A Brief Summary of Data Governance Maturity Models (2023)

Published By - Kelsey Taylor


Data quality cannot be ensured in a company without data governance protocols. Data’s quality rapidly deteriorates when it is unstructured and updates are made without documentation. This causes data teams a lot of trouble and also prohibits business users from exploiting firm data to innovate.

Inaccurate data sets are the result of poor data quality and a lack of data management methods. Additionally, inaccurate data can have disastrous repercussions, ranging from poor company decisions to possible data breaches and expensive compliance violations.

Organizations must implement a data governance strategy to address these problems, but for this strategy to be effective, there must be a high level of data maturity. Adopting a data governance maturity model is the best approach to accomplish this.

Data Governance Maturity and Its Models

Organizations must follow a data governance maturity model in order to reach a higher level of data governance maturity. There are many applications of this paradigm, but before we delve into the most well-known ones, let’s define the terms first.

What Exactly is Data Governance Maturity?

The degree to which an organisation has adopted and implemented data governance efforts is referred to as its level of data governance maturity. A young organisation will have a lot of disorganised data and won’t be leveraging it to spur expansion. As opposed to this, a mature organisation would understand the value of data as a crucial corporate asset and will govern and manage it accordingly.

What is a Data Governance Maturity Model?

A data governance maturity model is a tool and approach used to assess your company’s data governance initiatives and clearly convey them to the rest of the organisation. All the procedures for managing, using, and innovating with data assets are in place in a mature firm. The maturity model can help businesses with less development accomplish this goal.

A few well-known data governance maturity models exist, with examples provided by IBM, Stanford, Gartner, and Oracle among others. These models give businesses a way to discover effective data management techniques, enable user access, guarantee high-quality data, and enable everyone inside a company to gain from these advancements.

It is a good practice to assess the maturity of your organization’s system periodically. Maturity is the quantification of an organization’s ability and scope for improvement in a particular discipline.

A high level of maturity implies higher chances of improvement after the occurrence of an error or any incidence for that discipline.

These improvements could be either the quality or the use or implementation of the resources within the organization.

Data maturity models help companies understand their data capabilities, identify vulnerabilities, and know in which particular areas, employees need to be trained for improvement.

It also helps organizations compare their progress among their peers.

With maturity assessment, there is never a “one model fits all” situation. Although individual models for different organizations and vendors do exist, most follow the “Capability Maturity Model” method.

Here, we will go through two Data governance maturity models developed by two different vendors. Let’s dive right in.

  • Data Governance Maturity Model – Gartner
    • Level 0: Unaware
    • Level 1: Aware
    • Level 2: Reactive
    • Level 3: Proactive
    • Level 4: Managed
    • Level 5: Effective
  • Data Governance Maturity Model – IBM
    • Level 1: Initial
    • Level 2: Managed
    • Level 3: Defined
    • Level 4: Quantitatively Managed
    • Level 5: Optimizing

Data Governance Maturity Model – Gartner

(Video) What is a Data Governance Maturity Model? #datagovernance #maturitymodel

First presented in 2008, this data maturity model looks at the enterprise information management system as one single unit. It has five primary goals, as follows:

  1. Data integration across the entire IT portfolio.
  2. Unification of content throughout the organization.
  3. Integration of master data domains.
  4. Smooth flow of information across the organization.
  5. Metadata management and semantic reconciliation.

This maturity model has a total of six stages of maturity. Each stage has its own attributes and action items. Let’s take a look at each stage in detail:

A Brief Summary of Data Governance Maturity Models (1)

Level 0: Unaware

At this level, there is no awareness of any data governance activities. There is no ownership, security, or any system defined for data in the organization.

The processes for creation, gathering, sharing of data, or information is not defined.

There is a lack of definition of common established standards for data gathering or storage for metadata management. No data models are outlined here.

Data exchange, storage, and archiving take place mainly over email. Strategic decisions are often made without enough information.

Action Items: With no awareness about any data governance policies in place, the system architects and strategy planners need to educate the business and IT leaders about the value of EIM.

