How Data Science Shaped Modern Project Management

Data science is relatively new, but this interdisciplinary field has proven to be a boon for modern project management. If you’re relatively new to the application of data science in project management, read on. 

This article will cover everything you need to know about traditional project management methodologies and the relevance and benefits of data science when combined with project management.

Traditional Project Management Approaches

In the last few years, we’ve seen how different project management frameworks for different applications and industries were developed and deployed. 

Here are five of the best-known traditional project management approaches: 


The Agile Project Management(APM) methodology has gained immense popularity in recent years. If the numbers are to be believed, this methodology was used by 71% of organizations in 2017 alone. That number continues to rise.

The adaptability of APM is what gives it an edge over other project management approaches. It works by dividing the project into smaller time-boxed sprints that usually last for two weeks. This gives team members plenty of time to make changes and deal with any discrepancies that may arise.

The framework focuses on customer feedback and flexibility along with the effective collaboration of team members to ensure that the final result is according to client standards.


The Kanban framework, which was developed in the late 1940s, falls under the Agile methodology. It focuses on helping managers efficiently manage and track their projects by visualizing the entire project with the help of a Kanban board. This increases project transparency and encourages collaboration between team members.

The Kanban methodology aims for continuous improvement and development. It introduces small but meaningful changes within the existing organizational structure to make integration easier.


The Waterfall approach to project management works by breaking down the project activities into phases. It is so named because this methodology entails one phase cascading into the next one. You can only move to the next step once the completion of the previous step is terminal – a condition whereby you are not allowed to revisit a previous phase. 

There’s a reason why the rules of the waterfall methodology are so rigid. It was initially developed to help in the construction and manufacturing fields where jumping between steps is ill-advised if you want stable, livable, and long-lasting results. It’s a practice that’s been carried over to other business models, encompassing all fields.


The CRoss Industry Standard Process for Data Mining (CRISP-DM) can be used to solve the most complex business issues and is made up of six different phases. 

These phases naturally describe the data science life cycle through business understanding, data understanding, data preparation, modeling, evaluation, and deployment. It helps in the planning, organizing, and implementation of data science projects.

Unlike the Waterfall methodology, CRISP-DM works even if you shuffle through the phases. It’s quite flexible and versatile which is one of the reasons why this framework is often used.

What is Data Science?

Data science is a constantly evolving field and an in-demand career path. As the name suggests, it employs the use of scientific algorithms, methods, processes, and systems to extract knowledge and insights from structured and unstructured data. 

Meaningful insights are extracted from this data which analysts and business professionals use to derive business value. This empowers companies to draw conclusions and make informed choices based on a wealth of real-time data.

Why Data Science in Project Management Makes Sense

Project management is used to maximize productivity and deliver efficient results. It’s a process where a project manager leads a team to achieve all of its project goals based on certain constraints. The details, constraints, and processes of the project are created and documented at the beginning. The primary constraints are budget, scope, and time.

While project management provides structure, it can still be a hit or miss. Reasons why a project might fail to deliver is because the application isn’t right, there might be friction between team members, or the project goals aren’t clear. 

This is where integrating data science with project management can help. By combining these two disciplines, project managers can analyze data for unseen patterns and use a strategic approach to deliver the desired results. In short, it helps managers make smarter decisions.

How Can Data Science Be Used for Project Success?

The business environment is a force to be reckoned with. There’s always someone doing better than you, achieving higher returns, and reaching milestones within a shorter time frame. 

Of course, this is hardly pure luck. One of the ways companies are excelling is by integrating project management with data science. By adopting this novel interdisciplinary approach, they are able to reap the benefits from these two worlds.

Data science is an incredibly collaborative area that can be combined with a number of fields and industries. Any task, field, or role that uses data and problem-solving is a good match for data science. This is where project management comes in. Since many project managers are tasked with important business decisions, insights from data science can become a natural and empowering extension of their work. This has inspired many managers and companies to embrace digital transformation.

Let’s take a look at specific benefits of data science for a consumer business:

A Wealth of Data

The deployment of data science technologies and systems gives business managers and owners a wealth of data to base business decisions on. With the click of a button, they can quickly gain access to valuable information. From consumer and purchasing behavior and patterns to pain points, customer segmentation, and more, the possibilities are endless. 

Data science combined with the solid execution of the right project management framework can have tangible and positive effects on a business’s bottom line which leads us to the next benefit…

Real-Time Market Information

All this information is collected, processed, and can be evaluated in real-time. You no longer have to wait for weekly or even quarterly reports. If you’re in retail, POS systems can gather all the relevant transaction-related information as it happens which you can then distill into useful business insights whenever you want to look at the data.

Market Agility

Customers today have millions of options available at their disposal. Tastes, trends, ideas, and preferences seem to change at the drop of a hat. In a constantly evolving landscape such as this, how do you keep up? 

Use data science to your advantage.

You can now build, tweak, reposition, and redesign your brand, products or services, and operations based on real-time data. This is invaluable so that decision-makers can quickly adjust and take advantage of ever-changing market conditions before competitors. However, it’s important to note that the use of data science doesn’t eliminate risk but it can reduce it.

Final Thoughts 

A few years back, project management and its different methodologies rose in popularity. Though its applications were originally confined to engineering, civil construction, and the military, its prevalence grew and is now being used in almost every industry. 

The applications and potential of data science are similarly huge and far-reaching. Collectively, data-driven businesses are estimated to be worth $1.2 trillion in 2020, a huge leap from $333 billion in 2015.  

Indeed, combining these two powerful disciplines can have a transformative effect on your company.

Lavanya Rathnam

Lavanya Rathnam is an experienced technology, finance, and compliance writer. She combines her keen understanding of regulatory frameworks and industry best practices with exemplary writing skills to communicate complex concepts of Governance, Risk, and Compliance (GRC) in clear and accessible language. Lavanya specializes in creating informative and engaging content that educates and empowers readers to make informed decisions. She also works with different companies in the Web 3.0, blockchain, fintech, and EV industries to assess their products’ compliance with evolving regulations and standards.

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