The new era of project management - powered by data science and AI

We live and work in a fast-paced environment where technology is evolving. AI and data science (DS) are technologies that have changed almost every sector and feature of a business process. But what is the most important element of a business process? Well, it is project management.
A diagram.
Any business goal can only be achieved through professional project management. And the "boon" of "data science and AI" technology is currently driving excellence in project management.
Let's take an example from two different perspectives: Let's say you intend to open a bakery. A project manager without data science skills will choose a location where there is no existing bakery nearby. And why? Because this business venture would be most profitable in a location with the least number of competitors. A project manager with data science skills will choose a location where there are numerous bakeries. But why? Because this prediction is based on real-time data analysis and not on outdated traditional justifications.
Yes, it's 2022, and predictions are no longer that simple, and that also applies to project management.

What is project management from the perspective of data science?

Project management applies knowledge, tools, skills, and techniques to provide something of greater value back to people. The examples of projects are: 
  • The construction of a building.
  • Relief effort after a disaster.
  • Increasing sales in a new market.
But if you think carefully about the three examples above, you cannot deny that they require an extreme amount of data analysis. Regardless of the project example given here, you need to collect relevant data, make predictions, gain insights and plan the project steps accordingly. 

In which way does data science become relevant to project management?

Data management stores, organises, filters and maintains the collected data. The efficiency of project management depends on the screening, organisation and maintenance of important data.
Let's say you are analysing the potential customer base of a product. You focus on the data set that answers the question, "Why is this product so popular?" But you've ignored the research that asks, "Why do people dislike this product?" Your ultimate perspective and project planning and results will be inaccurate.
Analysing and managing data reduces project complexity, making decision-making easier for the project manager.
So now you know that world-class data management is almost unavoidable for effective project management. If data management is done unscientifically, then you are approaching the risk of project failure. So this is where the correlation between project management and data science comes into play.

How does data science power up the efficacy of project management?

Data science in Project Management has shown how it can be a successful key for project managers. The three main benefits of data science and AI in project management are as follows:
1. Better business insights

A project manager needs to identify the most profitable insights from a range of options presented to them by data analysts. AI-powered insight discovery tools help project managers see the correlation between different types of data and trends to make the most profitable and future-proof decisions. Basic knowledge of data science and AI helps managers better explain and justify findings to the team. Data science and AI-powered business insights lead to improved knowledge relevance, optimisation of the project plan and more precise prioritisation. 
2. Project risk management
Besides the general risks, every project has its own risks and biases. Therefore, risk management in project management is a very important task. Replacing traditional project management software with AI-powered software provides better estimation of risk responses, probabilities and impacts, as well as better recommendations based on historical performance. In addition, progress can be monitored in real time.
Project managers can test, analyse and evaluate possible outcomes by merging project assumptions with historical data and running multiple scenarios. Some examples are given below.
  • Using natural language processing, AI can also incorporate real-time data, such as analysing emails and documents for potential issues.
  • Reviewing future contracts can help predict future threats based on past project performance.  
3. Human Resource optimisation
The two biggest challenges project managers face are lack of resources and inefficient use of existing resources. The use of AI-powered people management tools can even go beyond identifying the skills of employees and allocating the best resources.
Rather, real-time analytics identifies the need for the right skills for the right job and the specific training required for an employee. It predicts whether more or fewer resources are available. Most importantly, it even provides feedback on the competence and work of project staff. 
A simple and short use case

When you have to choose a location for your new business, what does your instinct tell you? What factors do you look for as a project manager? Can we assume that discounts will attract customers?
From a data science perspective, you cannot rely on your intuition. Here you need to analyse the data in a needs-oriented way.
To establish a successful business in the geographic area, sales are measured with competitors. This shows who your competitors are and what they offer. Always remember: to improve the business, you need competitors.

So, in the end, where do we stand?

We are at a point where data science is approaching project management to reach the peak of its power. In the next few years, data science will be an integral part of project management. It is time to prepare for the future direction of AI-driven project management areas.
Author: Sairaj Tamse is an enthusiastic Data Scientist and passionate blogger who loves to write technical and educational content such as data science courses, Machine Learning and Artificial Intelligence. He always believes in intelligent learning that helps people understand concepts better and writing is his way of doing that. What he likes about people is that they value learning time, which motivates him to work on it more. He prefers to write about things that help tech enthusiasts advance their careers. 

Key words: Project management, AI, Data science 

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