Level 1: Aware

This is the stage at which a lack of data governance becomes evident. Business and IT leaders start to understand and acknowledge the value of information and EIM (Enterprise Information Management).

There is a well-recognized need for a standard set of tools, processes, and models in place to establish uniformity across the organization.

Action Items: System architects and planners develop an enterprise information management strategy to suit the business needs of the organization.

Level 2: Reactive

The business finally understands the importance and value of information. Sharing of information takes place between the internal teams in the organization.

Although the information management system is in place, the level of adherence is low.

Action items: The management needs to promote the EIM strategies defined in the level earlier as a solution to the cross-functional data exchange issues.

Level 3: Proactive

At this stage, the information management system is accepted and adopted. Now, this becomes imperative to support crucial business decisions. Information owners are assigned to govern the data.

Information sharing between teams is finally considered as a pivot for enterprise-wide projects.

(Video) Maturity Assessment for a Data Governance Maturity Model

The policies and standards defined earlier are now employed organization-wide. Data governance becomes a part of every project in the organization.

Action items: Take EIM a step ahead and identify the opportunities at the departmental level.

Level 4: Managed

Information, at this stage, is viewed as a valuable asset to the company. EIM standards and policies are well understood and implemented throughout the organization.

Information assets are categorized, and information metrics are defined. A committee is formed to solve inter-team information issues and to identify the places for the betterment of the same.

Action Items: Information management tasks need to be tracked and made sure they are in line with the EIM policies.

Level 5: Effective

This is the final level wherein it is safe to say that the organization has reached its goal in terms of information management.

Information is now considered to provide the company with an added edge over its competitors. EIM strategies are linked with improved productivity and efficiency.

Action items: Define and implement controls to ensure adherence to the policies defined throughout the process. The policies should be followed irrespective of a change of leadership in the organization or a change of direction.

Data Governance Maturity Model – IBM

Introduced in 2007, this data governance model addresses a total of 11 domains mentioned below:

  1. Data risk management and compliance
  2. Value creation
  3. Organizational structure and awareness
  4. Policy
  5. Stewardship
  6. Data quality management
  7. Information lifecycle management
  8. Information security and privacy
  9. Data architecture
  10. Classification and metadata
  11. Audit information, logging, and reporting

This model consists of a total of five levels. Let’s take a quick look at the characteristics and the action items required for each level:

A Brief Summary of Data Governance Maturity Models (2)

Level 1: Initial

There is little to no awareness of the importance of data. There are no set standards for managing data. The existence of silos and ad-hoc data managing approaches hinder the performance of the teams.

There is almost no adherence to project deadlines. Also, there is no formal management or tracking of data.

Action Items: The system architects should study the vulnerabilities regarding the data and information flow throughout the organization.

They should come up with a plan to manage the data and present the same to the stakeholders and IT managers.

Level 2: Managed

There is more realization of the importance of data and how it can benefit the organization. Data starts to be viewed as an asset in the company. There is a need for a set of data management tools and processes in place.

(Video) How To Select a Data Governance Maturity Model #datagovernance

Action items: Data regulation and documentation guidelines are defined and set in place to be implemented.

Level 3: Defined

The data regulation and management guidelines are defined as better and start integrating with the company processes. The regulation rules are refined and made less ambiguous.

There is a better use of technology to manage data. Data management practices are widely implemented throughout the organization.

Action items: Data stewardship is implemented. Risk assessment for quality of data and data management is made a part of the regular methodology.

Level 4: Quantitatively Managed

At this stage, all the projects follow the data governance guidelines and principles. Data models are documented and made available throughout the organization.

Measurable quality goals are set for each project and data process and maintenance. The performance of the business operations is continuously measured against the set goals.

Action items: Performance measurement metrics needs to be defined and set for each process.

Level 5: Optimizing

The cost of data management is reduced, and data becomes easier to manage. Operations are more comfortable to navigate through and are streamlined.

