Your path to becoming a better leader starts with following this simple, data-driven process.
By David M. Sluss, Ph.D.
There’s a big trend in leadership today. Top leaders are embracing big data and all things digital as the new way to connect with customers and stakeholders.
Amazon seemingly knows what we want and when, even before we do. Your local grocery store may send you a mobile coupon for 25 percent off your favorite organic granola just as you pull up into the parking lot. And the ads you see on social media are tailored perfectly to your interests.
Big data allows leaders to get to know (and influence) their customers in a more personalized way. But what about the role of big data in leading and influencing employees? Well, it’s already a part of our daily leadership journey—just not in the way that many of us might think. Big leadership data is qualitative and available 24/7. Leaders just need to learn a simple process to extract, decode, and gain insights from it.
What Is Big Leadership Data?
When we think of big data, we often think (rightly so) of huge amounts of data (volume) gathered from a huge variety of sources and at super high velocity. We also think of big data as quantitative—both in source (while not always true) and in analysis (usually true). While volume, variety, and velocity are characteristics of big leadership data, the data are uniquely qualitative in nature and are already present.
From working with many high potential professionals, managers, and executives, I’ve concluded that all leaders have a wealth of big leadership data right in front of their eyes, ears, and hearts. Yet they don’t seem to access the data when engaging as a leader.
What keeps leaders from taking advantage of big leadership data? Well, these data are emotionally laden, detailed, rich, and qualitative pieces of evidence, story, and narrative that describe the situation between leader and the other—whether it be a stakeholder, client, direct report, or peer. For the hard data leaders out there, dealing with soft, or qualitative, data such as these is downright scary.
So what do leaders normally do with these chunks of big leadership data? Not much! They might get lucky. Having previous deep and matched experience with the leadership situation, they use their gut instinct in how they respond to the situation and it works out okay, as recent research would predict.
However, “intuition is like nitroglycerine—it’s best used only in certain circumstances,” meaning when the leader has extensive experience in the specific situation. Indeed, most daily one-on-one leadership situations are uniquely specific and thus not fully apt to previous experience.
While some leaders don’t proactively analyze the big leadership data because they follow their intuition, another set of leaders might not know how to deal with or analyze scientifically the qualitative nature of the situationally embedded data. Many learn that to be data-driven, you need to use the scientific method. Indeed, much of evidence-based management systems focus on A/B testing using the deductive method.
While helpful for product development, A/B testing may not be best for experimenting on the individuals you’re trying to lead—especially when you need to provide guidance on a particular situation. Different from the deductive method, the inductive method uses qualitative data.
Marketing researchers use this qualitatively based inductive, or bottoms up, method where they observe a customer behavior, continue to collect more and more data via interviews, and eventually create a hypothesis (marketing strategy) to either promote or discourage the customer behavior. While helpful for learning about customers over the long term, leaders need to take action today.
Use Big Data the Right Way
In my teaching and coaching, I’ve found a third scientific method that lands the leader somewhere in the middle between these two extremes and allows them to adduce (rather than deduce or induce) powerful leadership actions from the data in the situation based on an appropriate framework.
Abduction is simply the art of finding the simplest or most elegant explanation from a set of observations—one that’s already present in our daily leadership experiences. Abduction will allow us to quickly and elegantly both diagnose the problem or opportunity and prescribe powerful and appropriate leadership actions.
How can you as the leader adduce—that is, extract, decode, and garner insight—from these big data and engage in a leadership approach that will be effective? There are three basic steps:
1. The leader chooses an analytical framework (that is geared to solving the particular leadership challenge or taking advantage of the leadership opportunity).
2. The leader thoughtfully extracts relevant observations from the big leadership data based on the analytical framework.
3. The leader diagnoses and prescribes leadership actions based on how the observations match up or align with the framework.
Let’s look at an example to explain this process. You’re the senior vice president of IT for a large financial transaction services organization, and you’re rolling out agile processes within one of your IT product lines, as a pilot test for the rest of the IT organization. Saul, your direct report and the leader of this product line, is passive-aggressively responding during all your agile transformation meetings.
You’ve worked with Saul for the past five years. Saul is usually direct, achievement-driven, and responsive to anything that will speed up the product development life cycle, so his latest behavior is a departure and quite perplexing.
What should you do? Without his support, you won’t have much success implementing agile in his product line, or any other for that matter. You want to ask him what’s going on, but wonder if you’ll get a straight answer. You also wonder what questions to ask.
