include the technical, more intricate details of how the project is to be completed. However, while preparing the PID, project management should be thinking about what metrics should be collected and measured so that insightful data analytics can be performed.
Definition and planning
In the definition and planning phase of project management, data analytics are used to help establish specific, quantifiable, realistic goals for a project.
Using data analytics, predictive models can be created so that project management can visualize how alternative rates of productivity can influence how a project is completed.
For example, a predictive model can show how if a certain task is completed late, it detrimentally affects the next or future tasks.
Conversely, a predictive model can show how early completion of a given task can create additional time for completion of a more complex task.
Launch or execution
The launch, or execution phase of project management, is the phase in which the project deliverables start being created or developed.
Data analytics are useful in this phase of project management to help establish how resources should be assigned and allocated, and how various tasks of a project should be scheduled.
Performance and control
Throughout a project, project managers use data analytics to know the rate of completion of specific tasks, and how a project overall is progressing, both in terms of time and quality of the deliverables.
Project managers establish and pay attention to Key Performance Indicators, or KPIs, to accurately measure progress.
Data analytics can help project managers to adjust strategies by revealing:
- When resources will become unavailable
- When equipment will need to be repaired or replaced
- When costs will increase or decrease
Further, data analytics ensures that project managers are able to quickly detect:
- Where budget overruns might occur
- Where predetermined time frames might be exceeded
By detecting these issues quickly, project managers prevent projects from becoming stalled, or worse, rendered unfeasible.
Data analytics helps project managers avoid being forced to make unclear, uncertain strategic project decisions based on gut-feeling. Rather, by using data analytics, project managers are able to make decisions based on objective, quantitative information.
In addition to being useful for project managers, data analytics is useful for project stakeholders.
Project stakeholders appreciate being provided with objective, meaningful status updates, rather than vague and rather uninformative, “the project is progressing well” messages from a project manager. Data analytics makes it possible for project stakeholders to receive valuable updates.
Upon conclusion of a project, when all project deliverables have been provided to the project stakeholders, the project close phase of project management begins.
In the project close phase, data analytics is helpful to understand how various project team members worked together and how processes used in the project contributed to completion of the deliverables.
If certain tasks were not completed within their allocated time frames, or if deliverables were not built to specifications, data analytics can help project management to understand why these shortfalls occurred.
Why is Data Analytics important in Project Management?
By using data analytics, project managers can help to ensure that projects are completed on time and that deliverables meet specifications. When used properly, data analytics allows unexpected problems to be detected quickly and remedied before they can block completion of a project.
How can Project Managers make use of a Data-driven Approach to Improve Project Outcomes?
As mentioned earlier, project managers want to avoid having to make strategic decisions in a project based on subjective evaluations. The data-driven approach of data analytics helps project managers from being forced into this difficult position, and helps to improve project outcomes.