Resources > Articles

The Stages of the Data Maturity Journey

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.

The Stages of the Data Maturity Journey

Data maturity is the degree to which an organization uses and makes the most of its data. And maturity takes time; many organizations spend five years or more to reach full data maturity.

Additionally, 91% of businesses have not reached a “transformational” level, where data is being used to predict and create a desired future. 

In reality, data maturity exists on a continuum. There are stages of little to no data maturity, all the way up to full data maturity. 

 

The Journey to Data Maturity

At Pragmatic Institute, we created a 4-stage framework with the stages of data maturity. At each stage, there are practices that contribute to why the business is at a specific level of data maturity. 

And you might ask, what is the cost of being data naïve? 

Well, there is a notable gap between organizations with mature data practices versus organizations who are not data mature. 

On average, data decisive organizations release twice as many products and increase employee productivity at double the rate of organizations with less data maturity. Continue reading to learn the data maturity categories at each stage of the journey. 

 

Data Naïve 

The organization relies on process, output, and how things have always been done. Decision making is made largely on the basis of rank, experience and/or instinct. The data is siloed and not consistent across the organization. Most of the staff are not aware of what data, if any, exists or how it might be used in the organization. 

Leaders and data teams, if they exist, rarely interact. The level of data literacy among employees is low, with no plans to increase it. Only a few employees understand how they could use data in their role. Additionally, leaders do not model data use or communicate its value within the organization.

 

Data Conscious 

The organization is aware of the potential value to the business. However, it seldom uses it to make decisions. Data use is in the hands of a small number of skilled data champions or early users. Data is not used by most personnel. Employees who may wish to use data for decision making may have a hard time accessing data. Additionally, they may not understand what data is available to them. 

Data personnel, if any, may produce reports that are ignored or irrelevant to business leaders. Additionally, leaders are aware of the value of data to the business. However, they do not have a clear plan yet for data to be of value across the organization.

 

Data Informed 

The organization knows the value of data to the business. They have applied some data use cases but use data inconsistently or only in select areas. Therefore, the organization is aware of the importance of a central data source, data dictionaries, data access, and data clarity. They are working toward making these a reality. 

The organization understands the importance of data literacy. They are creating opportunities for employees to upgrade their skills. The data and business teams sometimes struggle to be on the same page. However, they are making strides toward alignment. Business leaders are talking more about data. They are starting to model data behaviors. They are also empowering employees to use and act on data. The organization knows its direction but has not arrived at its final destination.

 

Data Decisive 

The organization is truly data-driven. Data is accessible, trustworthy, clear, and protected properly. Employees use data as a decision-making tool. At the same time, the organization can learn from occasional errors to improve future strategies. 

Staff use data in their everyday work. They are empowered to use data, question it and share it as appropriate. They are also empowered to enhance their data skills. Leaders and data teams align on data projects. Employees understand their roles. Leaders also show behaviors that show their commitment to data-driven decisions.

 

Data Maturity Assessment 

Curious to know where your organization falls in the data maturity continuum? Our Data Maturity Assessment is a powerful tool for organizations that want to measure and grow their level of data maturity. 

Take our assessment – comparing results with your colleagues – to discover how data mature your organization is and start building a data-driven culture. 

Take Assessment 

 

Continue Learning 

Are you ready to gain powerful business insights with data? Enroll in Data Science for Business Leaders to learn how to partner with data professionals to uncover business value, make informed 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

build a successful self service business intelligence
Article

3 Tips to Build a Successful Self-Service Business Intelligence (BI)

In this age of information overload, we’ve become accustomed to having the answers to all our questions at our fingertips. Enabling your staff with the information they need means building a successful self-service analytics platform to help make better business decisions.
Category: Data Science
Data Scientist Presenting Insights to Team
Article

Data Analytics vs. Data Mining: What's the Difference?

Both data analytics and data mining are important skills for any data scientist to master. When deciding which approach to use, it's important to consider the specific problem you're trying to solve and the type of data you have available.
Category: Data Science
Businesswoman networking using digital devices
Article

5 Common Misconceptions About Data Maturity

Companies that don’t utilize data lean heavily on intuition and industry experience when making business decisions. Data-driven companies are different.
Category: Data Science
The Power of Data Storytelling for Business
Article

The Power of Data Storytelling for Business Impact

In a world where data is increasingly becoming more accessible, it is more important than ever for businesses to learn how to leverage data to their advantage. 
Category: Data Science
paperwork and reports on 80/20 rule in data
Article

Overcoming the 80/20 Rule in Data Science

Data practitioners spend 80% of their valuable time finding, cleaning, and organizing the data, leaving only 20% to actually perform analysis on it. More often than not, data scientists spend hours preparing and cleaning the data to produce a report for stakeholders, only to find out they were looking for something else or didn’t understand the analysis enough to act on it.
Category: Data Science

OTHER ArticleS

build a successful self service business intelligence
Article

3 Tips to Build a Successful Self-Service Business Intelligence (BI)

In this age of information overload, we’ve become accustomed to having the answers to all our questions at our fingertips. Enabling your staff with the information they need means building a successful self-service analytics platform to help make better business decisions.
Category: Data Science
Data Scientist Presenting Insights to Team
Article

Data Analytics vs. Data Mining: What's the Difference?

Both data analytics and data mining are important skills for any data scientist to master. When deciding which approach to use, it's important to consider the specific problem you're trying to solve and the type of data you have available.
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.