kalido maturity model

So far I’ve provided an overview on 6 different data governance maturity models which I will post links below in case you’d like to learn more about them. Maturity models can provide a road map to follow in order to advance from one maturity stage/ level to another. Today I’ll provide an introduction into the Kalido data governance maturity model, but here are the other ones covered so far:

Kalido Data Governance Maturity Model

Overview: Published in September 2010, the Kalido data governance maturity model is based on Magnitude’s own market research with more than 40 companies at varying stages of maturity. Similarly to DataFlux, it has 4 stages, which map to the evolution of how organizations treat data assets. The model also offers a free online self-assessment tool which I will link to at the end of this article.

Here are the stages with their characteristics, outlined across 3 areas:

  1. Organization
  2. Process
  3. Technology
kalido maturity model
Kalido Maturity Model Stages

Level 1 – Application-Centric

At this stage, some organizations attempt to govern data through enterprise data modeling, which is mostly an academic exercise. Efforts are mostly driven by IT without the broad organizational support and authority to enforce compliance.


  • Authority and data stewardship do not exist
  • There is little or no collaboration between IT and the business
  • Business views data as IT’s responsibility


  • No processes in place for data governance


  • Models of data and business processes as well as rules are entirely embedded in applications
  • There are no tools for modeling, managing, and ensuring data quality
  • There are no repositories that capture enterprise-wide, cross-functional views of data

Level 2 – Enterprise Repository-Centric

Data governance is typically siloed around individual enterprise repositories, such as a data warehouse or an Enterprise Resource Planning (ERP) system. Also, governance is informal, lacking a distinct organizational structure and clearly defined and executed processes.


  • Some authority for data exists in IT
  • No official recognition of data stewardship, nor defined roles and responsibilities
  • Inconsistent collaboration between IT and business when it comes to data


  • Loosely defined processes exist around enterprise repositories (ex: a data warehouse, master data hub, and large operational systems)
  • Data issues are tackled reactively without addressing the root cause
  • No institutionalized process for making enterprise-wide, business centric decisions for data


  • Data warehouses and/or master data hubs exist
  • Investments in data quality and metadata tools are made around these systems
  • Managing data across multiple systems follow a bottom-up approach with limited influence

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.

Level 3 – Policy-Centric

Rather than envisioning ever-larger and more encompassing repositories, organizations put processes in place for defining, implementing and enforcing policies for data. It is acceptable for the same type of data to be stored in multiple places as long as they adhere to the same set of policies. Enterprise repositories continue to be important, but they’re built on governed platforms integrated with enterprise data policies.

Business takes increasing responsibility for data content, and data is widely recognized as one of the most valuable corporate assets throughout the organization


  • A cross-functional data governance council is formed
  • Data stewards have defined roles and responsibilities and are explicitly appointed
  • Business is engaged in managing data
  • Data is seen more and more as an asset


  • Processes for policy definition, communication and enforcement are implemented
  • A clear process for reporting and tracking data issues is established – Check out our free data quality issues log template
  • Key enterprise data repositories are governed by a single, streamlined set of governance processes


  • A centralized repository of data policies exists and sets policies in a top-down fashion
  • The process of data governance is supported using an automated workflow
  • Data quality is regularly monitored and measured

Level 4 – Fully Governed

Over time, the scope of the data governance program will increase to cover all major areas of competence: model, quality, security and lifecycle. Clearly defined and enforced policies will cover all high-value data assets, the business processes that produce and consume them and systems that store and manipulate them.
There is a strong culture that values data as a strategic asset. Like human resource management, a distinct data organization with institutionalized governance processes becomes a permanent business function.


  • The data governance organizational structure is institutionalized 
  • Data governance  is seen as business critical and has the same level of importance such as HR and Finance
  • Business takes full ownership for the data and data policy making


  • Data governance is a core business process and always taken into account
  • Decisions are made with quantifiable benefit/cost/risk analysis


  • Business policies for data model, data quality, security, lifecycle management are integrated with user interactions with data
  • Centrally defined policies and rules drive behavior of systems where possible
  • Data are monitored and issues are addressed proactively


Take away:  This model argues that the organization, process and technology need to advance in lock-step for data governance to be successful. They offer an online self-assessment tool, listed below, but be aware that the results may not all fall in a single stage.

More information:


Next I’ll go over the TDWI Data Governance Maturity Model.

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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.

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