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Data Analytics in Project Management
Written By: Vidya Halgeri

Expect the best, plan for the worst, and prepare to be surprised- Denis Waitley

Project Management – A mystery or a mere process?

Project management comprises the plan and execution of ideas to perform a specific task, event, or duty. Perfect project management offers vision & mission, motivation to stick to aim by removing roadblocks, training, and inspiring the team to utilize their best potential.

Managing projects means making decisions. It is the science and art of organizing all the components of a project. It includes conceptualization, interpretation and initiation, planning and execution, performance monitoring, and project closure.

Gartner predicts, most of the Project Management techniques followed by companies nowadays will be eliminated by 2030. Artificial Intelligence will take over all the complications.

The principal components of project management are:

  • Time- the calculated and predetermined duration of the work
  • Cost- the budget assigned for the work
  • Scope- innovations or advancements delivered by the project
  • Quality- the standard of the result of the project

Key Players- Knocking the ball through the court!

If you’re a project manager, you are the face of the project.

The role of the project manager in the future will become more strategic as opposed to the current tactical scenario.

Numerous businesses recognize the importance of investing in data science for innovation, but at the same time, not all of them know how to turn this data asset into business value. So project managers are the ones who leverage data to realize project benefits. Technical project managers have to understand and grab this opportunity to learn how AI project management is distinct. They can intellectually analyze how they can best prepare for the changing landscape of AI in project management.

Project team members adhere to the requests of both their functional managers and their project managers. Functional managers are responsible for coordinating and supporting their staff’s assignments among different projects. Also, they provide the necessary resources for their team to perform their tasks as per the highest standards of technical excellence. Upper management creates the organizational environment; oversees the development and use of operating policies, procedures, practices. It also encourages and funds the development of required information systems.

Importance of Data Analytics in Project Management

Data analytics helps to make better decisions and resolve typical project problems giving it a proactive approach.

Artificial Intelligence helps make better choices leading to improving the chances of delivering projects on schedule and budget. Data Analytics techniques facilitate project managers to use various interpretive reports and drill-down charts to break down complex project data and predict their behavior and outcomes in real-time.

Project Performance Analysis is the key for project managers to decide the efforts required to achieve desired goals. Deep and shrewd analytics can help improve resource utilization and better prognosis of revenue and costs. The project manager can gauge the estimate of work to complete the tasks envisioned to figure if they will get done in the scheduled time.

Project managers can utilize analytics to strategize actions. Data analytics amplifies the success rate by providing insights to educate everyone about how resources perform. Deep down, every company and its employees know what needs to happen. You need to commence and execute a data strategy that moves the needle for your organization. Data Analytics magnifies positive business results.

Data Analytics helps Project Managers to stay ahead of the curve.

Secrets to an Effective Project Management — ‘Data’ is the king!

Various techniques are available to manage projects. A tailored plan can be achieved, by incurring a well-structured project management approach, to fit any type and size of the project. The aspects to consider when using data for planning a project are:

1. Assessing the project risks

Accessing project risks means understanding how an uncertain event or condition can affect a project’s objectives positively or negatively. Risk can have an impact on people, processes, technology, or resources.

By embedding data analytics into any project management process, executives and managers can identify, rank, and prioritize those risks in a more informed manner. It is crucial for any project to regularly assess risks, as projects undergo changes or lead in a new direction with the minutest diversion in their execution.

Things to be considered while performing risk analysis:

o Size and complexity of the project

o The client’s risk tolerance

o The competence of the risk manager

2. Projecting the date of completion

Data analytics can be ventured by project managers to uncomplicate the whole process, by keeping an eagle eye watch on the early warning signs. This helps in taking proactive actions. When this data is analyzed, it leads the project managers to find out which process of the project is taking an abnormally long time. Empowered with this info project managers can engage with their teams early and solve the blockers.

Completion of tasks can be predicted by measuring the rate at which the work is being carried out. Data regarding the complications can be studied thoroughly, and the date of completion of the project can be predicted.

3. Measuring Success

In the past, it was quite a challenge to measure success. Only the surveys or the direct customer reviews would give some information. Now businesses have gained the capacity to learn more about their customers than ever before. This breakthrough has been solely possible by Data Mining. This process has enabled the project managers to understand the trends by sifting through large quantities of data. Finding impactful trends allows project managers to end with successful projects.

Data analysis enables the project managers to make more informed decisions which reduces the possibility of uncertain situations which in turn minimizes the complexity of the projects.

Thus making the measuring of success faster and more accurate. This leads to the ultimate truth, that gathering information is vital to success. The key to making good business decisions is ensuring that the data is relevant enough to drive meaningful actions. Good project managers are capable to foresee the bigger picture in the data analyzed and using it to tell meaningful stories.

But are companies ready to take their projects into real-time?

Nearly all businesses agree with the fact that it has become critical for them to adopt this real-time application in some or the other way into their project lifecycle. Many are already using the best of Project Management tools. AI can add a lot of value to this project life cycle due to the sheer number of moving parts and dynamic nature of project management.

Systech persistently supports all the customers to develop, design and implement, data and analytics projects with ease through customized services. Your team can achieve the desired results with the help of our most competent project managers, technical engineers, data engineers, and analysts. Our data & cloud analytics mixed with AI/ML technology initiatives will lead you to your next-generation results faster and more cost-effectively than you’ve ever imagined.

Has your company already taken this challenge?

Are you on your path to magical success?

The Systech Solutions, Inc. Blog Series is designed to showcase ongoing innovations in the data and analytics space. If you have any suggestions for an upcoming article or would like to volunteer to be interviewed, please contact Vidya Halgeri at vidyah@systechusa.com.

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