The Stages of Data Maturity: A Beginner’s Guide to Unlocking Your Organization's Data Potential
The ability to effectively harness the power of data is table stakes for success in the modern business era. Data is a key differentiation factor for any organization, fueling insights, driving decision-making, and ultimately (hopefully, ideally) propelling growth.
The extent to which an organization is able to effectively use its data to inform its decision-making is known as data maturity. Data-mature companies rely on their organization’s data to make well-informed, timely decisions, rather than instinct or tradition. But how should an organization assess its data maturity, and what can it do to improve its data maturity? That’s the topic for today. Let’s get into it.
The 5 Stages of Data Maturity
Literally What Is Data Maturity and Why Does It Matter?
Data maturity is a framework for measuring how well your organization leverages data to drive decision-making, innovation, and growth. It's not just about having a bunch of data - it's about continually turning that data into actionable insights and strategic advantages.
As an organization progresses through the stages of data maturity, it unlocks new capabilities and benefits. Your team goes from making gut-based decisions to data-driven decisions, from siloed data to integrated data, and from reactive to proactive insights. It’s important for companies to review and assess their data maturity because companies with high data maturity are more likely to outperform their peers, drive innovation, and create new business opportunities. Companies can avoid missed opportunities and gain a competitive edge by effectively collecting, managing, analyzing, and utilizing data.
Data maturity ultimately allows your company to differentiate. Most tools can be replicated, but the combination of proprietary data and digitized in-house processes allows your company to stand out from the competition.
One important note: this post is intended as a broad overview of the data maturity framework. We’ve found that leveling up your data maturity can actually look different at each stage of the data lifecycle, but that’s a more advanced topic for another blog post.
The Data Lifecycle
Don’t worry if these concepts are new to you - every journey begins somewhere, and we’ll give you some ideas for improving your organization’s posture at every stage of the data maturity framework. You may even find out you’re further along than you realized.
What Are We Talking About When We Talk About Data?
When we talk about "data" in the context of data maturity, we're referring to the raw information that organizations collect, process, and analyze to drive decision-making and create value. This data can come from a wide variety of sources, both internal and external. For example, a retail company might collect data on customer transactions, website visits, and social media interactions to gain insights into consumer behavior and preferences. A healthcare provider might gather data from electronic health records, medical devices, and patient surveys to improve patient outcomes and streamline operations. A manufacturing firm might use sensor data from equipment, quality control metrics, and supply chain information to optimize production processes and reduce costs.
Data — broadly defined — is the foundation upon which modern organizations build their strategies and innovate their products and services, regardless of the industry or use case. The way a company utilizes that data, its data maturity, becomes as crucial a pillar in building a competitive advantage as its people, its culture, and its products.
The 5 Stages of Data Maturity
Stage 1: Ad Hoc - The Starting Point
The Ad Hoc stage is defined by humans performing manual processes. Data is often unorganized and inconsistently used across the organization. There may be no design or plan for data creation, storage, and processing. How the data will need to be used is secondary to where it’s being stored. Data usage is centered around manual steps. Because these are not automated, programmatic processes, they may be performed inconsistently or even incorrectly.
When data is needed, someone goes and looks for it — within files stored on a computer, on paper, or in shared file systems in the cloud, like SharePoint. But the people who need certain data might not know where to look, and the data they want might not be available. There is very little coordination between the creation of the data and the accessibility of the data.
Accessing and utilizing data is a very manual, human-driven process. Processing data is done by hand, not by machine. The ability to process data accurately is based on thoroughly written & documented standards, and following them not just writing them. Decisions are primarily based on intuition and gut instincts rather than data-driven insights.
There is little to no data maintenance. New data comes in but the old data it’s meant to replace is not deleted or archived. It’s often unclear what data is most recent or relevant. This can in turn create a greater security risk as more and more old data accumulates.
Remember: there’s nothing wrong with being in the Ad Hoc stage, if that’s what the company can afford and is capable of. In fact it’s very common for new projects to start in the Ad Hoc Stage! But companies often overestimate the amount of work required to mature from the Ad Hoc to the Defined stage. After all, you are already storing and accessing your data, it’s simply being done manually, inefficiently, and possibly involving the occasional human error.
