Gartner data governance maturity model

I’ve received a lot of positive feedback on the data governance maturity models series as it provides the high level overview of each model to guide you into adopting one for your program. In this article I’ll go over the Gartner  data governance maturity model.

If you’d like to go over the ones covered so far, please go ahead:

Gartner Data Governance Maturity Model

Overview: First introduced in December 2008, the maturity model looks at enterprise information management (EIM) as a whole. The model has 6 phases of maturity, each with its own characteristics and action items, which will be covered below. It is important though, to also look at their concept of the EIM discipline and its five major goals:


Note: Gartner has since updated the model and released a new one in 2016.

You can learn about the updated model, other data governance models, and how to best use them while avoiding their pitfalls, in this Data Governance Maturity Model Online Course. Enroll if you want to upgrade your skills in the data governance profession or start or evolve your data governance program.


  1. Data integration across the IT portfolio
  2. Unified content
  3. Integrated master data domains
  4. Seamless information flows
  5. Meta data management and semantic reconciliation

Now, let’s go over each of the 6 phases.Gartner DG model

1. Unaware (level 0)

  • Information/ data governance, security, ownership or accountability does not exist
  • No formal information architecture, principle, process for creating, gathering, sharing and disseminating information
  • There are no common standards, business glossaries, no metadata management, no data models
  • Document management, workflow and archiving mostly occurs via e-mail
  • Information is fragmented and inconsistent across different systems and applications
  • Strategic decisions not made based on adequate information

Action items: Architecture staff and strategic planners should educate IT and business leaders on EIM and its potential value. Emphasize the risks of legal and compliance issues.


2.  Aware (level 1)

  • Lack of data ownership becomes apparent
  • Lack of business sponsorship in EIM is acknowledged
  • The business starts to understand the value of information
  • There is awareness in growing data quality issues and inconsistent information
  • The need for common standards, principles, processes, procedures, as well as tools and models is recognized
  • Business Intelligence outputs inconsistent and redundant reports – Check out this free guide on creating a report inventory
  • Inventory and evaluation of risks associated with not having an EIM

Action items: Architecture staff develops EIM strategy in alignment with enterprise architecture and business’ strategic intent


3.  Reactive (level 2)

  • Business now understands the value of information
  • Information is shared on cross-functional projects
  • Data and information is starting to be shared across systems with different ownership and across departments
  • Information quality procedures are still reactive
  • Information management policies and standards are created, but adherence is low
  • An baseline assessment is developed and metrics are gathered, mainly focused on data and information retention

Action items: Upper management to promote EIM as the solution for resolving cross-functional information issues. The value proposition for EIM is put together and presented.


Checkout this online course on Data Governance Maturity Model(s) to learn all there is to know about them, the best practices and pitfalls on selecting and using them, and so much more.


4. Proactive (level 3)

  • Information management is considered necessary to support decisions, and information owners and stewards are assigned to manage this asset
  • Information sharing is viewed as the key to enabling enterprise-wide projects
  • Governance roles and operating model becomes formalized
  • Full compliance with information management policies and standards
  • Data governance is part of every development and deployment project
  • Operational risk is minimized

Action items: Develop and present the EIM business case to management and stakeholders. Identify EIM opportunities at department or unit level.


5. Managed (level 4)

  • Information is viewed as being critical
  • Information policies and standards are developed, deployed and well understood throughout the enterprise
  • A governance body is placed to resolve cross-functional information issues and identify best practices
  • Metrics are refined, information assets are categorized, productivity metrics are developed and shared through dashboards

Action items: Information management tasks and projects need to be inventoried and ensure they are in sync with the EIM strategy. Create a balanced scorecard for information management.


6. Effective (level 5)

  • Information management is seen as a competitive advantage and it is used to create value and efficiencies
  • Service level agreements are in place
  • EIM strategies are tied to lowering risks and meeting/improving productivity targets
  • The EIM organization is well formalized and coordinates all information efforts across the enterprise
  • The organization achieved its EIM goals

Action items: Implement controls and procedures to ensure information excellence is sustained regardless if the leadership or direction of the enterprise changes


Take away: Their model makes a point that EIM is not a single project, but a program that involves over time. This message is something that leadership needs to understand and buy into from the start in order to secure continuous support and resources. A phase or their associated activities cannot and should not be skipped as this will cause EIM failure and higher risks/ costs later on. As you might expect, most enterprises are in the early stages of EIM maturity.

 

More information: 

Next I’ll go over Oracle Data Governance Maturity Model.

{"email":"Email address invalid","url":"Website address invalid","required":"Required field missing"}

About the author 

George Firican

George Firican is the Director of Data Governance and Business Intelligence at the University of British Columbia, which is ranked among the top 20 public universities in the world. His passion for data led him towards award-winning program implementations in the data governance, data quality, and business intelligence fields. Due to his desire for continuous improvement and knowledge sharing, he founded LightsOnData, a website which offers free templates, definitions, best practices, articles and other useful resources to help with data governance and data management questions and challenges. He also has over twelve years of project management and business/technical analysis experience in the higher education, fundraising, software and web development, and e-commerce industries.

You may also like:

George Firican

07/15/2020

TDWI data governance maturity model

>