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Summary paper: Operational Data Governance Framework

A high-level summary of the framework, providing information on the underlying context and issues addressed, drivers, primary components, and benefits. This summary was written by Kevin Sweeney and published in 2019.

Summary paper: Operational Data Governance Framework [PDF 196 KB]


Data governance approaches and associated frameworks have not evolved to reflect the increasing volume and growing influence of data and information on business practice and strategic planning.

There is a persistent low level of data and information management maturity across all sectors.

Data governance frameworks are typically based on a traditional political governance model which is:

  • decidedly hierarchical, structured (rigid) and prescriptive
  • inclined to embrace an exclusively top-down perspective
  • inexorably linked to the personnel organisational chart and therefore regularly subject to risks associated with restructures (common occurrence)
  • based in rules and compliance, rather than enablement
  • ill-equipped to support agile business operating environments
  • not successful at articulating the governance value proposition for line staff.

Drivers for a new approach

  • Facilitate integrated government
  • Acknowledge a consistent low level of data maturity and culture
  • Leverage and promote the inherently enabling nature of infrastructure
  • Align a data management perspective with the business process model
  • Embed data accountability across the enterprise and establish stewardship as a default capability
  • Sustain a focus on data quality
  • Deliver a practical and highly pragmatic solution

A new operational data governance framework for New Zealand government

The framework employs Enterprise Information Management (EIM) principles including data and information asset management as the operational manifestation of stewardship.

It promotes mutually supportive data lifecycle management and business process model.

  • Re-casting value chain steady states as business decision nexus points
  • Designating data responsibilities and accountability per lines of business
  • Establishing transparent and auditable data management practice within process workflow

It focuses on two aspects of successful asset management, both of which represent current gaps:

1. Cultivation of full-lifecycle and actionable knowledge of data and information assets.

  • Promotion of comprehensive understanding of data assets via data flow
  • Use of steady states data flow mapping model
  • Scaled data flow maps, from line of business to enterprise 

2. Facilitation of improved data and information management behaviours to a best practice standard.

  • Basis in a set of ten (10) foundational data governance capabilities
  • Implementation of human resource core competency framework that incorporates the ten data governance capabilities
  • Embedding data accountability and best practice data management across all data-handling positions, with goal of evolving beyond the need for traditional data governance roles (Data Custodians, Data Stewards)
  • Using resultant staff data accountability to lift organisational data culture and maturity from the bottom up


The framework fills a current operational environment gap, thereby supporting a data governance continuum, extending from the individual, through lines of business, across the organisation, throughout the (regional/national) system, and internationally.

It supports a holistic treatment of data governance across all major levels of the enterprise (executive, management and operational).

It establishes a data governance approach better positioned to support agile business models.

It promotes mindful data management to balance/complement staff bias on process.

It provides a mechanism for managing data and information records in a unified fashion and to a best practice standard.

It integrates data accountability and best practice data management within familiar workflow environments.

It establishes steady state data flow maps, which:

  • provide an important asset-based view of the enterprise in support of improved organisational design and operations
  • represent a mechanism for both extracting data asset information and inserting data management best practice, policy or strategy across the enterprise
  • facilitate measurement and maintenance of data quality throughout enterprise data lifecycles
  • establish process gateways, facilitating big data services and APIs
  • offer an effective means of communicating/negotiating improved administrative data sourcing from suppliers
  • allow for multi-scalar views of business unit data flows, adding new levels of insight for management across all levels of the org chart

Finally, the framework engenders higher levels of assurance and customer/constituent trust, which supports social license.

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Content last reviewed 02 July 2021.