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Way back before the Pandemic, I was invited to help judge one of the early Project Data Analytics Hackathons. As someone who had never been excited by the data of data, let alone data mining, I was flattered to be asked but had few expectations. How misguided was that! The Hackathon was a revelation.
On the day, I met a group of enthusiasts, some with a formal data analytics role, others simply with data skills and curiosity, of both genders, all ages and backgrounds. This collection of individuals (whose default working style was collaborative) supported, learned from and worked together with the others in the room to tackle the challenges that each of the teams had been set, using project data to which a number of the sponsoring organizations had given them access.
There were several groups working on each challenge, which meant that we ended up with several alternative solutions to the problem.
Two things I vividly remember:
- The excitement and the productivity apparent in the room – there was definitely what Ed Hoffman, ex-Chief Knowledge Office at NASA would call, the ‘sound of success’; conversations running between individuals and across the groups;
- Every team produced a working result of some sort.
I can’t remember which was the winning team but several of the teams’ solutions are stuck in my memory, for example, the team who hacked facial recognition software to create a form of AI that recognised and alerted users to compliance or non-compliance with Personal Protective Equipment requirements on a given site. And not just who was wearing hard hats and boots but it could identify people wearing safety spectacles, ear defenders and other specific equipment. Or the team that created a software application that read requests for tender and identified missing, out of date or otherwise erroneous standards that were cited in the documents and recommended solutions, such as a more recent standard.
The first example had the potential to prevent serious injury with all the accompanying personal trauma and the delays and costs that this creates. The second one had the potential to save weeks, if not months, of time during the tendering process which would otherwise be spent fruitlessly, whenever subcontractors raised queries on the requirements and these rumbled up and back down the supply chain.
The Major Projects Project Data Hackathon
All of which is why I am excited and delighted in equal measure that the Association has teamed by with the Project Data Analytics network to host Project Hack 11.
This is a tremendous opportunity for data analysts and the professionally curious to experience the intellectual and social power of the Hack, first hand; for member organizations to volunteer data and see what happens when these extraordinary minds are able to mash it, scrape is and mine it; and for others who, like me, only had a passing understanding of the value and significance of project data to observe the sessions and experience the same kind of revelation I had.
To help you get the most out of the event, we have created three options:
8th September: What is Project Hack – a briefing session and Q&A to help you set your expectations and introduce you to the magical process of the hackathon
7th-8th October: Hackathon (for delegates) – a chance for employees from across the membership to work in teams with your peers to hack the data you have been provided and tackle the challenges of the hackathon
7th-8th October: Hackathon (for observers) – the opportunity for managers and leaders to see data analysis in real time; to experience the excitement of the teams working together so productively and the thrill of watching working solutions begin to form and then emerge from the process.
Experience is one of the greatest teachers. You won’t have the same experience of the Hackathon as I did, that’s part of the beauty but whether you choose to take part as a delegate or to observe events as they happen, I guarantee this event will change your understanding and appreciation of data analytics for good.