Data governance becomes an enterprise-wide effort that improves productivity and efficacy.

Action items: ROI for any data project should be continuously assessed, analyzed, and monitored to make sure the data governance rules are followed through.

We have explained the two maturity models as examples. Now it entirely depends upon an organization’s individual needs to select any of the two. This will help the organization realize the highest level of benefits; else, it’s a moot exercise.

But more importantly, enterprises need to assess themselves to not only understand where they rank among their peers in the marketplace but also to be able to plan effectively. Or else, the competition is very fierce out there and will surpass them by miles.


What is data governance maturity model?

The stage an organisation has reached in the acceptance and implementation of Data Governance initiatives is referred to as data governance maturity. Organizations will observe observable results that can be directly linked to their Data Management and Governance activities when they attain the maximum level of Data Governance maturity.

What are the five levels of maturity model?

(Video) What is the Data Governance Maturity Model?

Five levels of maturity models are as follows.

  • Initial: limited to nonexistent data governance
  • Managed: Users are conscious of the commercial worth of data.
  • Defined: The data policies are clearly stated.
  • Quantitatively managed: Measures for Enterprise-Level Data Governance are in place.
  • Optimizing: The price of data management is decreased

What are the 4 pillars of data maturity assessment?

The four pillars of data maturity assessment are as follows:

  • Strategy: a plan, a path, and a goal
  • Culture: willingness for data-driven decision-making and risk-tolerance
  • Organisation: promote data privacy, collaboration, and trust, as well as ongoing progress.
  • Capability: The knowledge, procedures, and equipment needed to achieve your data and AI goals.

What is the purpose of data maturity assessment?

The purpose of data maturity assessment are as follows:

  • Identifying areas for improvement and establishing development priorities.
  • Improved resource allocation for the creation of data governance.
  • Data governance level alignment with business strategy.
  • Monitoring the effectiveness of data governance.

Related Blogs:

  • Understanding the Importance of Data Governance Maturity Model
  • Foundation Models in AI: A new Trend and the Future

10 Best Free Content Curation Tools in 2019

Top 7 Robotic Process Automation Tools


What is data governance maturity model? ›

It's a way for an organization to assess their improvement. In a particular discipline in Howard

What is the purpose of a maturity model? ›

A maturity model is a tool that helps people assess the current effectiveness of a person or group and supports figuring out what capabilities they need to acquire next in order to improve their performance.

What are the 4 pillars of data maturity assessment? ›

Their indicators were mapped to the four pillars of the Global Data Barometer (GDB) and, in a separate exercise, to the stages of the Data Value Chain. The GDB pillars are data governance, data capabilities, data availability, and data use and impact.

What is a data governance model? ›

A data governance model is a framework that outlines processes and systems for data creation, data storage and maintenance, and data disposal. Rather than a single data governance model used by every organization, there are several types of data governance models.

What are the stages of data maturity model? ›

Towards a data maturity model

It's a process that can be broken down into four distinct stages of data transformation: (1) Realising; (2) Transforming; (3) Leading and (4) Innovating.

What is the purpose of data maturity assessments? ›

Data Maturity Assessment provides the best practices roadmap to help organizations manage their huge volumes of data effectively. The assessment helps organizations implement an evolved data strategy and solution to transform their data analytics processes to deliver business insights consistently.

What are the advantages of using the maturity model to assess an organization? ›

Maturity models help integrate traditionally separate organizational functions, set process improvement goals and priorities, provide guidance for quality processes, and provide benchmark for appraising current processes outcomes.

What are the 4 maturity levels? ›

The 4 Maturity Levels of Data Management for Stakeholder Engagement Activities
  • Level 1 – Ground Level: No digital trail. ...
  • Level 2 – Ad Hoc Level: Focus on data collection. ...
  • Level 3 – Operational Level: Reporting with lagging indicators; operation and project-focused.
30 Aug 2019

What is 4 Phase Maturity Model? ›

The correct answer is Information, Interaction, Transaction and Transformation.

