We attended the 1st Data Savvy Practitioner Forum in Cork, Ireland on June 23rd and 24th last. The theme of the conference was “Big Data, Better Decisions, Brighter Future” and the keynote was given by Dr Tom Davenport of Babson College in the US.
In an entertaining presentation Davenport talked about the impact data was going to have on the world of work.
He spoke about the distinction between jobs and tasks, indicating that certain tasks will be automated and jobs will evolve as a result. He also spoke about the difference between automation and augmentation, describing how humans and machines will collaborate.
The elephant in the room was the prevailing sense of fear as delegates listened as Davenport listed off the professions under threat, with attendees shifting uncomfortably as their profession was added to the list.
Ten Automatable Knowledge Work Jobs
1/ Teacher /Professor – online content, adaptive learning
2/ Lawyer – e-discovery, predictive coding
3/ Accountant – automated audits and tax
4/ Radiologist – automated cancer detection
5/ Reporter – automated story-writing
6/ Marketeer – programmatic buying, personalized emails
7/ Financial advisors – robo-advisors
8/ Architect – automated drafting and designs
9/ Financial asset manager – index funds, trading
10/ Pharmaceutical scientist – cognitive creation of new drugs
While Davenport did not assign indicative timelines to the above, he gave examples of some major developments in these key areas. Similarly, he did not argue that these roles were going to disappear, but that many of these roles had elements that could and would be automated.
So given this context how do you preserve your employability?
Davenport described 5 Key ways to ensure that you still had career prospects once automation became ubiquitous.
1/ Step In
Humans treat machines as colleagues – as they master the details of the system.
2/ Step Up
Humans take a big picture holistic view of computer driven tasks and decide whether to automate e.g. hedge fund managers
3/ Step Aside
Humans focus on areas that they do better than humans (at least for now) e.g. creative and empathetic roles
4/ Step Narrowly
Focus on roles that could be done by a machine but it is not that economical for machines to do it e.g. knowledge domains that are too narrow to warrant automating.
5/ Step Forward
Humans build the automated systems e.g. those focused on building and maintaining the systems.
Bearer of Bad News
Finally, if all of the above fails there are opportunities bringing bad news to clients and customers !
“We like to hear bad news from a human, but we are happy to get good news from a computer”.