Resources > Articles

5 Common Misconceptions About 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.

Businesswoman networking using digital devices

There is a lot of discussion in the business world these days about data maturity.  

Companies that don’t utilize data lean heavily on intuition and industry experience when making business decisions. 

In contrast, a data-driven company will use market insights, trends, and patterns found through analyzing data in their decision-making process. 

As a result, they often experience several benefits, including: 

  • Increased efficiency 
  • Better products and services 
  • Faster speed to market 
  • Improved identification of opportunities and risks  

[How does your organization stack up? >> Take the Data Maturity Assessment

 

However, there are still a lot of questions around data maturity, including: 

  • Why is data maturity necessary? 
  • How can you achieve it? 
  • What are common pitfalls? 

Unfortunately, there is a lot of misinformation out there about data maturity. This article will focus on the five common misconceptions about data maturity.

 

1. Achieving data maturity is a linear process.  

The biggest misconception about data maturity is that it’s a linear process that starts with collecting data and ends with using it to make decisions.  

In reality, achieving data maturity is more like a journey than a destination. There will be ups and downs along the way, and you may find yourself revisiting earlier steps as you learn and grow. 

Achieving data maturity is an ongoing process, not a one-time event. As your organization grows and evolves, so will your data needs. It’s crucial to continuously monitor your data to ensure that it remains accurate and actionable, and make adjustments to your governance processes as necessary. Otherwise, you’ll quickly find yourself back at square one. 

 

2. Only large companies or IT departments can achieve data maturity.  

It’s also a common misconception that only large companies can achieve data maturity. This simply isn’t true.  

There is an ability and a need for all companies to improve their data maturity. 

According to a recent KPMG survey, “only 29 percent of [companies] have adopted a comprehensive data strategy at scale, even though more than 70 percent of [companies] believe it is likely that effective and widespread data usage will radically change the business model.”

Data maturity is something that any organization can strive for, regardless of size or industry. 

While IT departments play a critical role in achieving data maturity, they cannot do it alone. Business leaders must be involved in setting the goals for data maturity and ensuring that everyone in the organization understands the importance of adhering to strict data governance standards. 

Only by working together can organizations hope to achieve true data maturity and move up in the continuum.

 

3. Data maturity is expensive and time-consuming to achieve. 

This one depends on how you look at it.  

Yes, achieving data maturity requires investments of both time and money upfront. However, these investments will pay off in the long run by helping you avoid costly mistakes and missed opportunities. In other words, achieving data maturity may require some short-term pain but it will result in long-term gains for your organization.

 

4. You need perfect data to achieve data maturity.  

This misconception probably arises from the fact that quality data is important for achieving data maturity. However, perfection is not required. 

In fact, striving for perfection can actually impede your progress towards data maturity. The key is to focus on continuous improvement rather than perfection. 

Data maturity and data quality are two completely different concepts. 

Data quality refers to the accuracy, completeness, timeliness, and consistency of your data. Data maturity, on the other hand, refers to how well-organized and structured your data is. A dataset can be of high quality but low maturity (i.e., poorly organized and structured) or vice versa (i.e., well-organized and structured but with low quality data).

 

5. Once you achieve data maturity, you’re done forever.  

Finally, some people mistakenly believe that once you reach a certain level of data maturity, you’re done forever and don’t need to do any more work in this area. This couldn’t be further from the truth! 

Data maturity is an ongoing journey, not a destination. As your business changes and grows, so will your needs in data management and decision-making. 

Piling up more and more data isn’t the answer either. While it’s true that more data can help you make better decisions, it’s not the only factor that contributes to data maturity. Oftentimes, access to more data can also bring more access to other issues. 

Additionally, other factors such as the quality of your data and how well it’s organized are also important for data-mature organizations.   

 

Conclusion 

Data maturity is an important concept for businesses to understand and strive for—but there are many misconceptions about what it really entails. 

In this article, we’ve debunked some of the most common myths about data maturity. If you want to know more about improving data-related decisions for your business, check out our course and begin to translate data insights into business strategy.

 

Leverage Data for Business Decisions 

Data Science for Business Leaders is designed for business leaders to partner with data professionals, learn what problems to solve with data, and how to leverage the findings to make better decisions. 

  • Focus on data projects that drive business impact 
  • Gain better outcomes through stronger partnerships 
  • Identify the fastest path to actionable insights 
  • Champion data-driven decision making 

>> 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

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
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
Balancing Profits and Ethics
Article

A Conversation on Ethical Use of Data in Business

It’s vital for organizations to be open and transparent and spend time discussing ethics within the business.
Category: Data Science
professional analyzing reports
Article

6 Dimensions to Measure Data Quality in Your Company

Data quality is a critical aspect of any business. If your data is inaccurate, you will make poor decisions that can hurt your company. In this blog post, we will discuss the 6 dimensions to
Category: Data Science

OTHER ArticleS

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
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

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.