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Operational data governance

What is operational data governance: the current issues, the benefits, what it involves, and where to start?

What is operational data governance?

Operational data governance is closely associated with data activities and needs at the operational levels of an organisation. In the continuum of governance for data, an operational approach complements traditional political data governance.

Political governance

Political data governance focuses on the highest levels of an organisation's data-related governance activities. Typically, it is top-down, hierarchical, and designed to capture the insights of senior staff and communicate the data strategy consistently to all levels of the organisation.

However, this top-down approach can lead to a disconnect with the operational environment of the organisation. This may result in low staff engagement, less input from the front-lines, and unrealistic data strategies that fail to deliver outcomes.

Holistic data governance

When put into practice, operational data governance supports a holistic data governance approach. It supplements traditional approaches and can fill any gaps in organisation’s data governance practice.

This approach ensures that data governance operates and delivers value within and across all levels of an organisation, in a unified way. It can be an effective response for organisations whose data and information management processes fail to reflect modern data contexts.

Operational Data Governance Framework

The operational Data Governance Framework (oDGF) is designed to support successful data asset management, address current governance gaps, and promote mutually supportive data lifecycle management and business process models.

operational Data Governance arises from data stewardship strategies. Each assets within the framework represents an activity and an artifact. All of this centralises on a business decision point and, ultimately, gives rise to benefits for the organisation.

Figure 1. The operational Data Governance Framework

The operational Data Governance Framework supports consistent best practice in areas like data management, and helps organisations realise the value of their data by:

  • monitoring and improving data quality
  • promoting accountability and transparency
  • ensuring that good data practice is built into a data management approach, from the start and ‘by design’
  • supporting agile business models
  • facilitating internal business process monitoring.

Understand the state and flow of data

The framework is made visible through the production and use of data flow maps, specifically through the use of a data flow model called ‘steady states’.

Steady state data flow maps can illustrate:

  • how data and business processes interact and are related to one another
  • ways to improve the use of data from other organisations
  • ways to improve current business processes
  • who interacts with data and how
  • how long data takes to move through its lifecycle
  • the existence of any roadblocks in the data’s lifecycle
  • how data adds value to the organisation.

These data flow maps can be scaled to reflect a specific line of business or the full enterprise, depending on the level of detail required.

In addition to presenting a picture of data flow, steady state data flow maps also provide a way to capture and monitor different characteristics of data, including data quality, at meaningful points along its path. This helps operational staff collect, record, maintain and generally be aware of any changes, including improvements, to the data they manage.

Considered as a whole, the collection of steady state information provides a comprehensive view of the organisation’s data assets. This perspective offers valuable support for strategic thinking or tactical approaches to the use of data.

Data management and business processes

Though closely related, data management and business processes often operate separately from one another. Operational data governance is designed to bring data and business processes together into one approach. It does this using steady state data flow mapping.

Richer view of data assets

Unlike traditional data flow mapping, steady state data flow maps treat data states as business decision points. Using decision criteria, these maps capture the data and the forces acting upon it (i.e., the decisions made by people (or machines), which often involve data, infrastructure, and processes all acting together). This means a much more realistic and accurate view of data assets.

Embedding executive inputs 

At the same time, by editing of decision criteria, high-level inputs like strategy, policy or agreed best practice, and any changes to them, can be easily reflected in the steady states. This means that those inputs quickly become part of business operations, something that traditional top-down data governance approaches struggle to accomplish.

Better transparency

The recording of decision criteria for each steady state in the data flow map also establishes an auditable information set associated with process workflows. This can support increased transparency around the use of data, which is particularly important for government agencies. It can also be used as a way to inform decisions around process improvement, automation and risk identification and mitigation.

Improve capability and culture

In addition to providing increased knowledge about the data itself, operational data governance also provides a way for operational staff to apply a set of core, good practice data governance capabilities in their work.

In doing so, it helps embed data accountability and best practice data management within all data-handling positions. This represents an important step towards an organisation where all staff are operating as data custodians or data stewards.

Increased levels of data accountability can also help establish and support an organisational data culture, while it contributes to increased levels of data maturity. Importantly it does so from the bottom up, becoming part of business as usual, and where such positive changes can be more robust and persistent.

How to get started?

We have included further detailed resources in a section below. If you would like to explore this approach in further detail, we suggest you read these. We also plan to publish some guidance on steady states data flow mapping in the future.

You can also contact Stats NZ to speak to a subject matter expert about the support available.

Additional information

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Content last reviewed 10 May 2022.