5 Reasons Why Analytics is Vital to Project Management

5 Reasons Why Analytics is Vital to Project Management

From the idea to the execution stage, the project life cycle entails identifying the risks, budget, project schedule, and adhering to the timelines. It’s a great idea to use project management tools such as Wrike, Jira, etc. to meet the demands of increasingly complex projects by capturing intricate data at various stages.

But how do we extract intelligence out of the project data?

The short answer is via Project Analytics. Think of it as a plug to your project management tool that lets the data from the tool be processed, projected, and visualized for better decision making.

Analytics has changed drastically in the past few years, and its power can be applied to project management too. This blog gives you the top five reasons to go for analytics to enhance any project outcome.

If you think Project Management Analytics is just about reporting, you are mistaken!

Analytics comprises Business Intelligence and Business Analytics.
Business Intelligence (BI) is a combination of processes and tools to gather, store, and analyze big data from various sources, while Business Analytics is about predicting the outcomes and direction of the project. 

Analytics comprises Business Intelligence and Business Analytics.
Business Intelligence (BI) is a combination of processes and tools to gather, store, and analyze big data from various sources, while Business Analytics is about predicting the outcomes and direction of the project. 

5 Ways Analytics Helps Project Managers

  1. Visualization
  2. Forecasting 
  3. Process Integration Across Different Disciplines
  4. Managing Project Portfolios
  5. Descriptive to Prescriptive Analytics

Visualization – An Aid to Decision Making

Project Management Dashboard

Wouldn’t it be great if project management professionals could recreate the project in charts and dashboards with the most essential data points? That’s the power of analytics, it’s like moving into a data warehouse and self-serving the output from the data sets.

Visualization is an important component of data analytics which simplifies the data so you see the big picture easily. Bar charts, pie charts, and 3-D charts like segmentation charts, treemaps, etc. make project decision-making simpler and predicting the outcomes of the project easier. 

Project Managers are looking beyond excel and google sheets for ways to better visualize their reports. There are 5 reports for project managers: Project Overview Report, Baseline Report, Timesheet Report, Task Progress Report, Employee Productivity..


Handling high budget or critical projects needs a bit of extrapolation. Project forecasting often involves analysis of the project performance history whether the project is likely to be profitable in the future. Managers can then decide to forego new projects or stop current projects if forecasts are unfavorable. 

With forecasting you can easily achieve the following:

  • Trend analysis – Identifying patterns in the historical data and using those to clearly estimate the risk, cost, variance involved in a project.
  • Break-even analysis – As the name suggests, break-even analysis estimates if the returns can match the cost of the project.
  • Predictive analytics – Uses predictive models and AIML algorithms to predict the future of the project Key Performance Indicators(KPIs).
  • Prescriptive analytics – Tells how we can correct something in the project before it occurs.

Process Integration Across Different Disciplines

Imagine you are running multiple projects with stakeholders involving different departments. Your reports will not be clean until you have factored in data and processes from various departments. 

Project scope can expand across different disciplines like marketing, sales, customer support, etc. to get a holistic picture of the project KPIs. We need analytics software that can help draw insights from multiple sources so that project dependencies are covered. In the above screenshot, you can see how support requests from help desk are being mapped to various tasks within the project.

Business Intelligence for Wrike can help with syncing information from various apps like Salesforce, Wrike, Aircall, and building real-time visualizations from multiple sources in one place.

Managing Project Portfolios

Managing Project Portfolios

One of the useful applications of analytics is Project Portfolio Management (PPM). Strategizing is more important than executing, who else would know it better than Project Managers. When there are multiple projects and limited resources, selecting and prioritizing the most valuable ones becomes necessary. The process involved in selecting the right project and parallelly managing projects for profitable outcomes is called Project Portfolio Management.

Good analytics software can help identify projects that can be prioritized for best results and also predict the outcomes, so managers can focus on the right projects at the right time.

Descriptive to Prescriptive analytics

Descriptive to Prescriptive analytics

Project management in the past was restricted to descriptive analytics – understanding what went well and not so well in the project. According to Gartner, analytics has matured to four levels. While descriptive and diagnostic analytics look at what happened and why something happened, predictive and prescriptive analytics predicts and makes corrections to minimize the risk that’s forthcoming.

With Project Analytics companies can bring Project management into the predictive and prescriptive analytics stages. Project managers can then completely control the project rather than fall victim to the less calculated project execution strategies.

Key Takeaways

Project Management methodology was different a decade ago when managers had to just keep the project running. Now, it’s more about strategizing, collaborating with the project team, agile project management, and project portfolio management. Analytics can simplify these tasks for modern project managers. 

Via happyfox blog