In the meetup we will have two talks on Using project data to understand project performance and Agile effort estimation.
Mudano are looking to build the autonomous project by combining the right data, technology and delivery expertise to deliver business value for our clients.
In the data-driven future of project management, project managers will be augmented by artificial intelligence and advanced analytics that can highlight project risks, determine the optimal allocation of resources and automate project management tasks.
In this talk, they will give an overview of the journey they've been on in achieving the above including how they applied natural language processing (NLP) to classify project status reports from a retail bank to highlight projects that are failing, those at-risk and those that are on track.
Dave McCallum is a senior delivery manager with extensive background in delivering large scale change, particularly in data transformation for financial services clients. He's done a lot of work within Mudano on definining delivery method aligned to Mudano principles of removing waste, applying science, pursing value, anticipating issues and empowering teams
Sarah Schofield is a delivery manager with experience in software delivery within retail, telecomms and financial services. At Mudano, Sarah's focus is how we can use data, combined with design processes underpinned by scientific principles to drive and validate behavioural change, ensuring that we deliver solutions that 'do the right thing'
Within this talk Derek Jones will provide insights into his analysis of 10+ years of commercial development using Agile (10,100 unique task estimates made by 22 developers, under 20 project codes). paper+link to data: http://arxiv.org/abs/1901.01621
Factors found to influence task implementation effort estimation accuracy included the person making the estimate, the project involved, and the propensity to use round numbers.
Derek used to write compilers that translated what people wrote, and then moved on to analysing code to try to work out what they intended to write. These days he is analysing all the publicly available software engineering data.