Highlight for the last couple on months.
On one hand, the latest unemployment figures are great news (UK unemployment falls to new 42-year low - BBC), but as the Guardian points out, there are important layers of interpretation to consider. The Joseph Rowntree Foundation report mentioned is worth reviewing if you have time too).
Working in Employee Engagement research; this is both an opportunity and challenge for clients. There is an underlying skills gap and sectors where wage pressure will increase, so engagement and retention of employees is a critical to business success with many expecting to change jobs in the next two years (HR Review). Just as important is is making sure new employees' joining experience is positive and welcoming as described in Harvard Business Review
We’re talking to clients about managing the whole employee lifecycle, tracking engagement and experience to identify risks early on and address them quickly.
As part of a business which collects data from customers and employees on a regular basis, the upcoming changes to Data Protection legislation are a key focus, particularly in a post-Brexit world (WARC). But the changes are also an opportunity as Tanya Joseph points out in Marketing Week.
My LinkedIn network has provided some reading too:
- I’d missed this from Thomas Barta in June until it was re-posted on LinkedIn: Customer experience is a marketer’s biggest leadership challenge.
- This article on "Vanity Metrics" not being a *complete* waste of time is useful too The ugly truth about vanity metrics: they matter.
- Rounding off with a great customer story Transavia & TripAdvisor: How customer feedback generates double value.
Finally, in this bumper round up, looking at how data (big or complex) is being used remains a major topic of discussion.
- I have a lot of frustration with complicated and overblown models using predictive or advanced statistical modelling, which are hard to translate into action and/or become invalidated all to quickly due to new factors. Why complex modelling is rubbish and Limitations of Predictive Analytics: Lessons for Data Scientists.
Valuable tools when used well, but not always the best choice.
- Patricio Pagani looks at the AI world and the risk to jobs, something I’ve highlighted before, but with potentially a 60% risk that my job might ‘disappear’ due to automation was worth paying attention to! Machines may do the learning, but people do the teaching.
- But oft-made predictions of AI taking over everything is not necessarily clear-cut, per Professor Tom Davenport, Are Analytics Truly Self-service? It's also often the case, in my experience, that clients are experts in their fields and interpreting and creating the business context for action is not that open to automation.