I was asked to meet a friend who is a talent acquisition leader for “coffee” recently. These requests have been coming back fast since the lifting of restrictions, and I have to admit that I have really missed meeting folks reasonably randomly for “coffee”, in and about town.
Anyone who speaks on the circuit or gets involved in networking in the talent acquisition industry knows that meeting for coffee is really code for “I want to pick your brain, float an idea to see if I’m stupid or to get some other advice.” Whilst some advisors and consultants may feel that they should charge for every bit of advice, I'm not one of them. I love these meetings primarily because I'm flattered that someone felt I was “wise or grey haired enough to ask”, but mostly because the frequency of these questions keep me close to what industry folks are thinking about, and where time allows, I will always take the call or coffee. The time I will worry about it is when folks stop asking.
This particular “coffee” mirrored quite a few I have been having lately in a socially distanced way. It seems folks now are getting more and more data from a myriad of dashboards, or from dedicated tools like Horsefly Analytics. The thing most of these folks are looking for help with can best be divided into 3 areas:
- What data points are important, and what tells us nothing. (The banana story.)
- What trends should I be picking up and reporting to get strategic?
- How do I tell the story to get the outcome I want?
I’m going to address these in reverse order in a series of three blog posts over the next few weeks, and will leave explaining the meaning behind the banana story to the last post. You will have to stick with the series to find out. This first post about storytelling with data, I think is probably the most important one. There is no point having access to all these numbers and graphs unless you are going to do anything with them. Labor market insights are great for adjusting your own plans as to how you are going to go about hiring, but they are also going to prove critical in convincing others.
I picked up on a meme in one of the better Facebook groups I belong to recently that really hits home this point:
William Edwards Deming (October 14, 1900 – December 20, 1993) was an American engineer, statistician, professor, author, lecturer, and management consultant. Educated initially as an electrical engineer and later specializing in mathematical physics, he helped develop the sampling techniques still used by the U.S. Department of the Census and the Bureau of Labor Statistics.
I really get the point here, whilst opinions, sometimes expressed as thoughts, are very valid and have a real place in the hiring conversations we need to have with leadership and hiring managers, it is the data and the facts that turn opinions into facts. It is a bit like needing to show your workings in a math exam, to show how you got the answer. It carries some extra marks even if the answer is wrong, so I want to see the data that forms the story I am trying to tell. It brings weight to my story, particularly when I can validate the source. It's a bit like hearing a breaking news story from a friend, and wanting to know where they heard it from in order to decide how credible the story is. In data terms, this means I need to know things like the size of the data set, how long it was collected for and the margin for error. I need to be able to stand by the data, because it is this that adds credibility to my closing conclusion. Always start your data stories by outlining the “in the beginning bit”, this is demonstrated with graphs, pictures or simple tables where we are today. Always start a story with “in the beginning.”
Next you want to take the recipients of your story on a journey. The bit between the beginning and the “happily ever after” that you are going to end on. Stories as old as time all finish with a happily ever after. Some wolves might need to be slain on the way. We might need to take the recipients to a few cliffhanger moments about what could happen next if we don’t do something, before you paint a rosy picture of the happy ever after. This is the bit where you use data to demonstrate why you face a challenge, and how much that challenge will grow if you don’t change tasks. For every role I measure and demonstrate hiring complexity. I have a scoring system based on things like how easy it was to fill the role in the past, the number of candidates we will need in the pipeline, and the availability of people in the location, our proposed salary vs. the average market rate, data like that. What's happening now, is that data gives context to the story. By showing the now in a way that can be understood, the next bit is about the journey, explaining the route.
When you can apply the same aspirational data to what you want the future to look like, you can demonstrate the gap in data terms. The journey part of the story takes us from the “in the beginning” to the aspirational “happy ever after.” The steps we take in the gap is what enables improvement. This is now, these are the steps, these are the projected outcomes, and this is why we believe in these steps and outcomes. That is the story you are telling, and the data points act as the check points that make the story believable. Labor market data is that starting point.
Till next time, be good to each other,
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