7 Characteristics of Teams That Consistently Make Better Decisions
- Dr. Jeff Doolittle
- 5 hours ago
- 8 min read
Two executive teams can have access to the same information, comparable analytical capability, and equally experienced leaders, and still reach decisions of very different quality.
That gap has economic consequences. Research on group decision-making shows that when the best decision depends on combining insights, groups are far less likely to identify the optimal answer.
The difference is not just data.
It is often the discipline around how the team challenges assumptions, distinguishes symptoms from root causes, responds to unfavorable findings, and converts conclusions into results that matter.
This makes decision quality observable.
Teams that consistently make better decisions demonstrate recognizable patterns in how they use evidence, not because data replaces judgment, but because it refines judgment. For executive teams, this distinction matters.
Better decisions are not simply the product of smarter individuals or more sophisticated dashboards. They emerge from habits that reduce bias, expose flawed assumptions, clarify what the evidence actually supports, and connect conclusions to action.
The Value of Data-Driven Decisions
Data-driven decision-making has become somewhat of a buzzword as organizations invest in analytics, artificial intelligence, and other technologies designed to improve decision quality. A useful working definition of data-driven decision-making is:
Using facts extracted from data and metrics to guide business decisions that support business goals rather than relying on experience, intuition, and stories alone.
This does not mean experience and intuition are unimportant. Executive judgment remains essential, especially when leaders operate with uncertainty, incomplete information, or rapidly changing conditions.
The problem occurs when intuition becomes the default, evidence is selectively considered, or data is introduced primarily to support a conclusion already reached.
Data can speed up and improve decision-making. It can help leaders understand what is and is not working. It can expose patterns that are difficult to see through experience alone. Predictive analytics can improve strategic foresight and preparedness as market conditions change.
Data-driven decisions can be descriptive, predictive, and prescriptive.
Descriptive analytics helps leaders understand what happened.
Predictive analytics considers what may happen.
Prescriptive analytics addresses the more consequential question: What should we do?
But data alone cannot answer that question.
Leaders still have to interpret the evidence, evaluate competing alternatives, exercise judgment, decide, and act.
The quality of that process matters. The following seven characteristics distinguish teams that use evidence not simply to become more informed, but to make better decisions.
1: They Seek Truth Rather Than Confirmation
Deming is often attributed with saying, “In God we trust. All others must bring data.” The underlying principle is important. Data-driven teams use evidence to seek truth rather than confirm what they already believe.
This sounds straightforward. In practice, it can be difficult.
Leaders naturally develop beliefs about markets, customers, employees, competitors, and organizational performance. Experience creates valuable pattern recognition, but it can also create assumptions that become increasingly difficult to question. The real test occurs when credible evidence contradicts a strongly held position.
What happens when the data challenges a preferred strategy?
What happens when an investment fails to produce the expected result?
What happens when evidence contradicts the prevailing explanation for a performance problem?
Teams that consistently make better decisions are willing to examine evidence that challenges their assumptions. The objective is not to eliminate judgment.
It is to prevent judgment from becoming immune to evidence.
2: They Look for Patterns and Root Causes
Data-driven teams aggregate information to identify patterns, predictions, and potential root causes. They treat problems as possible symptoms of deeper issues rather than isolated events that simply need resolving.
Organizations can become very efficient at solving recurring problems without ever addressing the systems producing them.
Turnover may look like a recruiting problem when the underlying issue is leadership.
Declining performance may look like an employee problem when decision rights are unclear.
Customer complaints may appear to be a service issue when the root cause exists upstream in process design.
Looking for patterns changes the leadership question.
From: How do we fix this problem?
To: What is causing this problem to continue occurring?
Identifying root causes protects the organization from repeatedly treating symptoms while systemic problems remain unchanged.
3: They Examine Variation Rather Than Relying on Averages
Averages can distort organizational reality because they conceal variation.
An enterprise-wide average may look acceptable while significant problems exist within particular teams, departments, geographies, customer segments, or business units.
Average employee engagement can obscure a struggling division.
Average customer satisfaction can conceal deterioration among strategically important customers.
Average operating performance can hide substantial differences between locations.
Teams that consistently make better decisions examine information at a sufficiently granular level to understand what is actually happening. The goal isn't greater analytical complexity for its own sake. Granularity helps leaders distinguish isolated events from patterns and determine where attention, accountability, or resources are needed.
Sometimes the most consequential insight is hidden inside an acceptable average.
4: They Use Data to Interrogate Assumptions and Stories
Stories and anecdotal evidence provide powerful personal connections. They are also dangerous when treated as representative evidence.
One customer complaint can redirect executive attention.
One successful employee can reinforce assumptions about an entire talent strategy.
One memorable failure can shape investment decisions long after the underlying conditions have changed.
Teams that consistently make better decisions do not eliminate stories. They test them.
They ask whether the story represents a broader pattern, an isolated exception, or something the organization does not yet understand. Data provides a way to examine the narratives that naturally develop inside organizations.
The question is not whether the story is compelling. The question is whether the evidence supports the conclusion being drawn from it.
5: They Value Negative Findings
Finding out that something does not work can be as valuable as finding evidence that supports an idea. This is one of the more difficult characteristics of a genuinely data-driven team.
A major initiative may not create the anticipated value.
A long-held assumption may prove incorrect.
A strategy may underperform.
An investment may fail.
Unfavorable findings can create defensiveness, particularly when significant resources, executive credibility, or organizational identity are attached to the decision. But negative findings are still information.
The strategic value of evidence is not that it continually proves leaders right.
