Resources > Articles

6 Pillars of Data Maturity

Post Author
  • Pragmatic Institute is the transformational partner for today’s businesses, providing immediate impact through actionable and practical training for product, design and data teams. Our courses are taught by industry experts with decades of hands-on experience, and include a complete ecosystem of training, resources and community. This focus on dynamic instruction and continued learning has delivered impactful education to over 200,000 alumni worldwide over the last 30 years.

bar graph with six pillars

The concept of data maturity is related to the concept of being data-driven. Data maturity is a term we use to understand the degree to which an organization has the required elements in place to be data-driven. 

You may ask yourself, what does it mean to be a data mature organization? It means data is fully embedded, and employees can talk about it in meetings and discussions and apply it in their daily activities. 

So, what are the features and practices that contribute to data maturity? Becoming data mature is beyond having data and wanting to be data-driven. 

Below are the pillars of data maturity and what needs to be in place to be data mature at an organizational level.

 

1. Support from Leadership

 

For an organization to achieve data maturity, it’s essential to have support from leadership. There are various ways leaders can support employees in data-driven organizations; below are a few. 

 

  • Modeling of data-driven behaviors: 

Leaders need to demonstrate how data supports their decisions while reinforcing employee behaviors reflective of data use. 

 

  • Recognition and reinforcement of data use: 

When leaders see other employees or teams utilizing data in their roles, they must highlight and encourage the practices they are using. 

 

  • Trust and confidence in employees: 

Employees need to know leaders believe and trust employees with the data they need to drive the organization forward.

 

  • Interest in multiple types of organizational data: 

The leaders are concerned with data in the organization. They are aware of data impacts that contribute to the performance of the business, including the company culture, employee satisfaction and customer engagement.

 

2. Organizational Values 

 

A data mature organization embraces specific values that contribute to being data mature. The organizational values improves transparency in businesses and data can be applied without bias when making decisions. 

 

  • Agility and experimentation:

When an organization values agility and experimentation, they know failure is necessary before real innovation or growth is possible. 

 

  • Openness to new insights:

A data mature organization is open to learning and adapting based on what the data is showing. The organization is not threatened when they hear unflattering findings or suggestions that changes may need to be made. 

 

  • Focus on results/outcomes:

The organization is results-oriented, focusing on outcomes while encouraging staff to generate and test their data hypothesis.

 

  • Empowerment of staff:

The business encourages team members to generate and test their data-supported hypotheses and question the status quo. 

 

3. A Data Mindset

 

Having a data mindset is essential in an organization and ties back to leadership modeling the importance of data to employees. Viewing data as something that can help businesses grow and succeed will improve the organization’s maturity and foster the idea that data is an important asset. 

 

  • Data is a core business asset:

In a data mature organization, they view data as a core business asset essential to business growth and success, not just something that is nice to have. 

 

  • Data use is a priority and requirement:

An organization recognizes the importance of data and uses data as a requirement and priority for everyone in the business. 

 

  • Data is a primary tool for decision-making: 

Data is the primary tool for decision-making by the entire organization. 

 

  • Curiosity and questions about data are encouraged:

Employees at all levels are curious and willing to challenge assumptions with data. This also means questioning what the data might tell us, even if this leads to some uncomfortable conversations. 

 

4. Data Access

 

Having data access is one of the most technical pillars to become a data mature organization. For an organization to be data mature, staff need access to the data. Organizations can’t expect staff to use or make data-driven decisions if they can’t get the data or have to wait weeks for it. 

 

  • There is a central data source containing high-quality data:

Staff have to have access to the data. In addition to having access, it’s also important the data is clean, high-quality and comes from a central source.

 

  • Data is easily accessible by all employees: 

Team members need to easily access the data to perform their daily work. Employees need to be able to get data when they need it and know where to find it.

 

  • Data is shared across functional areas: 

Access to data from one source allows employees to collaborate across teams and share data from multiple functional areas.  

 

  • Ethical and privacy measures are in place:

A data mature organization has processes in place to prevent and mitigate data-related ethical and privacy concerns. 

 

5. Alignment of Business and Data Teams 

 

In organizations that lack data maturity, data and business teams don’t speak the same language, and it can cause frustration with each other. Data teams are frustrated because their reports and findings are not being used. Nevertheless, business teams are frustrated because they don’t understand data analytics methodologies or are given analytics reports they can’t use. 

