In this article you’re going to learn exactly how to retain your top analytics talent, start to remove business obstacles and find immediate value from this investment.
I will also share 5 simple steps that you can follow to create the resources that your data talent needs, when they need them and to automate this process at scale.
Let’s dive straight in.
After the famous article Data Scientists: the sexiest job of the 21st century we all saw a hiring frenzy for this new super hot analytics talent. It made total sense, we have lots of data, we now need the skills and capabilities to apply analytics to it.
But we now have a problem.
They’re all leaving.
And the reason?
“It never got used”
This is what we hear from such talent all the time and is the number one reason why data scientists feel isolated and under valued. They spend weeks working through complex data sets, attempting to surface meaningful insights for the business to no avail.
Then they simply vote with their feet.
Identify what it is they are trying to get done and then provide them with resources that they can use within their workflow.
Looking at a typical analytics project in your business, ask them to plot against the Y axis here. What is making them feel 😡 and what is making them feel 😄?
What’s fantastic (and always surprising) about this exercise is that data talent always says;
No one ever asked me about how I feel trying to get my job done every day!
At each turn of the curve, ask them what is happening there and causing this reaction? Get them to write it down!
We have run this exercise 000s of times over the past decade with business and analytics talent and across multiple industries and sectors, and very similar issues arise. Below is an example snap shot from 2017–2019:
Design resources that help those teams at the point of need. What’s so interesting about this step, is that once you are clear on what they are trying to get done then the simplest tools can help.
As per the example above “It never got used” you can provide those teams with the following;
- Impact Matrix — predict impact and resistance from the business.
- Understanding the business problem check list — get a deeper understanding of what decisions need to be made and when to avoid wasted effort.
- Common language booklet — align on a common taxonomy to avoid miscommunication.
- Running intelligence briefings top 5 tips — make a big impact in front of that business leader and not hide behind the dashboard.
- Feedback loop guide — design effective touch points so you don’t go down a blind alley.
Using machine learning technology, start to push resources to analytics teams when we know they will need them (from X axis above). For example;
- START of project: Trust Exercise. Language map. Business problem checklist. Key assumptions mapping exercise.
- 2 weeks into project: Impact matrix. Outside-in thinking tool. What If?! analysis technique.
- 4 weeks into project: Explain my results checklist.
- End of project: After action review exercise. Feedback template.
So, when that Data Scientist is sat at his/her desk feeling 😡 — a resource pops into their Learning Management System feed and suddenly… 😃!
Make these resources available to your entire analytics community via a digital resource.
All of these resources, when designed specifically for your business, will achieve three things;
- Drive engagement and retention of your top analytics talent.
- Increase the utilisation of your analytics investments through business results.
- Remove frustration from the business as they are part of this new culture and way of working.
Finally, with open access and unlimited usage to this resource, you will start to find value from your analytics talent.
They will be happier.
And, they will stay.
About the author
Graham began his career as a Royal Marines Commando Officer, specializing in Intelligence. On operations in Afghanistan, the Southern Arabian Gulf and Sierra Leone he led a team that connected deep insights generated through sophisticated analytics to impact organizational decision making. He is the award winning author of Seeing Around Corners.
He is now the CEO of Pelatum, helping Helping Executives Unlock Value from their Analytics Investments through People.