What are the 3 key elements of good data governance? ›

The three critical aspects of building an effective data governance strategy are the people, processes, and technology. With an effective strategy, not only can you ensure that your organization remains compliant, but you can also add value to your overall business strategy.

What are the 4 pillars of data governance? ›

There are four pillars to the data governance framework to enable organizations to get the most out of their data.
  • Identify distinct use cases. ...
  • Quantify value. ...
  • Improve data capabilities. ...
  • Develop a scalable delivery model.
9 Mar 2022

What is the purpose of data governance? ›

Data governance (DG) is the process of managing the availability, usability, integrity and security of the data in enterprise systems, based on internal data standards and policies that also control data usage. Effective data governance ensures that data is consistent and trustworthy and doesn't get misused.

What does data maturity mean? ›

What is data maturity? All organisations produce, process and use data in some shape or form. But the presence of data does not necessarily mean that it is being used effectively. Measuring your data maturity is a way of identifying how set up your organisation is to make the best use of its data.

How often is a data maturity assessment completed? ›

What I recommend will depend on your circumstances, but definitely no more frequently than six-monthly, because in my experience, not enough will have changed to make it worth the effort of doing that - so I would say six-monthly, or maybe yearly.

How does data maturity model reflect value creation for a business? ›

In short, a maturity model tells you where you can improve in a given area to achieve a higher maturity level in your business within that area. It can benefit companies pushing for digital transformations because the models help you identify problem areas to reach your business goals.

What is quality maturity model? ›

Quality management maturity (QMM) is the state attained when drug manufacturers have consistent, reliable, and robust business processes to achieve quality objectives and promote continual improvement.

How many stages are there in IT governance Maturity Model? ›

The five maturity levels define a scale for measuring the maturity of an organisation's software process, and for evaluating and improving the capability of these processes.

What is data strategy? ›

What's a Data Strategy, and how does it support business? - YouTube

What are three major advantages to an organization using the maturity model? ›

Organizations that improve their level of maturity gain many benefits, including but not limited to: Increased customer satisfaction with project outcomes. Higher return on project investments. Improved schedule and budget sustainability.

How might maturity Modelling contribute to quality improvements? ›

You can use a maturity framework to collect data about where your company is on the improvement journey. By finding out where the gaps lie, you can develop a plan that addresses specific issues. After identifying an organisation's progress, then you can come up with solutions to institute the necessary changes.

How many maturity models are there? ›

3 types of maturity models.

What is a mature process? ›

A mature process is one that is complete in its usefulness, automated, reliable in information and continuously improving. Six Sigma, Kaizan, business excellence, total quality management and similar methodologies encourage a quality and continuous process improvement culture.

What is a maturity matrix? ›

A Maturity Matrix is a self- assessment tool to help the organisation understand the extent to which it has developed or implemented, in this instance, big data1 infrastructure and applications.

What is the maturity level of a company? ›

A maturity level consists of related specific and generic practices for a predefined set of process areas that improve the organization's overall performance. The maturity level of an organization provides a way to characterize its performance.

What are the 5 key business characteristics required to help develop BPM maturity? ›

2.2.1 BPM-maturity model

CMM uses five maturity phases to illustrate the degree maturation of the software engineering which is adopted by most BPM authors to create BPM-maturity model. These five phases are: (1) Initial State, (2) Defined, (3) Repeatable, (4) Managed and (5) Optimized (Harmon, 2003).

What are the key process areas of CMM? ›

The key process areas are categorized in Figure 2.4 into three broad categories: Management, Organizational, and Engineering processes.

How many stages are there in IT governance Maturity Model? ›

The five maturity levels define a scale for measuring the maturity of an organisation's software process, and for evaluating and improving the capability of these processes.

What is Gartner analytics maturity model? ›

Gartner ranks data analytics maturity based on a system's ability to not just provide information, but to directly aid in decision-making. More mature analytics systems can allow IT teams to predict the impact of future decisions and arrive at a conclusion for the optimal choice.