Its value is that it enables an organization to discover when an assumption may be wrong before the consequences become more expensive. Teams that consistently make better decisions do not treat unfavorable evidence as failure to be hidden.
They treat it as intelligence to be understood.
6: They Convert Evidence Into Decisions and Action
Analysis without action creates little organizational value. Teams can have excellent analysts, sophisticated dashboards, and extensive reporting while repeatedly failing to act on what the evidence reveals.
Data's purpose is not simply to improve understanding. It is to inform a decision.
What are we going to do differently because of what we now know?
What will we stop?
What will we continue?
Where will resources move?
Who owns the action?
When will the decision be evaluated?
These questions connect analysis to execution.
Teams that consistently make better decisions don't let evidence stay trapped in presentations, dashboards, or meetings. They translate what they learn into decisions, ownership, and action.
7: They Know When the Evidence Is Sufficient to Decide
Being data-driven does not mean waiting until uncertainty disappears. Leaders rarely have complete information. Markets move. Competitors respond. Customer expectations change. Technologies develop. Unexpected events occur.
At some point, leaders have to decide.
The discipline lies in understanding what evidence the decision requires, how reliable that evidence is, what remains unknown, and what risks accompany acting—or waiting.
Some decisions warrant extensive analysis. Others require speed and informed judgment. The danger exists at both extremes. Leaders can make consequential decisions primarily through intuition when relevant evidence is readily available.
They can also create analysis paralysis by continuing to gather information long after additional data is unlikely to materially improve the decision.
Teams that consistently make better decisions understand that the objective is not certainty. It is making the best defensible decision available under the circumstances and remaining willing to adjust as new evidence emerges.
Building a Culture That Supports Better Decisions
These seven characteristics do not develop simply by investing in analytics.
Culture influences how employees interpret and use evidence.
Leaders reinforce that culture through what they consistently pay attention to, how they respond when performance deteriorates, where they allocate resources, what behaviors they reward, and who receives greater organizational responsibility.
Several leadership actions are particularly consequential:
Pay attention to metrics that matter and review them consistently.
Respond to organizational problems by examining evidence rather than relying on anecdote alone.
Allocate appropriate resources to analytical capability.
Develop employees' ability to interpret and apply evidence.
Recognize disciplined, evidence-based decision-making.
Support constructive challenge when data contradicts established assumptions.
Connect decisions to clear ownership and follow-through.
Culture is also reinforced through organization design, policies and procedures, rituals, performance systems, traditions, stories, and leadership behavior.
Employees pay attention to these signals.
If leaders say data matters but routinely dismiss inconvenient findings, employees notice.
If teams are encouraged to challenge assumptions but disagreement creates personal risk, employees notice.
If analytics are reviewed but never influence decisions or resource allocation, employees notice.
Over time, those repeated signals teach people how decisions are actually expected to be made.
These patterns also reveal why better decision-making cannot be separated from leadership habits. The way leaders respond to disagreement, examine assumptions, seek evidence, allocate attention, and follow through on decisions becomes part of the operating environment others experience.
In Life-Changing Leadership Habits, I examine the recurring leadership practices that shape people, performance, and organizational outcomes. In the context of data-driven decision-making, those habits matter because evidence does not interpret itself or act on its own. Leaders determine whether evidence creates inquiry or defensiveness, whether disagreement produces learning or compliance, and whether decisions ultimately translate into accountable action.
The connection is important: decision quality is not only analytical. It is behavioral. What leaders repeatedly do becomes part of how the organization repeatedly decides.
Better Decisions Require Decision Discipline
The advantage of a data-driven culture is not that every decision becomes correct.
No decision process can eliminate uncertainty, incomplete information, or the need for executive judgment.
The advantage is that the organization becomes better at discovering when its assumptions are wrong, distinguishing evidence from narrative, identifying patterns beneath individual events, learning from unfavorable outcomes, and adjusting before errors become more expensive.
That is a more demanding standard than simply being data-driven.
Organizations can possess extraordinary amounts of data without developing this discipline.
They can also employ highly capable people and still allow confirmation bias, anecdote, hierarchy, or analysis paralysis to weaken decisions. Better decision-making requires something more. It requires teams that can use evidence to discipline judgment without surrendering judgment to the evidence.
For executives, the central question is therefore not simply: How much data does our team use? A more consequential question is: Does the way our team uses evidence consistently improve the quality of judgment, action, and organizational learning?
Because the objective is not to create an organization that uses more data.
It is to create an organization that makes better decisions.
References
Bartlett, R. (2013). A Practitioner’s Guide to Data Analytics: Using Data Analysis to Improve Your Organization’s Decision-Making and Strategy. McGraw-Hill.
Davenport, T., Harris, J., & Morison, R. (2010). Analytics at Work: Smarter Decisions, Better Results. Harvard Business Press.
De Smet, A., Lackey, G., & Weiss, L. M. (2017, June 21). Untangling your organization’s decision making. McKinsey Quarterly.
Deloitte. (2019). Deloitte Survey: Analytics and Data-Driven Culture Help Companies Outperform Business Goals.
Greenstein, B., & Rao, A. (2022). PwC 2022 AI Business Survey. PwC.
Lu, L., Yuan, Y. C., & McLeod, P. L. (2012). Twenty-five years of hidden profiles in group decision making: A meta-analysis. Personality and Social Psychology Review, 16(1), 54–75.
Upadhyay, P., & Kumar, A. (2020). The intermediating role of organizational culture and internal analytical knowledge between the capability of big data analytics and a firm’s performance. International Journal of Information Management, 52, 102100
