From customer profiles to financials, every organization is swimming with data. The number of systems and data points available are constantly growing, making it increasingly difficult to manage information. The most forward-thinking organizations actively measure and improve their data maturity to ensure they’re progressing with the rapid pace of technology. 

Data maturity is the measurement of how advanced an organization’s data capabilities are. Data maturity models enable companies to assess their data governance practices, benchmark against similar organizations, and communicate to key stakeholders. It also supports the development and continuous improvement of data governance. Achieving higher levels of data maturity is essential to avoiding the pitfalls of poor data management, especially as technological capabilities only increase and complicate the data available.

Common Pain Points of Mismanaged or Immature Data 

Data governance should be a part of every organization’s business strategy. Mismanaged and immature data are key contributors to negative business outcomes, such as increased risk, subpar customer experience, and poor internal communication. 

• Inconsistent and incomplete client data: Without an effective data governance framework, it’s difficult to gain a complete, accurate understanding of the client, including their risk profile. 

• Inability to consistently define data attributes across business areas: Data inputs (such as what constitutes a client) aren’t standardized, making it difficult to integrate data from different departments. 

• Inability to accurately respond to regulatory requirements: In highly regulated industries, effective data governance is essential to meeting guidelines for data collection and maintenance, as well as providing documents and information needed for compliance. 

• Inability to fully understand the depth of customer relationships: Poor data governance makes it difficult to know who your contacts are at an organization, which services or products they may use, and how you interact with them. 

• Inability to assess the profitability of specific business areas, product lines, and customer relationships: Without good data governance, it’s hard to identify which revenue streams are actually generating profits, and how much.

• Inability to provide executive scorecards and dashboards in a timely manner due to manual processes: Effective data governance streamlines reporting, especially from different departments, making it easy to provide executives with a bird’s-eye view of your performance.

The data maturity model helps you identify the gaps that may be causing these negative outcomes and find opportunities to improve.

What Is a Data Maturity Model?

A data maturity model is a simple framework that can be used to monitor data governance efforts and share progress with key stakeholders. With a data maturity model framework, organizations can visualize the stages of data maturity and assess where they are currently and where they want to position themselves in the future. The data maturity model framework also allows you to benchmark your progress against your peers. 

The Data Maturity Model:

This example of a data maturity model demonstrates four levels of organizational maturity. The x-axis represents the stages of maturity:

1. Beginning Stage
2. Advanced Novice Stage 
3. Competent Stage
4. Current Leader Stage

The y-axis represents the capabilities increase of an organization as it reaches maturity:

1. In the first stage, there is limited to no IT Digital Maturity Structure in place.
2. In the second stage, there is some realization as to the importance of IT Digital Maturity. This is where we begin to strategize with key business stakeholders. 
3. In the third stage, IT begins working with the business to align its technology roadmap and strategy with the organization’s overall growth strategy. 
4. In the fourth stage, IT is working fully in unison with the business. Key business stakeholders drive the tactical direction of IT’s strategic roadmap.

Implementing data governance is a continuous journey. Data maturity models should be used periodically to help guide the data governance strategy. Whether an organization has no formal data governance program at all, is in the middle of implementing better strategies, or completed a major data improvement project, data governance maturity models are useful at each stage of the journey. 

What Are the 4 Stages of the Data Maturity Model? 

There are several different types of maturity model frameworks, each with slightly different stages. Some of the most well-known and commonly used examples are the Gartner, IBM, Stanford, and Dell models. To give you an idea of the stages of one maturity model framework, let’s look at the Dell model. 

1. 1. Data Aware: At this stage, an organization is using manual processes to compile data. There is little or no standardization, and reporting is often performed ad-hoc. 
   2. Data Proficient: Organizations at this stage are beginning to recognize the shortcomings of their data management capabilities. They haven’t addressed integration and siloed data, but they are ready to begin implementing a formal data management initiative. 
   3. Data Savvy: Data-savvy organizations have figured out how to use data for decision-making and differentiate themselves from competitors. IT departments and executive sponsors work in alignment to break down data silos.
   4. Data Driven: At this stage, organizations have integrated data silos and leverage advanced analytics to drive all decision-making. IT and all other parts of the business are working in unison to develop and maintain data management strategies. 

While each data model generally covers these same stages of maturity, there are some nuances between each one. The Gartner and IBM models, for example, include five stages. The differences between the stages are often the level of sophistication and adoption of data governance practices within an organization. 