To progress from this stage, organizations should:
Identify and consolidate data sources
Establish basic data governance policies
Encourage collaboration and data sharing among teams
Solicit input from staff on processes that could be improved and the inefficiencies that frustrate or complicate their roles.
Stage 2: Defined - Laying the Foundation
In the Defined stage, organizations start to establish basic processes and standards for data management. Data quality and consistency become a priority, and the benefits of a more structured approach to data begin to emerge.
Organizations begin to implement software systems that create their own data. They may have a CRM, which is separate from their ordering system, which is separate from their accounts service. These multiple systems create and store the data they rely on, but there’s no easy way to access all the data in one place. If any of these separate systems needs data from the other systems, there is a set of steps we can take to move the data to them, but it probably requires some level of manual effort.
Here we start to see some automation, some coordination, and some cooperation between data creation and data processing. Stronger rules are in place. The key difference is that the use of human intelligence is not optimized. There’s no good hand-off between what computers do automatically and what requires human approval.
To advance from this stage, organizations should:
Maintain a strict set of rules and Standard Operating Procedures for how data is validated and when data should be archived.
Ensure strong data access controls, understanding how to separate and organize sensitive data.
Maintain data in relational databases that make it easy to link records together. Be thoughtful about how tables and schemas are constructed to facilitate improved data accessibility later.
Stage 3: Scalable - Expanding Data Capabilities
At the Scalable stage, organizations are ready to expand their data capabilities. Processes are beginning to be automated, new data sources are integrated, and more teams are empowered to leverage data in their decision-making.
Good integration management is a key feature of an organization in the Scalable stage. As new data is created or ingested from a system, automation is in place to store it in a central, accessible location. This way, other tools or softwares can easily utilize this data quickly. Created data may even be captured in storage systems in real time instead of on a schedule. Any collected or stored data is automatically processed and altered as needed to match a designed storage format, making subsequent use easier. There are built-in security controls, configuring user access for different classes of data. With these automations and designs in place, integrating new software or tools with their data becomes a more straightforward process.
A key factor between Defined and Scalable is the ability to leverage real-time technologies. We often see the same tech implemented in these two stages, it’s simply used more efficiently at the Scalable stage. Automation maintains and processes data, and human intervention is not routine, but rather reserved for higher-risk processes or critical changes.
To progress from this stage, organizations should:
Implement a data lake or data warehouse to centralize and scale data storage
Invest in advanced analytics tools, such as machine learning and predictive modeling
Foster a data-driven culture through training and resources
Stages 4 & 5: Strategic and Innovative - Driving Business Value and Pioneering New Frontiers
Stages 4 and 5 are the most advanced stages of the data maturity framework.
Strategic is about using your data capabilities to aggressively compete. You create data assets that no one else has, you deliver convenience that your competitors can't.
At the Innovative stage you're using your data and tech capabilities to create an entirely new market. Think of Uber creating ridesharing by digitizing taxi dispatch and building a platform for their fleet. Their cost structure does not resemble a taxi company in any way. They could do this as a tech and data first company.
In these stages, data becomes a core part of an organization's DNA, driving strategic initiatives and powering innovation. Data insights are consistently used to guide decision-making and optimize operations. These companies have mastered and optimized the data lifecycle and use it to aggressively drive value at their organization. They easily leverage data to transform their products and services, as well as their internal operations.
To maximize ROI, most companies should prioritize advancing through the first three levels of the data maturity framework, as these stages typically present the most significant challenges and opportunities for improvement.
Is it always important to be growing in data maturity? It depends! Improving your maturity requires money, time, and effort. Depending on your business, you might be very comfortable operating at your current level, or it might not be the right time to focus on getting to the next level of data maturity. Every organization is different, so it’s about balancing what you need, what you can do, and how fast you want to get it done.
For a deeper dive into data maturity and the data lifecycle, check out our online video course, Data Management Foundations.
If you want help assessing your organization’s data maturity, or need an unbiased, expert opinion on how you can improve your data maturity, schedule some time with us today!
