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Use of Data Analytics in Project Management

Bohitesh Misra, Co-Founder at DECISIONTREE ENDEAVOUR  

Use of Data Analytics in improving Project Management controls

As a certified Project Management Professional (PMP) from Project Management Institute (PMI) and a Data Science enthusiast, I have tried to share my understanding based on my experience in developing and managing Mission critical projects. In this article, you will learn how Data Analytics can be used in improving the Project Management controls in a development project.

Managing projects means making decisions. This process can be supported by data mining and machine learning techniques, based on selection and analysis of project data in order to make better decisions and resolve some typical project problems.

Data plays a significant role in any organization. Using analytics, managers and executives can watch for early signs of slippages in terms of budgets, costs, and timelines and take corrective action. Analytics also helps managers capture the rate of work, so they can easily predict whether the project will be completed on time. Managers can use a burn-down chart, for instance, which is a graphical representation of work left to do over time.

Moreover, deep and insightful analytics can help you improve resource utilization and better forecast revenue and costs. With analytics, organizations can take a broader view and combine unrelated data streams to offer deep insights into projections and early warning signs in complex projects. This role can be taken by Program Management Office (PMO) in an organization.

How can project managers make use of a data-driven approach to improve project outcomes?

Gartner says, 80% of today’s Project Management tasks will be eliminated by 2030 as Artificial Intelligence takes over. Artificial Intelligence will not be replacing anyone’s jobs just yet, however, AI will help make better decisions leading to improving the chances of delivering projects on time and on budget. Traditional project management functions like planning, data collection, tracking, and reporting will be taken over by machine learning algorithms.

The role of the project manager will gradually evolve into one that is more strategic as opposed to current tactical role. AI shall be a work augmentation tool, not a human replacement and AI cannot manage a project, so Project Manager’s tedious status reports and messy resource scheduling could be greatly improved with AI, Machine learning and Robotics Process Automation (RPA), but it can’t gather requirements or get stakeholder buy-in. However, Organizations will have to adopt the use of AI in projects and hence they have to merge the power of humans with that of machines learning for better managing their critical projects.

With applications of artificial intelligence already disrupting industries ranging from finance to healthcare, technical project managers have to grasp this opportunity and learn how AI project management is distinct and how they can best prepare for the changing landscape for use of AI in project management. AI and Machine Learning will help in enabling a fully digital program management office (PMO) in future.

 Data Analytics techniques can enable project managers to use various analytical reports and drill-down charts to break down complex project data and predict their behaviour and outcomes in real-time. Project managers can use this predictive information to make better decisions and keep projects on schedule and budget. A data-driven analytics approach enables project teams to analyze the defined data to understand specific patterns and trends. Executives can use this analysis to determine how projects and resources perform and what strategic decisions they can take to improve the success rate.

A report by global management consulting company McKinsey, discovered that US $66 billion was “lost” across 5000 separate projects. This was due to them exceeding their lifecycle, poor planning, and the wasteful expenditure on the wrong kind of talent.

Data plays a significant role in any organization. Using analytics, managers and executives can watch for early signs of slippage in terms of budgets, costs, and timelines and take proactive action. Analytics also helps managers capture the rate of work, so they can easily predict whether the project will be completed on time. Managers can use a burn-down chart, for instance, which is a graphical representation of work left to do over time.

Moreover, deep and insightful analytics can help you improve resource utilization and better forecast revenue and costs. With analytics, organizations can take a broader view and combine unrelated data streams to offer deep insights into projections and early warning signs in complex projects.

Data Analytics in Project Management

A recent Project Management Institute survey confirms that artificial intelligence (AI) disruption is happening—81% of the 551 survey respondents report their organization is being impacted by AI technologies.

As we say that learnings from the past helps makes our future better. As a Project Manager we should always connect the dots looking backward & forecast the projected path to future using Earned Value Management. This will bring a prospective whether the current projected path & pace will take us to the desired destination. The past performance & remaining quantum of work in project will help us for the course correction. Project manager always does a balancing act among the various Constraints set at start of a project. To be in control of Project he always requires to have check on project performance matrices. Project Performance Analysis is key for project manager to decide the revised rate of efforts to achieve desired goals.

The complexity of managing projects within the Triple constraints has been increasing day by day. Quick browsing through the changes over past one decade tells us how fast the “Complexity” of managing projects has been changing. Various factors contributing and will continue for next one decade to name few, as, shortening product development cycles, changing customer expectations, exponentially increasing usage of internet as well as a greater number of millennials in the project teams.

One of the ways to manage this complexity and the need of changing world, is using digitization. The digitization of Project Development phases will provide all synchronized database available to each stakeholder appropriately and same can be used for Managerial decision making. Building Analytics on this database, Risks affecting Project Performance Parameters – Time, Cost, and Quality can be effectively predicted and controlled. In addition, status will be available for each project to individual project teams whereas Portfolio Dashboard will provide bigger picture for managerial decisions on Strategies & Organizational Priorities. Because of its real-time nature, it can be available across the world at the same time providing a common platform to network and common language to interact with team.

If you do not measure your data, how can you manage or optimize your business?

Project Management is no different. Effective management of projects entails efficient management of the uncertainties and risks on the project. It requires today’s project managers to use analytical techniques to monitor and control the risks as well as to estimate project schedules and costs more accurately with analytics-driven prediction. Project-based data with analytics can enable project managers and executives to measure, observe, and analyze project performance objectively and make decisions and commitments based on facts.

