DataScience AF Lead
Adam Fletcher
Adam Fletcher Data Scientist

Tech Focus Thu 6th October, 2022

Resourcing project management and reporting – how data science can help

When our client receives project requests from their customers, a lot of time and cost is spent on resourcing project management – determining which teams should conduct the work –  before we even start delivering the work. Here’s how we used data science and dashboarding to speed up this processing time and provide up-to-date metrics on the project delivery process.

The problem of resourcing project management

Our client runs an ever-growing department of over 800 people, delivering numerous projects in parallel, and the number of projects grows year after year. However, the client’s method of distributing work to the relevant teams (what they call their impacting process) hasn’t scaled with the success the client is having.

Impacting is a resource-intensive process requiring each team to read multiple documents – sometimes up to 25k words – to identify whether they are required for the project, and often they’re not. This results in a slow, manual process that requires multiple redundant points of contact.

After a project has been through the impacting process and is being delivered, there is no automated reporting. Typically, reporting is triggered by a status request from a senior leader, at which point the data is manually collected, creating slow and infrequent feedback loops.

This is an intensive process which puts tremendous strain on an already busy department, especially as they currently have to process over 100 project requests a week.

Our aim is to reduce the number of people involved in a project impact to only the most relevant individuals, and to streamline the amount of reading required to understand the project.

Leveraging data science for improved project resourcing and reporting

As the client had no clear insight on in-progress projects, we determined that the most useful first step was to provide reporting on these projects using data from their Jira ticketing system. This allows senior leaders to access project delivery information quickly and interactively, enabling them to identify issues and bottlenecks before they become problems. 

We then focused on reducing the resource overhead in the impacting process. Project  impacting is designed to determine which teams are required to work on a project. In this case, it involved a lot of people reading large documents which were potentially irrelevant to their team’s specialism. 

So we sought to improve the impacting process in two ways:

  • Can we reduce the amount of time needed to understand the project? 
  • Can we highlight the project to only the relevant teams?

The scope of data science

Reducing time to understanding

With a typical design document being approximately 25,000 words, it takes a person roughly 3-4 hrs to read. Reducing the amount of text needed to understand the document would result in significant time savings per person.

 This was done in a variety of ways; firstly we used an AI model to summarise the text while retaining important information, allowing users to control the degree of summarisation. This summarisation method is also being used to create executive summaries for the senior leaders who constantly switch context between pieces of work, and need to very quickly understand different projects.

Secondly, we extracted keywords from the text so the user can rapidly determine important terms within the document.

These tools have proved very useful in enabling individuals to quickly establish whether they  need to read the document in full, and can slim down reading time from a few hours to a few minutes.

Identifying Relevant People

Typically 12+ people can end up reading these documents, meaning that each project takes 6+ days of work just to impact – and many of these people are not even relevant to the project. Therefore, reducing the number of people reading these documents to only the most relevant compounds the savings given through document summarisation. 

To do this we developed a machine learning classifier to determine which teams were relevant to a project, reducing the people required for impacting. Additionally, we identified similar existing projects and the teams involved in those, to further assist in establishing the right teams for the work.

A future enhancement we wish to add is building a recommender system that automatically alerts people if new projects arrive that are similar to previous projects they have delivered, further reducing the operational overhead.

The business value of improving project resourcing and reporting through data science

The client is now able to direct incoming projects to the relevant teams much faster, reducing the delay between a project’s request and work starting, and improving new customer satisfaction. The people involved in impacting now have time freed up to lead the deliveries of in-progress projects, which also benefits existing customers and team efficiency.