Let’s apply our simple abductive process to this big leadership data situation.
Tip #1: Choose a situationally appropriate framework. Given that Saul is resisting an organizational change, you decide to use a framework dedicated to analyzing resistance to change called ADKAR. You remember learning this framework in a change management workshop you attended a year or two ago. ADKAR stands for awareness, desire, knowledge, ability, and reinforcement. The framework allows you to diagnose Saul’s readiness for the agile transformation as a change effort.
Tip #2: Thoughtfully extract relevant observations (as adduced from the framework) from the big leadership data. You use the key questions in ADKAR to thoughtfully extract evidence from the situation—what Saul has said and done with regard to the agile transformation.
You realize that, while he’s aware (A) of the change and has previously stated a desire (D) to quicken product development cycles (via agile processes), he also stated several times—which you tended to ignore—how he doesn’t understand how to run “scrum” meetings (daily standups) or the other duties of a scrum master (lack of knowledge, K, and ability, A). He, as a potential scrum master or supporter, just wants to quicken the product development cycle, but says, “What’s the need for a scrum master anyway?” (R).
Tip #3: Diagnose and prescribe leadership actions based on the big leadership data observations.You adduce (i.e. diagnose) that Saul may be resisting the change because he lacks the knowledge and ability (and thus confidence) to lead as a scrum master in the agile transformation. You then decide (i.e. prescribe) to offer an offsite scrum master training and certification solution as a personal pilot project for him.
You present this opportunity to Saul as a way for him to assess the process more closely and give you a report concerning “suggestions for implementing agile” in the organization. You know this will slow down the agile transformation, but it’s a much better alternative than assigning him to special projects or just not moving forward successfully with agile in your organization. You’ve now appropriately gotten Saul moving in a positive direction toward implementing agile.
You aren’t done, but you’re on your way—all thanks to adducing leadership actions from the big leadership data you had right in front of you.
Here are some additional tips or points to consider as you adduce solutions from your more real-life big leadership data.
Tip #4: Ask for a framework, not a solution. The adducing process relies on your ability to apply an appropriate framework. In the last scenario, you had a readily available framework to use for change readiness. There are many managerial and leadership frameworks available; see one example of an online repository. As you progress through your education, training, and career, it’s wise to create a file with all the useful frameworks for leadership and managerial decision making.
In short, you can create your own big leadership data toolkit repository. Which frameworks do you already know? Which templates and frameworks are available through your organization? Who in your network could recommend an appropriate framework?
But here’s the key: Don’t ask them to solve your problem; ask them (after describing the problem) if they know of a helpful framework or theory that might help yousolve the challenge or exploit the opportunity.
Tip #5: Stick to the framework and triangulate the data to reduce emotional bias. In the scenario, you might have been getting a little impatient and upset with Saul for passive-aggressively resisting the transition to agile processes.
However, sticking to the framework allows you to focus less on your emotional reaction to the data and more on the data and the possible insights from the data. In the end, Saul wasn’t being a jerk, but just acting somewhat normally due to having a lack of confidence in his ability to lead the scrum master process.
You supposedly own all the data from your own observations. While this may be effective, you could also triangulate the data. You could ask Saul for a meeting and use the questions conversationally to inform your own observations—having an open and diagnostic approach.
We don’t often have visibility into many of the answers, or the data is distributed across multiple individuals and their experiences. So we just need to go and ask. How do you know you’ve extracted enough data? You can ask yourself if you’re saturated in your diagnosis.
Is the extracted data adding only incremental perspectives to your understanding of the situation? Is the extracted data repetitive or confirmatory of multiple pieces of evidence or data? If so, you know you can stop extracting data and start finalizing your diagnosis from the big leadership data.
What About Creativity?
As a final point, some may worry that the framework will restrict their ability to creatively prescribe solutions that really move things and their people forward. The power and creativity comes in your prescription from the diagnosis, not the diagnosis itself.
From your personalized big data knowledge of Saul, you decided to offer the training as an evaluation method for Saul, and thus respected his autonomy. The diagnosis based on the framework doesn’t determine, but inspires the prescription. So let’s go out there and be inspired from all the rich, nuanced, and big leadership data that surrounds us all.
David M. Sluss, Ph.D., is an associate professor of organizational behavior at Georgia Institute of Technology’s Scheller College of Business. He is active educating high potential executives on leadership, high performance teams, and leading transformation efforts (with a focus on digital transformations).