How to achieve the next step in data driven project delivery
It’s hard to avoid discussions in the press about the impact Artificial Intelligence (AI) and data will have on the way we live and work. The changes that some predict are so profound, it’s easy to feel helpless and a bit daunted by what the future holds.
While we are still some way off from being completely replaced by these emerging technologies, there can be little doubt that in the coming years we’ll see some fundamental changes to the way we work.
Project delivery will be no exception. In fact, given the challenges we face in delivering complex change, and the need to address grand challenges such as the climate crisis and economic stability, it’s essential we embrace the opportunities these technologies offer.
At the APM Conference in June, Andy Murray, Executive Director of the Major Projects Association, described AI and project data as “another stakeholder in the room.” It won’t make decisions for us, but it does have the potential to challenge conventional thinking and provide insights that have been previously unavailable.
To some project professionals, working to immovable deadlines with limited resources, it might seem daunting having to accommodate another stakeholder with another opinion. Added to the unrelenting pace of technological change, it may seem easier to not engage at all.
So, where to start? Many industry experts, including James Garner, Chair of the Project Dara Analytic Taskforce, have highlighted that fundamental data literacy is both the foundation stone and gateway to digital transformation.
The drive to improve awareness of digital skills has inspired the publication of APM’s new guide Developing Project Data Analytics Skills, following on from APM’s 2021 guide Getting Started in Project Data.
We do not expect, nor do we need all project professionals to become data scientists or programmers to engage with data analytics, but a better understanding of what data is telling us, how to use it and an understanding of its origins, biases and ethics will all help us benefit from the wealth of data and insights that are available to us.
The guide has kept this in mind; produced with the support of a range of project professionals and data specialists from across all sectors, it outlines eleven key data skills, some of which you will already have and others you will need to develop; in a simple, accessible framework to get you started.
How your projects can benefit from the opportunities provided by AI and project data will depend on the projects you are working on and your role within it. You may be a leader wanting to improve your team’s data driven performance, a project professional looking to be more effective in communicating what your data is telling you about your project or a specialist with a deep understanding of the data sets at your disposal, this guide aims to complement the competencies of a project professional by adding the skills you need today to get the most from the data you have.
The opportunities offered by AI and project data have the potential to be transformative for our profession, and through the delivery of increasingly beneficial projects, for society more broadly. The journey to realising that potential is potentially long and complicated, this guide aims to give you your next step.
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Sue Simmonite is the Global Project Management & Control Director for BAE Systems, a FTSE 100 Global Aerospace & Defence Company delivering some of the most complex military platforms, products and services. Sue has been with BAE Systems for over 26 years with experience across a range of complex projects in different sectors and business units leading on Project Control, Project Management, Contract Closure and Business Integration, having joined initially as a Business Modern Apprentice.
Operating within the Chief Technology Office based in the UK, Sue works across many different cultures and geographies to provide the enabling Project Management & Control capabilities to achieve operational excellence in projects, programmes and portfolios.
Sue is actively engaged in operational support to the global Portfolios, Programmes and Projects through Chairing key independent reviews such as Integrated Baseline Reviews. Sue also is the corporate sponsor for all senior PM leadership programmes working closely with thought leaders in this area in academia.
Sue is the company representative for the Association for Project Management (APM), the Project Management Institute (PMI) and the UK Major Projects Association (MPA) and achieved recognition as an APM Fellow as well as having been recognised internally as one of the inaugural BAE Systems PM Fellows.
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I loathe the term 'data driven project delivery'. It risks taking us back to the bad old days of process oriented project management where people aspects were non-existent. APM should be proud that its BoK and competencies was the first major PM organisation to include people aspects. Yes data is important, it has been all of my 35 year career. But its just part of an integration of People/Process/Technology aspects to successful delivery. Use of the term is misleading to younger, less experienced minds.
@adrian. I suggest your comment about "the bad old days of process oriented project management", are a reflection more on bad process, than processes themselves. There is no reason a good process can't be people-centric. Nor value-adding. I suspect what you see as 'process' I see as bad-process or wasteful, box-ticking, CYA bureaucracy. Or maybe the issue want the presence of a process, but the absence of leadership? 35 years ago I joined ICI and it had an excellent project management process. Well used, continuously improved based on lessons learned and innovations. But whilst it was necessary for good project management it wasnt sufficient. I reckon 2/3 of the training I received was in behaviours and leadership. The good well documented process allowed more time to develop this other necessary but not sufficient capability.
Is there any reason the skill of "Statistical modelling and forecasting" was chosen, rather than what I would see as the broader "Simulation modelling"? Given the long timescales and unique nature of projects it isnt possible to do controlled trials on different ideas and options. A representative simulation model would allow more testing prior to deciding. IMO this is a useful skill both in developing scope and execution strategy on individual projects and portfolios, and in developing methods generally. Statistical modelling implies to me models built only on data. Simulation models use data but also other inputs such as behaviours and causality.