 

  • Common data language and framework:

When business and data teams are in alignment, they can take an active role in designing, monitoring and sharing insights from data projects.  

 

  • Mutual understanding of business and data roles:

It’s important for data and business teams to understand their contribution and respective roles in data projects and how to best support each other. 

 

  • Data questions and metrics tied to business metrics:

Data questions and success measures tie to business goals and metrics in current business realities. 

 

6. Data Literacy

 

Finally, the last pillar of data maturity is data literacy. Data literacy is a skill everyone who works with or interacts with data should have for an organization to be considered data mature. 

 

  • Employees are data literate for their roles: 

Being data literate means knowing how to read, locate, work with, analyze, and/or communicate with data. 

 

  • Data literacy training is available:

Data mature organizations usually offer training and support to help employees enhance their data literacy skills. Usually, employees at multiple levels in the organization are eager to learn more about data. 

 

  • Employees know what data is relevant to their role:

There are different levels of data literacy, and employees must have the appropriate level of data literacy in their roles.

 

  • Everyone knows what data is available:

Employees of a data driven organization have a comprehensive understanding of what data is available within the organization and can easily access it when needed.

 

 

How Data Mature is Your Organization?

 

Curious to know where your organization falls in the data maturity continuum? Take The Pragmatic Institute Data Maturity Assessment to find out if you’re making the most out of your organization’s data and start building a data-driven culture. 

Take Assessment 

 

Continue Learning 

 

Do you want to gain powerful business insights with data? Learn to translate data insights into business strategy with our course Data Science of Business Leaders. Understand how business leaders and data professionals contribute at each stage of data projects to drive decisions and solve problems. 

Learn More

Author

  • Pragmatic Institute is the transformational partner for today’s businesses, providing immediate impact through actionable and practical training for product, design and data teams. Our courses are taught by industry experts with decades of hands-on experience, and include a complete ecosystem of training, resources and community. This focus on dynamic instruction and continued learning has delivered impactful education to over 200,000 alumni worldwide over the last 30 years.

Author:

Other Resources in this Series

Most Recent

two professionals looking at spreadsheets and graphs
Article

4 Types of Data Analytics to Improve Decision Making

Data analytics is the science of evaluating raw data to make outcomes from the data at hand and is crucial for organizations to make informed decisions. This article will go through the four primary types of data analytics and how organizations can use them efficiently to drive business outcomes.
Category: Data Science
data privacy login error message on laptop
Article

Data Privacy: Fact or Fantasy?

While the ethics of A.I. might seem removed to many of us, how we govern and use technology is something that we face every day. Learn more about the privacy laws and data protection.
Category: Data Science
person evaluating bar graph report and typing on laptop
Article

20 Ways to Improve Data Quality

Businesses gather and have access to so much data, it's essential to ensure the data at hand is of high quality. And, at the end of the day, data should be the foundation of all business decisions. Learn 20 ways organizations can improve their data quality.
Category: Data Science
two people evaluating reports
Article

Uncovering the Relationship Between Data and Product

Data is everywhere; you can find it in companies in every industry at every scale. Data-driven organizations are leveraging data to drive business outcomes and better serve customers.
Category: Data Science
Illustration of Microsoft Clippy on a stage with roses on the ground.
Article

Context-Driven Customer Engagement

Data collection and analysis have made it possible to better understand end-user behavior and activity so that communication within a product can be optimized to drive and enhance user engagement. Organizations can gain insights and provide context through powerful combination of software-usage analytics and in-application messaging.
Category: Data Science

OTHER ArticleS

two professionals looking at spreadsheets and graphs
Article

4 Types of Data Analytics to Improve Decision Making

Data analytics is the science of evaluating raw data to make outcomes from the data at hand and is crucial for organizations to make informed decisions. This article will go through the four primary types of data analytics and how organizations can use them efficiently to drive business outcomes.
Category: Data Science
data privacy login error message on laptop
Article

Data Privacy: Fact or Fantasy?

While the ethics of A.I. might seem removed to many of us, how we govern and use technology is something that we face every day. Learn more about the privacy laws and data protection.
Category: Data Science

Sign up to stay up to date on the latest industry best practices.

Sign up to received invites to upcoming webinars, updates on our recent podcast episodes and the latest on industry best practices.

Training on Your Schedule

Fill out the form today and our sales team will help you schedule your private Pragmatic training today.

Subscribe

Subscribe

Training on Your Schedule

Fill out the form today and our sales team will help you schedule your private Pragmatic training today.