Reducing the burden of repetitive tasks within a project environment.


How many tasks do you perform that are routine? Perhaps you buy a travelcard every month, tally up the hours of an employee, process timesheets or extract content from emails. Chances are, each time you carry out the task, the process remains the same. If we think about a task as a process that takes an input and produces an output, then when the input and output are static, the process is often static, too. It turns out that many business tasks fit this description, and we are in a perfect position to perform Robotic Process Automation (RPA).

RPA is a set of tools that transfer the job of automation from an in-house software developer to anyone. So how exactly can one use RPA to automate these tasks? How easy it for a non-programmer to use?

We’ll be exploring some of the use cases where such a technology can be applied and understand its limitations. We’ll also be exploring how to build on RPA and integrate it with other data science techniques to explore some of the more complicated use cases. Such methods will enable project teams to relieve themselves of the burden of the mundane and focus on the higher value added activity. Together, we can begin to transform how project teams operate.

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Dr James Smith is the Chief Technology Officer at Projecting Success. He is responsible for a wide scope, spanning full stack development through to data science and AI. He is acutely aware of the challenges of working that often exist with project management data, where the use cases can often be emergent, data can be hard to find and data quality can be variable. James has a passion for optimising and transforming how projects are delivered using the latest advanced methods, where he has been project lead for around 30 projects. He is adept at a wide range of methods including PowerBI, Python, machine learning and regularly grapples with the challenges of data engineering. James received his Doctorate in Applied Mathematics and lectured in the Maths department of the University of Kent before making the logical transition into data science.