dataflux data governance model

This article is covering DataFlux data governance maturity model and continuing the series of data governance maturity models as a data governance evaluation tool. Check out these other ones:

DataFlux Maturity Model

Overview: Developed by DataFlux in 2007, it was based on their ten years of experience in developing the core components of data governance technology. First presented in their white paper on The Data Governance Maturity Model: Establishing the People, Policies and Technology That Manage Enterprise Data“. It had since been revised and updated to include the business perspective that drives the need for managing data as an asset, besides the technology adoption at each phase.

The model has 4 levels of maturity with the following characteristics:

dataflux data governance model

1.       Undisciplined

  • Little or no rules and policies on data quality and integration
  • Redundant data across multiple different data sources, format and records meeting similar purposes
  • High risk of lost opportunities and incorrect decisions due to poor data quality

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.

2.       Reactive

  • Data rules and policies are created at department level
  • Data quality is also mostly addressed at department level
  • Still a lot of poor data quality enterprise-wide

3.       Proactive

  • The value of a centralized view of information and knowledge is understood at the enterprise level
  • A data culture is beginning to be adopted across departments

4.       Governed

  • Data and information is unified
  • The data strategy and framework is well established and understood
  • Everyone understands information is a key enterprise asset

Each one of these phases is evaluated against four major dimensions:

  1. People
  2. Policies
  3. Technology
  4. Risk

The model offers their characteristics at each stage and proposes what needs to be addressed to advanced to the next. Ex:

DataFlux DG model phase 1

Take away: Higher levels of maturity yields greater information and knowledge rewards and reductions in risks. The “Reactive” level is where a data governance program is put together. Moving out of the “Reactive” into the “Proactive” one is a difficult step to take, usually taking 4-5 years.

More information: 

Next I’ll go over Gartner’s EIM 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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