In today’s digital-forward society, you can’t avoid the growing importance of data—especially in the realm of business. Regardless of an organization’s size, industry, or business model, you can’t plan for optimal growth and success without also discussing data. A mature approach to data can help businesses build greater trust in their information, make more informed decisions, and use data more effectively across the organization.
But what exactly does it mean to achieve data maturity, and how can you determine whether your business needs to optimize its approach to data? Below, we’ll delve into what data maturity truly entails while helping you discover where your business lands on this scale.
What is data maturity?
In the business world, data refers to any information an organization collects surrounding its customers, workers, marketing plans, and organization at large. But if data is the information collected and processed, what exactly is data maturity?
Put simply, data maturity refers to the extent to which your business can collect valuable data, derive meaning from it, and leverage this information in the decision-making process.
Your business’s data maturity is determined by how your organization’s data is:
- Processed
- Analyzed
- Utilized
It’s important to note that the quantity of data does not directly correlate to the maturity of the data in question. The goal is to strive for quality data collection that helps an organization successfully make the best marketing and operational decisions possible.
At Experian, we track three key components that make up data maturity:
Strategy: We look at the processes behind data management, from implementing data quality control measures to sharing insights with a wider range of roles.
People and skills: We take note of data ownership, considering whether there is a chief data officer in place and who manages insights across the business. We believe that data skills are becoming increasingly important, whether an organization has a data literacy program or is hiring for data skills.
Technology: We look at an organization’s tech stack, data quality management functionality, and how user-friendly the software is. Depending on the organization’s data skill level and resources, technology can make or break the data experience.
The three elements live across the four levels of data maturity, which we discuss in the next section.
The 4 levels of data maturity—how data mature is your business?
Below, we’ll delve into the four stages we use to assess data maturity that can serve as a benchmark for how your business is performing from a data quality standpoint.
1. Under-developed
If you’ve never really put much thought into your business’s data and find that data at large has never been a topic of discussion in your organization, you’re likely to find yourself starting from square one. At the base level, organizations that lack an understanding of data quality and its implications fall into an under-developed category. If you notice your organization confronting data quality issues, manually fixing data problems as they arise, and having an inconsistent understanding of data across the business, you may fall into this category.
For organizations at the under-developed stage, establishing a more intentional approach to data management can create a stronger foundation for future progress. Identifying existing data quality challenges, defining ownership, and introducing consistent processes can help your organization begin moving toward greater data maturity. Reaching out to a data management expert can also be a helpful place to start.
2. Reactive
Maybe your organization has started to think about data and its importance within your business, but it hasn’t laid the foundation for the proper metrics or processes necessary to utilize this information in a meaningful way just yet. As the name indicates, a “reactive” business reacts to data when issues arise and need to be fixed. However, discussions surrounding data aren’t evenly spread throughout the business.
While tackling data quality issues in silos is a start, it’s crucial to expand the data experience throughout your organization. This means that if only certain departments have the tools and knowledge necessary to leverage data insights, it’s important to expand data access and training to those outside of traditional IT roles, too.
3. Proactive
Moving along the data maturity scale, businesses are likely to achieve a proactive state once they’ve established clear data quality success metrics that help them make better use of their data across the board. Within this level, there’s a more universal knowledge of data across the organization, which helps team members use data in meaningful ways when performing their daily tasks. There’s also an increased emphasis on data discovery, which places the organization a tier above “reactive” businesses that focus more heavily on root cause analysis.
If you think your business identifies with a proactive level, you may still confront issues surrounding data trustworthiness and optimal data governance. Investing in a trusted data management program could be the next step in your journey toward higher data maturity.
4. Optimized
At the highest level, businesses with the greatest data maturity have established an advanced data extraction and interpretation strategy to reap more business value from their data. An organization in this category has successfully:
Established advanced data quality measures
Created confidence in the quality of its data
Invested in the right data professionals to oversee company-wide data assets
Incorporated data quality monitoring into everyday operations
While it may seem like achieving a high level of data maturity is the endpoint, it’s important to note that data management is an ongoing process. Businesses at an optimized level can still benefit from improving their data management programs over time to reduce costs and automate more of their daily processes.
If you’re still not sure where your organization lands on the data maturity scale, we’ve put together a free assessment that can help you get a better idea of where you stand so that you can begin taking the steps necessary to strengthen your approach to data.
My business isn’t as data-mature as I thought: What now?
Regardless of where your business lands on the data maturity scale, your business could still benefit from enhancing your data’s trustworthiness and developing new data quality strategies as time progresses.
Data isn’t stagnant, so your data strategy shouldn’t be, either. In the modern world of business, data has become a competitive asset, and what you do with the information you collect matters. At Experian, data is top of mind for every decision we make. When you reach out to a team member, one of our data quality experts can connect you with the resources you need to kickstart your data maturity journey.
Unlock the importance of data in your business
Building greater data maturity can help your organization get more value from its data and create a stronger foundation for informed decision-making. Data quality and governance should remain important considerations for your business—regardless of your organization’s industry, size, or overarching goals. As you plan for the future, evaluating your current data management practices can help uncover opportunities to strengthen how data is managed, trusted, and used across the organization.
Explore how strengthening data quality and data management can help your organization advance its data maturity journey and make data a more valuable part of future business planning.