What Are the 4 Pillars of Data Maturity Assessment? 

Regardless of the data maturity model you use, and the stages it contains, there are four key pillars to designing and using a data maturity model. Incorporating these pillars into your data maturity modeling will help ensure that your approach is methodical and comprehensive.

1. 1. Definition: Understand the scope of your data and the capabilities needed to realize data governance goals. 
   2. Standardization: Use a standard data maturity model to establish the stages of data maturity that apply best to your organization.
   3. Measurement: Assess the current state of your data maturity, using key performance indicators to measure progress. 
   4. Benchmarking: Compare yourself to peers in your industry.

Why a Data Governance Strategy Is Necessary

Higher levels of data maturity lead to the development of better data governance. Data governance and data maturity go hand-in-hand. If your organization is stagnant in progressing through the data maturity model, then the cause is typically poor or nonexistent data governance. To keep moving through the stages of the data maturity model, it’s important to implement a data governance strategy. 

The goal is to ultimately realize the organizational benefits of data governance. When companies set policies and procedures, streamline processes, and actively clean their data, the business benefits. For example, reports and dashboards can be generated quickly and accurately. Many sources of customer complaints can be prevented before they turn into a phone call or email. Records are more complete and accurate, making it possible to reliably meet regulatory requirements.

Without strong data governance, organizations incur unnecessarily high costs. According to Gartner, poor data quality costs businesses $12.9 million every year. These costs appear in numerous ways, such as increased customer and employee turnover, lost revenue, time wasted on manual processes, and inability to forecast and plan effectively. 

Common Misconceptions About Data Maturity Modeling and Data Governance

Some common misconceptions prevent organizations from implementing data maturity modeling and advancing their data governance efforts. Let’s take a look at some of these myths:

It’s a One-Time Process

Measuring data maturity and implementing data governance isn’t a one-and-done project. It should be part of an ongoing journey towards better data governance. Even the most advanced organizations have to continually improve, as data sets are always growing more complex. 

It’s the IT department’s Responsibility

Measuring data maturity isn’t solely the job of the IT department. As data maturity improves, more departments are involved in data governance efforts. That means that IT alone can’t be responsible for continuous improvement. Executive leadership must drive data governance strategies companywide to achieve higher levels of maturity. 

It’s Only Necessary for Regulatory Compliance

Improving data maturity has wide-ranging benefits, not just better compliance. There isn’t a single department that isn’t impacted by data—from sales to human resources. Better data governance helps them all achieve better outcomes and be better partners internally. 

It’s Only for Large Organizations

Organizations of all sizes are inundated with data. They can all reap the benefits of assessing their data governance maturity and progressing to higher levels. And data maturity models don’t require technical expertise to understand, making it possible for smaller teams to use them to improve data governance as well. 

It’s the Natural Result of Collecting Better Data

Collecting higher-quality data is a crucial aspect of data governance, but it’s just a single component of a more comprehensive approach to improving data capabilities. Better data governance involves processes, policies, and procedures. With all of those components in place, you can realize better data quality. 

It’s Extremely Difficult to Implement

Depending on where you are in your data governance efforts and where you want to go, achieving higher levels of data maturity can seem overwhelming. But the data maturity model allows you to visualize the stages you need to progress through in a simple way.

Get Expert Help To Maximize Data Maturity Modeling

Understanding where you are in your data journey can be difficult. Kenway makes it easy by providing actionable insights that enable you to progress to the next level of data maturity. We partner with organizations—many of which are in highly regulated industries—to build a data governance framework that enables them to maximize the use of their data. 

When a financial services firm was faced with incomplete, poorly managed, siloed data, Kenway implemented a data governance maturity assessment to identify gaps and opportunities in their data management practices. We interviewed stakeholders from across the business to uncover pain points and determine where they wanted to be in their journey to data maturity. We then provided key recommendations to help them reach the next stage of data maturity. Read the case study to learn more. 

Understand Your Level of Data Maturity

Data maturity models help companies assess their data governance efforts and identify the actions needed to continually improve. By regularly performing data maturity assessments, you can realize the full benefits of data governance and benchmark your progress against your peers.  Organizations that assess their data maturity are more likely to be successful in their data governance efforts since it’s the best way to identify areas for improvement and root out the causes of immature data management. 

Not sure how to get started? Kenway’s experts are ready to help. We’ll help you hone in on the objectives you want to achieve, perform a data maturity assessment, and provide a roadmap to realizing your goals. Get in touch today.