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Personality Traits of Data-Driven Organizations

If you want to become a data-driven organization, be mindful of these 7 traits successful data-driven enterprises exhibit.


In my previous article, I introduced capability as a building block that can be used to describe what key things a company must do to be successful. Each capability defines the skills, processes, technologies, and policies that are needed to support its implementation. Each capability is designed and implemented to meet a desired level of performance.

Multiple capabilities are grouped into categories to help organizations carry out their activities and achieve their strategic intent. Capability maps show key groupings and relationships among individual capabilities. The maps help us design, implement, and manage the appropriate details to ensure completeness and consistency, thereby driving superior performance.

A capability map that supports data-driven enterprises will be developed in this series of articles. The map will provide a useful framework for describing, planning, and evaluating what new capabilities are needed and identifying which areas require improvements.

Before we develop a framework in future articles, we must take a step back and describe what it means to be data driven. The term data driven can mean different things to different people.

This article will introduce and describe some visible personality traits that are found in organizations that have successfully become data driven. Think of personality traits as characteristics we want to create or enable. By understanding these traits, the right capabilities can be identified, developed, and implemented

Personality Traits

Organizations have distinctive personalities rooted in their culture and history. As customers, suppliers, investors, or employees, we observe behavioral patterns in companies of all sizes. For example, as a customer we soon learn which bank, restaurant, store, or hotel consistently offers superior service.

We soon learn as an investor which companies are more innovative compared to those that sit on their laurels and eventually become commoditized. As employees, we understand how decisions are made. Some companies let their managers use gut feel and intuition for all forms of decisions. In other environments, certain decisions must be supported by detailed analysis.

We sometimes see situations where one thing is said and something else is actually done. For example, a company may say they use data to drive decisions but in reality some managers still follow their instincts and dismiss the analytical results as "nice but not for me."

Another personality trait shows how information is shared across silos. Openness and collaboration are traits where information flows freely through the firm. A personality where politics and turf building are dominant shows information being hoarded and not shared.

Data-Driven Personality Traits

Organizations have a wide range of personality traits, including level of risk tolerance, degree of environmental stewardship, amount of product innovation, and approaches to customer service. Other personality traits include how processes are managed in terms of compliance, consistency, quality, and efficiency. Decision-making styles and compensation models are personality traits that encapsulate how we reward people for their results.

Seven key personality traits of successful data-driven organizations are identified and described below. They describe what observed behaviors demonstrate evidence of being data driven. The necessary capabilities for enabling these conditions can then be identified, developed, and implemented.

Trait #1: Business strategy is integrated with and enabled by data

The first trait of a data-driven personality is based on how strategy is developed. Integrating the business, technology, data, and analytics components into a single, unified approach is critical to becoming data driven. The overall strategy considers how data and analytics can enable business value and how employees will be encouraged to use new data sources and analytical models to drive their decision making.

Trait #2: Innovation and market disruption are evident

Companies cannot rely on previous successes to drive future prosperity. Eventually, a competitive advantage becomes commoditized and new ideas are necessary to sustain growth. Innovation is a necessary trait of sustainable companies. Data-driven companies use new ideas to create new revenue streams from data. Data can be packaged as new products or services that create additional customer value. For example, a heavy equipment manufacturer may use data from sensor readings taken from a customer site to provide a value-adding health monitoring service.

Trait #3: Data and analytics are widely embraced

Widespread use of data and analytics in driving decisions is the third personality trait. Diversity of data and analytical model types used across the organization is indicative of this trait, as is evidence that people are constantly searching for new types of data from additional external sources and evaluating their suitability for new applications.

Because the data and the models are relied on for new business areas, further evidence of this trait is the application of mature asset life cycle management principles as a sign that data has become a valuable asset to the organization.

Trait #4: Measurements quantify the right things

The fourth personality trait describes how measurement principles and concepts are used. This trait describes how well the "right" things are identified and measured. Measurement principles provide the core foundation of being data driven. Evidence that the right things are being measured appears in how feedback and analytical models are used to improve business or process performance. Maintaining situational awareness for managers and employees ensures risks are mitigated in real time.

Managers who become proactive in their decision-making style are evidence that predictive models are providing early warning detection of situations requiring corrective action. Further evidence of effective behavior is whether lessons from historical operations enable smarter decisions in the future. If the right things are being measured, problems from the past are not replicated and new solutions are applied to improve future operations.

Trait #5: Diverse thinking styles are developed and supported

Data and analytics models provide new perspectives about business operations to experienced employees. Developing new and diverse thinking styles is a trait of sustaining a data-driven company.

Critical thinking enables staff to interpret new information appropriately without clouding the issue with an existing bias. Development of statistical thinking helps employees quantify uncertainty inherent in the measurement and analytics components. Curiosity is a critical process needed to challenge the validity of conventions and assumptions based on new empirical evidence provided by data.

Trait #6: Experiments and tests are widely used

Data-driven organizations routinely create experiments and controlled tests to learn new relationships or to remove biases from critical decisions. Evidence of this trait is found in how often controlled tests are carried out to evaluate options and scenarios. Competitive research and analysis is completed using a wide range of digital content and analytics tools. Trait #7: People and teams communicate and share information

The seventh personality trait of data-driven enterprises is the most critical. It is not technical. It is a trait based on politics and culture. This trait describes how willing people are to share information with others to provide transparency to all stakeholders. If a company does not have a sharing and open culture, then it is unlikely to become data driven.

Another component to this trait is learning from past mistakes or failures. Companies that punish failure do not show this trait. Punishing failure can make data-driven personality traits impossible to develop.

Building a Framework

The next article in this series will introduce a framework based on capability mapping to describe how these data-driven personality traits can become realized.

About the Author

Mark Peco, CBIP, is an independent analytics consultant and educator. He is a faculty member of TDWI and holds undergraduate and graduate degrees in engineering from the University of Waterloo. Mark is based in the Toronto area and can be contacted at

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