• Tara Kenyon, PhD

Creating the Data-Rich Sentinel Species

Attracting and Retaining Key Talent while Turning Data into Money

Think about this: The canaries aren’t in coal mines to mine coal.


On the business side of your organization, your rising stars and key talent typically aren’t the ones mining data and performing data analyses. Rather, they are learning your market and your product and service offerings.


With a few extra skills and coaching, they can become a sentinel species for detecting risks to your business and provide advance warning of dangers and better, provide predictive indicators for your business potential.


In this blog post, I am going to illustrate how business executives can learn to attract and retain their rising stars without threatening established managers and executives in their succession plans.


Canary in a Coal Mine

If you haven’t heard of the proverbial “canary in a coal mine,” know that the expression is not just a proverb. The Smithsonian explains that canaries need immense quantities of oxygen to enable them to fly to heights that would make people altitude sick. Interestingly, their anatomy allows them to get a dose of oxygen when they inhale and another when they exhale. So, they get a double dose of air—and, unfortunately, of any poisons the air might contain. Miners would get an earlier warning to put their respirators on when they saw the birds getting sick.


Toxin-detecting tools have long replaced canaries in coal mines, but the sentinel species concept is one that we should take note.


Mining foreman R. Thornburg shows a small cage with a canary used for testing carbon monoxide gas in 1928. Photo by: George McCaa, U.S. Bureau of Mines. (reproduced in Smithsonian magazine, source below)

I tell you the canary story not to make you aware of the dangers of carbon monoxide poisoning in mines, but to make you aware that you have “canaries” in your business—you just may not recognize them.



Data-Rich Analytics and the Sentinel Species

I’ve written in a previous blog post, "The Magic of Analytics," about three types of analytics: descriptive, predictive, and prescriptive. In this post, I’m adding one more type: diagnostic analytics.


Diagnostic analytics is a form of advanced analytics that focuses on explaining why something has happened. Like the canary getting ill long before humans, the aim of diagnostic analytics is to understand the underlying issues and determine why something is happening.


When properly trained to do two things, your up-and-coming business leaders will perform diagnostic analytics naturally:

  • The first is to be able to articulate what the business side of the house needs from the data side of the house. It’s not really a natural thing to do but it can be learned.

  • The second is to be able to draw some conclusions from your descriptive and predictive analyses to help move you and your company forward and turn data into money.


One of the tests of leadership is the ability to recognize a problem before it becomes an emergency.

~ Arnold H. Glasow (1905-1998), American Businessman


And your sentinel employees can recognize those problems and alert you to them well before they become emergencies.


Before the Emergency Happens

Here’s an example of a problem you have that may not be an emergency yet but has the potential to turn into one.


A colleague of mine, the CEO of a US bank, is probably one of the best recruiters of key talent I know. When he’s in need of someone in the C-suite or to manage a division and doesn't have the talent in-house, he looks at the most successful banks, then finds the second individual in charge of that function in that other (often, larger) bank and recruits them heavily.


That CEO understands the difficulty of retaining good talent when there is no apparent way for that individual to move further up in their current organization. That’s good news for him, but bad news for you.


So you either have to start creating opportunities for growth and advancement for those seconds-in-command—including those second to your position—or my colleague and those like him will make them offers they can’t refuse.


The Practice

Here’s what you need to do to retain your key talent (and be attractive to those working for your competitors now):

  1. Be clear about your intent in this strategic cycle and let your key talent come up with what your organization needs from your data to carry out that intent.

  2. Have them communicate with the data team to get the proper diagnostics.

I am not asking you to send your key talent to get their master’s degrees in data analytics nor am I asking you to have them learn how to program in Python or in R. Remember that, as a sentinel species, these individuals aren’t there to mine the data—any more than canaries mine coal…they are there to diagnose where you are.


What I am asking you to do is give your talent the skills to diagnose the present, describe the past, predict the future, and prescribe actions to get the company to the future you envision. Those are skills that can be learned.


The Big Ask…

That sounds like a big ask, I know. But this isn’t complicated—it just looks like it is. Start putting a little more effort into training and coaching these up-and-comers in DIVA (that’s the acronym for Data Insights, Visualization, and Analytics). Look for new opportunities to promote them to head up new lines of business or new strategic initiatives—particularly the initiatives they’ve found themselves.


This is one of the ways to turn data into money. Keep those talented people, and they will pay for themselves and their staff several times over.




Sources:


Adair, Bergen. 2021. "Descriptive vs Predictive vs Prescriptive vs Diagnostic Analytics." SelectHub, accessed September 20, 2022. https://www.selecthub.com/business-intelligence/predictive-descriptive-prescriptive-analytics /.


Eschner, Kat. 2016. "The Story of the Real Canary in the Coal Mine." Smithsonian Magazine, accessed September 20, 2022. https://www.smithsonianmag.com/smart-news/story-real-canary-coal-mine-180961570/ .





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