Digitization of Project Management process and application of Analytics will provide strategic value creation on the part of organizations.

Project Analytics:

Analytics can be defined as the systematic quantitative analysis of data to obtain meaningful information for better decision making. It involves the collective use of various analytical methodologies such as statistical and operational research methodologies, Lean Six Sigma, and software programming. Though Analysis and Analytics terms sounds similar but they do have some differences.

Project analytics can help project managers handle complex projects and keep them on-schedule and on-budget. Using analytics, project managers have the ability to go beyond simply capturing data and completing tasks as they are completed. Now, they can find out a multitude of information, including exactly how projects are performing, and whether or not they are in line with the overall objectives. Analytics provides project managers the ability to make strategic decisions and improve project success rate

Quality of Deliverable

Managing a new project can be unnerving task. There are different stakeholders, approvers, teams, budgets, outcomes and high expectations to manage. To manage all of these, analytics have become a major part of modern-day project management.

In decision-making, biases effect the decision making and it means that it is likely that decision-makers will search, use and interpret only the type of information that supports their existing beliefs and will focus on only one alternative when making decisions, reducing the likelihood that it will be a good decision.

As a project manager, you need to understand how analytics can reduce your workload, improve processes and enhance the outcomes of your project. Quality is an ultimate measure of your project’s success upon delivery. Analytics help you plan, monitor and review the quality throughout your project.

Assisting Strategic Decisions

Analytics helps organizations make decisions that are based on facts instead of gut feeling. Real-time project analytics reveals a wealth of information that helps organizations align with their strategic objectives. Analytics allow managers and executives to deepen their understanding of how ongoing and proposed projects fit into the overall portfolio and organization vision.

Lowers project costs

Big data analytics means collecting more and more data that you can use to predict future events and trends within your industry easily. This helps make your resource forecasting and planning process more efficient since you’ll have a library of relevant data to determine the right budget, timetable, estimates, and more for cost-effective project implementation. 

Insights from analyzing big data can also make your output efficient because it allows you to identify and assign the tasks your team members are excellent at and provide them with accurate information to complete their jobs.

This reduces potential project errors and inaccuracies that could cost you a ton of your resources, and, if not corrected, could set back your operations for days, weeks, or months. 

Improves resource management

Data analytics helps you extract the right information to understand your project needs. This allows you to see available resources and how these two matches up for efficient resource allocation and, in turn, seamless project operations.

For instance, the moving parts and changes in project management, such as your budget, can significantly impact your deadline and resources.

Using big data insights and resource management solutions equips you with the information and tools to uncover the right approach to handle project plan changes easily. 

It will also help you predict project outcomes and make better strategic decisions to ensure the most cost-effective resource spending.  

Enhances project risk management

Project management is dynamic and affected by many internal and external factors, leaving it open to various risks that could negatively impact your delivery outcome.

The key is to actively and regularly identify and manage your project management risks, which means you’ll need to document all risk events and the troubleshooting and firefighting activities.

Data analytics allow you to analyze your project issues and risks to manage them better and minimize their impact on your processes and results.

This also helps you develop the right methods and use the right tools to identify, analyze, prioritize, monitor potential issues, and create solid risk response strategies.

Benefits of use of Artificial Intelligence in Project Management

  • Automate repetitive, tedious tasks so you can spend more time on problem-solving
  • Create Project database and use historical data of completed previous projects to perform calculations and predictions, improving the accuracy of the results
  • Perform risk modeling and analysis based on any changes to scope, available resources, budget, etc.
  •  Optimize resource scheduling and allocation on projects.

AI and ML can be used to:

  • Assess the type of resources the project needs based on the tasks required, such as time to build a custom workflow and then perform quality assurance testing.
  •  Use historical data to calculate the length of time for tasks.
  •  Reference a database of people and their skills and select the best person for the tasks required.
  •  Review the work and time-off schedules of all the people available to work on a project.
  •  Estimate how many tasks an individual could complete when compared to their weekly report of productivity.
  •  Compare the proposed resource schedule against historical data to identify inconsistencies and improve the accuracy of the proposal.
  •  Propose the best possible schedule of resources with the team available


Data Analytics provides plenty of opportunities to advance your team members’ skills and optimize your project management implementation process. After all, regardless of your objective, you can always find data to influence your project results.

Leverage data to analyze past, real-time, and future information to model the probability of your project outcomes and use it to make data-based decisions and improve your efficiencies.

The market for data analytics and the business intelligence is predicted to grow to $22.8 Billion. According to the Project Management Institute, there will be a demand for 87.7 million project managers by 2027. With both these disciplines growing at an explosive rate, it only makes sense to use powerful tools interwoven into the organization’s fabric to create a more sustainable competitive advantage.

About the Author

Bohitesh Misra PMP, with more than 25 years of IT and Project Management experience in developing and managing some large mission critical applications in the field of manufacturing, transport, solar, Fintech, Edutech. Bohitesh is the co-founder of Decisiontree Endeavour (, an all-in-one learning platform for data science, project and portfolio management.

Winner of multiple CIO awards, he takes pride in being a Technology Evangelist and a Data Science enthusiast at heart. Bohitesh can be reached through email at and connect on linkedin at

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