What are the five levels of the DPM Performance Maturity Model? ›

To achieve maturity in performance management, organizations need to build capabilities in 5 core elements — referred to as “Operational Levers” — Tools, Processes, Governance, Architecture, and Integration.

What is analytics maturity model? ›

Analytics maturity is a model commonly used to describe how companies, groups, or individuals advanced through stages of data analysis over time.

How many maturity models are there? ›

3 types of maturity models.

How often is a data maturity assessment completed? ›

What I recommend will depend on your circumstances, but definitely no more frequently than six-monthly, because in my experience, not enough will have changed to make it worth the effort of doing that - so I would say six-monthly, or maybe yearly.

What are the three levels of analytics maturity in organizations? ›

An organization's analytics maturity can be described using an analytics maturity model. At AIM we employ a model with three levels of analytics maturity: Descriptive, Predictive and Prescriptive.

How many stages are in Gartner's maturity model? ›

Summary. The five-stage maturity model for manufacturing excellence helps supply chain leaders responsible for manufacturing operations assess their organization's current capabilities, create a plan for change and support the development of a future-state vision for production's role within supply chain.

What are the IBM 5 stages of analytical maturity model? ›

We look at Organization, Infrastructure, Data Management, Analytics, and Governance. Those five categories are used during an assessment to determine a company's level of maturity, each with a set of criteria.

How do Organisations improve maturity levels? ›

Here are my tips for getting there:
  1. Set the right objectives. ...
  2. Understand the objective and value proposition for each project and program. ...
  3. Establish organizational capabilities to manage the work of a project or program. ...
  4. Optimize and manage risk. ...
  5. Prepare the organization to effectively manage change.
2 Jun 2019

How do you calculate maturity of an organization? ›

To establish your level of maturity, it'll require deep-diving into your organization's core customer engagement components: People and Agility, Marketing Insight and Analytics, Readiness and Process, Activation and Execution, and Technology.

What is maturity in relationship to an organization? ›

Organizational maturity is a measure of an organization's readiness and capability expressed through its people, processes, data and technologies and the consistent measurement practices that are in place.

What is data analytics in simple words? ›

Data analytics (DA) is the process of examining data sets in order to find trends and draw conclusions about the information they contain. Increasingly, data analytics is done with the aid of specialized systems and software.

What are the five levels of analytical capability? ›

Key processes for analytics can be divided into five functional areas: analytic modeling, analytic operations, analytic infrastructure, analytic strategy, analytic governance, and analytic security and compliance.

Why should we learn data analytics? ›

Data analytics is significant for top organisations

The outburst of data is transforming businesses. Companies - big or small - are now expecting their business decisions to be based on data-led insight. Data specialists have a tremendous impact on business strategies and marketing tactics.


1. Uncovering Data Governance Maturity Models (webinar) #datagovernance
(Lights OnData)
2. GAMP Data Governance Maturity Model Data Integrity & Records NTZ
(New York Events List)
3. Data Governance Maturity Model
(SlideTeam PPT Designs)
4. How to Measure Your Data Governance Maturity? | Data Governance Maturity Model | OvalEdge
5. Data Governance Maturity Model
6. How to Measure Your Data Governance Maturity
(Lights OnData)
Top Articles
Latest Posts
Article information

Author: Kareem Mueller DO

Last Updated: 06/09/2023

Views: 6202

Rating: 4.6 / 5 (46 voted)

Reviews: 93% of readers found this page helpful

Author information

Name: Kareem Mueller DO

Birthday: 1997-01-04

Address: Apt. 156 12935 Runolfsdottir Mission, Greenfort, MN 74384-6749

Phone: +16704982844747

Job: Corporate Administration Planner

Hobby: Mountain biking, Jewelry making, Stone skipping, Lacemaking, Knife making, Scrapbooking, Letterboxing

Introduction: My name is Kareem Mueller DO, I am a vivacious, super, thoughtful, excited, handsome, beautiful, combative person who loves writing and wants to share my knowledge and understanding with you.