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7 Data-Driven Characteristics of Teams that make Better Decisions

Think back to the last big decision your team faced. What were the options considered? How was the choice made about what to do? A recent extensive survey conducted by PWC revealed that data-driven companies are three times more likely to make better decisions than businesses that are not. Decision-making is a significant part of leadership; many depend on the decisions. Without good choices, companies and leaders can't thrive. But studies reveal that more than half of us rely on intuition to make significant personal and professional decisions. The problem isn't with using intuition. The problem is when your intuition comes at the cost of data and is the only or default way you decide. While most companies are investing significantly in building analytics capability, the benefits can't be fully realized until the company culture supports data-driven decision-making. Here are the seven characteristics of a data-driven culture and practical steps any leader can take to architect culture.

The value of data-driven decisions

Advances in technology create a significant advantage for organizations that can leverage data to make better decisions and take the right actions. Data-driven decision-making (DDDM) has become somewhat of a buzzword as many leaders and organizations aim to be data-driven.

A good working definition of what it means to embrace 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.

A study involving more than 1000 executive leaders demonstrated that 80% of organizations with a mature approach to data analytics exceeded their goals, and 48% significantly exceeded their goals.

Making data-driven decisions is not the only way leaders can succeed. However, there are many advantages, such as:

  • Enhanced decision speed and sophistication. In a volatile, uncertain, complex, and ambiguous digital workplace, leaders need to find insights and speed matters. Businesses need to make good decisions quickly.

  • A better understanding of what is and is not working. Testing and data collection enable leaders to fail fast and learn from making decisions.

  • Reduced costs and increased revenue. Using data enables organizations to optimize operations. Predictive analytics goes one step further, allowing organizations to transform during market change quickly.

  • Improving strategic foresight. The goal is not to predict the future but enable better decision-making and preparedness so that leaders can grow revenue amid uncertainty.

Data-driven decisions can be descriptive, predictive, and prescriptive. While understanding why something happened and what will happen is helpful. Understanding what should be done provides the most significant organizational value.

The following video gives a real-world example from Google of how businesses can make better data-driven people decisions.

Seven Data-Driven Culture Characteristics

A recent 2021 Fortune 1000 executive leaders survey revealed that 99% are investing in data initiatives to transform their companies. These investments in technology are producing a deluge of available data within companies. But are these investments leading to better decisions?

According to this same report, 96% of executives report that they are achieving measurable business outcomes. However, these leaders identify culture as the most significant deterrent to becoming a data-driven organization.

"Culture is more powerful than anything else in the organization," and often why good management ideas fail." Upadhyay & Kumar

To maximize data, analytics, and AI value, organizations need a data-driven culture orientation. However, this represents a sizable shift for many cultures that often rely on stories and experience to make decisions.

Here are seven attributes and behaviors of employees working you would expect in a data-driven culture:⁠

Characteristic #1: Desiring to find the truth

W. Edwards Deming is attributed as saying, "in God we trust. All others must bring data." This saying is something you would likely hear in a data-driven culture about using data to find the truth without bias. When seeking truth, employees are often surprised, and it sometimes leads to politically incorrect actions that result in innovation.

Characteristic #2: Looking for patterns and root causes

Data-driven cultures aggregate data to identify patterns that can lead to predictions and root causes. In a data-driven culture, problems are considered symptoms of deeper issues rather than being 'swept under the rug.' Identifying root causes protects the business from recurring systemic errors.

Characteristic #3: Developing detail-oriented analysis

Averages are considered flawed and a distortion of truth in data-driven cultures because averages ignore inevitable variations. Granular data is used for decision-making. A detail-oriented analysis allows stakeholders to determine causation more effectively and present solid arguments for decisions.

Characteristic #4: Using data to analyze questions

Stories and anecdotal evidence provide a personal connection, but alone, they are not often representative. Data-driven cultures use data to tell stories and make decisions. Data stories are the annotations of crucial data insights.

Characteristic #5: Appreciating both positive and negative findings in the data

Finding out something doesn't work is just as valuable as finding data that supports an idea. Data-driven cultures adopt an experimentation mindset and seek to learn from the data about predictions.

"The unexamined decision isn't worth making." Davenport

Characteristic #6: Making decisions and following through on actions

Power and politics are not driving forces in a data-driven decision-making culture. Emphasis is on the value of results from decisions rather than a confirmation of senior leadership ideas. Authority is vested in the data quality rather than the positional power of the person with the data.

Characteristic #7: Being realistic about when and where to use data analytics

Data-driven cultures are practical about the need for velocity, veracity, volume, and variety of data before making decisions. Decisions are based on experience and available data and avoid analysis paralysis.

"Culture eats strategy for breakfast.” Drucker

If you recognize your culture doesn't demonstrate the seven characteristics of a data-driven culture, you will want to work on architecting the desired culture ahead of or in parallel with your data analytics investments.

How to architect a data-driven culture orientation

Organizational culture is the one thing that influences every aspect of a business. It directly impacts organizational success, employees, customers, and communities. An organization's underlying cultural values affect employees' behaviors and decisions.

Executive-level sponsorship is vital for investments in data analytics. However, leaders at all levels play a vital role in shaping organizational culture in business. Organizations are likely to resist the need for culture change. Although architecting corporate culture is challenging, changes often don't require considerable investments or physically co-located employees.

Leaders can leverage the following primary and secondary actions and tools for leaders to embed the desired culture:

Primary Actions and Tools

  • Pay attention to metrics that matter and provide regular updates

  • Respond to organizational crises with data

  • Allocate resources to support data-driven decision making

  • Provide data analytics training and development

  • Provide rewards and recognition for data-driven decision making

  • Make selection, promotion, and termination decisions in support of data-driven decisions

  • Manage change created by shifting to data-driven decision making

Secondary Actions and Tools

  • Organization design

  • Policies and procedures

  • Rituals and events

  • Workspaces

  • Traditions and stories

  • Vision and mission statements

Organizational culture typically varies to some extent across teams, departments, and geographies. When trying to architect a data-driven culture, it is best to understand your culture at a granular level. To do this, you will need a data-driven actionable measure of your current and preferred company culture.

What's the real data-driven decision-making challenge?


Bartlett, R. (2013). A practitioner's guide to data analytics: Using data analysis to improve your organization's decision-making and strategy. McGraw-Hill. New York.

Davenport, T., Harris, J., & Morison, R. (2010). Analytics at work: Smarter decisions, better results. Harvard Business Press. MA.

Deloitte. (2019). Deloitte survey: Analytics and data-driven culture help companies outperform business goals in the age of with’.

Greenstein, B., & Rao, A., (2022). PwC 2022 AI Business Survey. PWC.

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.


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About Dr. Jeff Doolittle

He is the founder of Organizational Talent Consulting in Grand Rapids, MI, and Program Director of online graduate and continuing business education at Olivet Nazarene University in Bourbonnais, IL. Executive leaders who work with Jeff describe him as thoughtful, decisive, intelligent, and collaborative. Jeff is a business executive with over twenty years of talent development and organizational strategy experience working with C-suite leaders in Fortune 100, Forbes top 25 private, for-profit, non-profit, and global companies in many industries.

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