Nic Ryan’s North-West meetup
On 25th June the North West Project Data Analytics meetup welcomed Nic Ryan, one of Linkedin’s global top 10 voices on data science. It was a 2 part evening, comprising a talk on data science within a project delivery context followed by a Q&A session.
The slides to the presentation are available here: Nic’s presentation Data Science and Project Management
Nic covered the following main themes:
How to get started in data science
Nic provided an overview of the 80/ 20 skills to get up to speed quickly in Data Science. These are the skills needed to become an effective member of a data science team. His LinkedIn post about this topic has received over 200k views.
Nic provided a very useful framework for how to tackle a data science challenge. He reiterated that it was essential to define the question or problem statement, rather than looking at a set of data and waiting for insights to emerge.
Why data science in project management makes sense
- Nic highlighted that one common theme across all the main sectors and industries in the UK is project management.
- While people may argue that projects cannot be effectively modelled Nic drew on his insurance and banking modelling experience to highlight that in fact it could be done
- Automated credit decisioning had reduced human bias in lending, the same idea could be used to impartially assess project risk.
- The modelling exercise in credit decisioning also gives insight into the common risk factors associated with borrower default. Likewise a data driven approach to modelling project failure could uncover important variables impacting project delivery
- In banking Nic has seen that even though individuals or businesses may have different characteristics often the way they experience financial stress and default can show some tell tale signs as an example when a business starts to head towards default they will become desperate for new sources of finance and therefore they will have many enquires for new credit, these are recorded on the credit bureau. In a similar way we may see these tell tale signs of project failure preceeding the failure event.
How to undertake a program of intelligent automation and AI in project management
- Nic mapped out a process for a company undergoing a transformation towards intelligent automation and AI as a series of large steps, but with individual tasks completed in 2 week sprints to gain the confidence of the business.
- Starting with robotic desktop automation of Excel reporting and building dashboards in Power BI, then
- Robotic process automation of entire business processes.
- From there once the data has been captured a machine learning and data analysis phase can begin
- Insights from the data science projects could then be A/B tested by the business.
- Finally the business is then ready for production AI systems to make decisions in real time.
The caveats and gotchas of implementation of intelligent automation and AI
Some of the caveats he mentioned included
- Having the corporate strategy either misaligned or not communicated to the data science team
- Trying to accomplish too much in one hit rather than delivering incrementally via sprints.
- Recruiting staff that are unsuitable for the stage in your journey towards intelligent automation and AI. You probably do not need 20 data scientists if you are struggling to get a figure out of Excel, a better hire would be someone who could automate the Excel work you are doing.
- He also spoke about the danger of buying AI software from a vendor who is incentivised to sell their software rather than solve your business problem
The spoils for companies who undertake an AI and intelligent automation transformation
- Better data capture and automation allows further steps in the roadmap to be achieved, particularly in lifting data quality, so automation is an enabler of AI
- Project managers can make better decisions with data at their fingertips rather than becoming slaves to the wrangling and reporting of data
- An understanding of the pain points in projects and learning from the past, Nic explained how companies are beginning to recognise the intrinsic value in data. He cautioned against locking down data within organisational silos, highlighting the exponential benefits that can be derived from pooling data to provide high quality, large volume datasets to feed into ML and AI.
- More projects would then be completed on time and on budget, which has benefits for society as a whole
Martin Paver is CEO/Founder of Projecting Success. In Dec 2017 he saw the opportunity to transform how projects could be delivered through the application of advanced data analytics and founded the London Project Data Analytics meetup, which has expanded throughout the UK and has grown to a community of ~3000 people. He delivers strategic and tactical solutions that integrate project management and leading edge data science and analytics.