Not sure how you did with learning a foreign language at high school, if you needed to do that. When I was trying to learn French growing up in England, it was a matter of rote grammar drills, writing out the different verb tenses, and very little conversational practice.
I cannot speak French today so can never claim to be fluent.
I also spent two years in my early twenties living in Belgium and gained some Flemish language skills. However, upon returning to Canada and many years absent with speaking Flemish, I have found out that if you don’t use a language, you lose it.
That’s why being fluent with the data gleaned from your recognition programs is such a necessary skill for you as a recognition manager or program administrator. If you don’t use it you’ll lose it.
What Is Data Fluency?
It is all about understanding the differences between descriptive analytics and diagnostic analytics. It’s knowing the benefits of predictive analytics and prescriptive analytics.
Data fluency allows you to communicate with people across departments using the same data terms and understanding the processes and tools used to get the right data from your recognition programs.
When you and others understand the data produced from your recognition programs, you can magically turn the raw numbers into actionable insights. You need not be a statistician. Draw upon the skills of data analysts and analytic experts to translate the program data you have into reports and AI driven insights that provide ways to improve recognition and change key results and objectives. This is something we have been doing at Rideau over the past ten years and implementing in organizations today with our recognition programs.
The idea behind data fluency is to make more people fluent in understanding data and AI knowledge, so this information is more commonly shared across the organization, and not held solely by a privileged few data scientists.
How To Become a Data Translator
For too long we have relied upon centralized data teams that manipulate data fed to them but sometimes without the proper context to produce the right actionable results.
Researchers Nicolaus Henke, Jordan Levine, and Paul McInerney recently wrote about this in their Harvard Business Review article on You Don’t Have to be a Data Scientist to Fill This Must-Have Analytics Role.
These authors promote finding and developing data translators. This is a role you will need to adopt in the days ahead.
Henke, Levine, and McInerney suggest data translator role act on the following five steps:
Step 1: Identifying and prioritizing business use cases.
A data translator role would be to work with business unit leaders in identifying key results and objectives desired to be achieved that would lend themselves to using data analytics and provide actionable solutions. For example, we have been able to show that the employees of managers with higher quantitative and qualitative recognition measures from using their recognition programs, correlates with higher performance and people metrics.
Step 2: Collecting and preparing data.
A data translator role as a recognition program owner requires that you work with your business unit leaders to narrow down what their most important business data metrics are that will produce actionable insights. You already have access to the data from the various recognition programs that can provide a picture of how well managers recognize their staff.
Step 3: Building the analytics engine.
A data translator role suggests you create data results and easily interpretable reports by end-user business leaders and which solve a specific business problem. For example, you know you want to improve employee engagement scores which correlates with higher performance measures. Better employee recognition will improve a part of your overall engagement scores. You bring the context that a data analyst may not bring to the table with these ideas.
Step 4: Validating and deriving business implications.
A data translator role: You will work with data analysts in funneling the data from recognition programs and performance metrics into easy-to-understand reports. These should show business leaders actionable insights they can review and act on. For example, if a specific manager is not using certain programs the right way, you, or their manager, can follow up with them and coach them on how to be more effective.
Step 5: Implementing the solution and executing on insights.
A data translator role: Work with your business leaders, those in communications, and with your learning and development people, in promoting and adopting the use of the AI-driven analytics and results with whatever communications resources and education required.
For example, we have found that when analytics systems with recognition are primed and receive ongoing communication support, and when senior leaders set a clear expectation to engage and follow up, action really happens, and metrics improve. We also have developed prescribed micro-learning modules delivered to managers to give them the skills to give better and more effective recognition to employees and peers.
Don’t become intimidated by data but become a data translator and help business leaders and data scientists better understand your recognition programs.
Recognition Reflection: How well do you use your recognition program data to produce actionable business insights for your managers?
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