As per a recent study by Gartner, about 80% of today’s project management tasks will be eliminated by 2030 as the traditional project management functions like planning, data collection, tracking and reporting will be taken over by machine learning algorithms. This means that most of the day to day tasks will be automated. Now the question arises, what will be your role as a project manager if most of the things will get automated? The answer to this lies in this popular quote,
“Data is just summaries of thousands of stories—tell a few of those stories to help make the data meaningful.” – Dan Heath.
What is Data Science?
Data science is an interdisciplinary field of study that uses statistical methods and algorithms to deal with vast volumes of data and find patterns to get meaningful information which can help you make better business decisions. Data science practitioners may apply machine learning algorithms to produce AI (artificial intelligence) ready data and then perform tasks that otherwise require human intelligence.
For example, a company trying to understand customer buying habits would traditionally rely on sales figures or surveys. But with data science, it can analyze a much larger dataset including customer demographics, purchase history, etc and by applying machine learning algorithms it can find hidden patterns in consumer behavior which can even reveal that customers who buy a specific product are also likely to purchase another item together or later after sometime. This insight can then be used to optimize product placement in stores or recommendations online or target advertising to boost sales.
How is Data Science Used in Project Management?
Data science and project management are almost like oil and water. You can not just completely mix them together! They have different bodies of knowledge and principles. Therefore as a project manager you will have to understand how to take benefits of their methodologies and apply them in a project to provide the best outcome. Let’s look at four step process to use data science in project management:
1) Identify Project Data Sources:
Clearly define project objectives and desired outcomes. Once you have created a problem statement and challenges in your project, check from where the data will come (the data source) that can be used for decision-making throughout the project lifecycle. Some examples of data sources are your organization’s internal historical databases, market research data, or project management software data.
2) Prepare Project Data:
Once data sources are identified, you need to ensure that the data quality matches with the project requirements. If required perform data cleaning. This may involve handling missing values, identifying and correcting inconsistencies in the data and transforming data into a format suitable for analysis. This is an important step in data analytics. This will become even more important for big projects because the data in this case can be large and complicated.
3) Analyze Project Data:
This step may require a little advanced knowledge as there are various methods of analyzing the data. You need to have a good understanding of these methods to figure out the most suitable one based on your project requirements. Some commonly used methods in project management are Predictive Modeling, Regression Techniques etc. You can also leverage data visualization by creating charts, graphs, and dashboards to understand trends and monitor project progress visually.
4) Make data-driven decisions:
Finally comes the part where you need to use your strategic abilities and use data analysis techniques to make more informed project decisions. This could involve assessment of project risks or predictive modeling to forecast potential roadblocks or delays. This may also include identifying future opportunities to invest..
Application of Data Science in Project Management:
Problem Statement:
Acme Manufacturing is a mid-sized manufacturing company specializing in automotive parts. Acme is facing the challenge of increasing competition and need to improve production efficiency to remain profitable as they are experiencing frequent delays in delivery due to unexpected equipment breakdowns leading to missed deadlines and dissatisfied customers. Let’s see how they improved their process using data science techniques.
The Solution:
Since they are facing equipment breakdowns, they can collect data from mainly 2 sources. One would be production machines for which they can use sensor data which captures machine performance metrics like temperature, vibration, and energy consumption. Second source for data would be maintenance logs to get historical data on past repairs, maintenance and parts replacements. Both these sources are internal to the company. Once the data is collected, this data can be analyzed to predict equipment failures by identifying patterns in sensor data and find any potential breakdowns to schedule maintenance before failures. Also, by analyzing historical data they can identify areas of wastage and optimize resources to improve the efficiency.
Results and Benefits:
By using data science into project management, Acme achieved improvements by reducing equipment downtime by 20% using predictive maintenance. They also increased production efficiency by 15% because of scheduling and resource allocation and material wastage is reduced by 10% through process improvements.
The case study we saw was simple but it demonstrates how data science can be a powerful tool in project management for companies. If you see this carefully, you can understand that we have used the same process that we discussed earlier which is collection, preparation and analysis of data. By using some data-driven methods, companies can easily optimize their production processes.
Conclusion:
We saw an example of how the manufacturing industry uses data analytics methods in their operations but this is applicable equally on other industries. Even PMI actively promotes the integration of data science into project management practices as it recognises its potential to improve project outcomes across different industries. They offer multiple resources related to this in their publications. While there is no dedicated data science certification specifically offered by PMI, their existing certifications like the Project Management Professional (PMP) emphasize the importance of monitoring projects based on data. You can join a Project Management Training to learn the use of data in monitoring projects or check out our resources on data driven project management. Additionally, you can check out the PMI Playbook for Project Management in Data Science and Artificial Intelligence Projects developed in collaboration with NASSCOM. You can download the playbook here.
