Data Strategy

An agency has a clear vision and plan for using data to achieve objectives

What is a Data Strategy?

A Data Strategy is a detailed plan for an agency’s data and how it can be best used to meet the agency’s broader business objectives and priorities.

It can provide an opportunity to refresh current practices around data collection and use, and to implement new technologies and innovations to get the best use out of an agency’s data. It identifies a target state for agency data use and a pathway to get there.

A well-rounded Data Strategy should consider all data processes, what is required at each step of those processes and where improvements need to be made and efforts focused. It may also consider other measures, such as skills and capability building, and cultural change activities.

Ideally, all agency data would be within scope of a Data Strategy. However, due to resource constraints, an agency may choose to initially limit the data in scope of the strategy. For example, an organisation may choose to focus on its program data, but not its corporate data (e.g. Human Resources data).

The scope of the Data Strategy can be expanded in a second or third-phase Data Strategy as an agency’s practices mature.

What is the difference between a Data Strategy and an Information Strategy?

A Data Strategy is closely related to an Information Strategy but narrower in remit. An Information Strategy ‘describes your agency’s planned approach to information management to meet current and future organisational needs and regulatory requirements’1. It includes all of an agency’s information, including data and all files and records.

Data is just one of many different types of information an agency will hold. A Data Strategy only focuses on data and associated practices, but the two strategies should align. Some agencies may choose to have a single Information Strategy incorporating information and data, others may choose to have separate but related strategies.

1 developing-information-management-strategy

Putting Data Strategy into practice – Develop and implement a Data Strategy

There are some key considerations when developing a Data Strategy:

Value proposition

Before you embark on a Data Strategy you should understand the value proposition: ‘Why are we doing this?’ and ‘What’s in it for me?’. For some areas of an agency, the need for and benefits of a Data Strategy are obvious. However, this is not the case for everyone, particularly when a Data Strategy may divert resources from one stream of work to another. This is where a value proposition can be beneficial. It will clearly articulate what the benefits of the Data Strategy are for all parts of the agency.

The different perspectives, values, concerns and priorities of stakeholders will need to be considered to develop value propositions. Spending time defining what value looks like for stakeholders and tailoring messages is important to gain buy-in.

State analysis

To inform your Data Strategy you need to have some understanding of what your agency’s data capability is; your agency’s current state.

Once you know your current state you can identify what you want your future state to look like and what pathway can get you there.

Agencies may use a formal maturity model to conduct their state analysis (see ‘Where can I find more information?’ for details) or they may use some other method, such as workshops or focus groups. A risk analysis may inform your state analysis.

A data audit or inventory may also help shape the Data Strategy as it allows agencies to determine the extent of their data holdings and how they are stored, managed and used.

The following article outlines the components of a number of data maturity models:

Engagement, consultation and communication

A Data Strategy aims to drive transformational change across an agency. Engagement and consultation will be essential to develop buy-in from staff and drive change through the strategy.

A stakeholder analysis can be undertaken to determine who will be involved and impacted by the Data Strategy, and the level of influence each stakeholder has. Other elements to consider are the frequency with which you engage, using a co-design approach, ensuring you engage across all staffing levels, using a variety of engagement channels to communicate progress, and demonstrating you have heard the input from staff including how it has shaped the strategy.


There is no right ‘size’ for a Data Strategy. It needs to be as long or as short as necessary to achieve desired outcomes. An agency can focus on small improvements, or big transformation. The scale of the Data Strategy will depend on resources, culture, appetite and what is realistically achievable for an agency.


There needs to be a balance between choosing a timeframe that is too short to achieve meaningful change, or one that is too long to respond to the changing data landscape. Typically, an agency’s Data Strategy would span three years, with regular ‘check-ups’ or progress reports to assess how the Data Strategy is performing.


The contents of a Data Strategy can vary, but some useful elements to include are:

  • Vision statement
  • Alignment with Government data policy
  • Alignment with internal policies / strategies
  • Strategic objectives
  • Initiatives/areas of focus
  • Measurable outcomes/indicators of success
  • Roadmap

Vision statement

This outlines where an agency desires to be and what the Data Strategy should ultimately achieve. This should be short, catchy and future-focused. If an agency already has a strong vision statement, it may be helpful to relate the Data Strategy to this.

Examples of Data Strategy vision statements include:

  • Data is F.A.I.R (findable, accessible, interoperable, and reusable)
  • Service delivery is enhanced through maximising the value of our data
  • Data drives decision making to achieve policy outcomes
  • Public trust is enhanced through transparent collection and use of data

Strategic alignment with Government data policy

There are many initiatives within Government that may help to shape a Data Strategy. As government agencies, it is important that any activities related to the use of data reflect the direction of current Government commitments. A Data Strategy should be compliant with mandatory government legislation and policies, and align to the greatest extent possible to non-mandatory policies.

Relevant Government data policies include:

Internal policies/strategies

A Data Strategy may struggle to get traction if developed in isolation. It should complement and leverage existing internal strategies, policies and roadmaps, such as the Corporate Plan. This is particularly relevant in the area of ICT. The Data Strategy should consider the technical requirements needed to support its implementation such as data warehousing capability. These requirements will need to align with your agency’s ICT or enterprise architecture directions.

Examples of relevant internal policies include an agency’s:

Strategic objectives

Strategic objectives define what you want to get out of the Data Strategy. They should be supported by explanatory text so it is clear to all staff members what they mean in practice. The strategic objectives should align with the Vision Statement and also reflect the outcomes of the State Analysis.

Examples of strategic objectives include:

  • Build first-class analytics capability
  • Manage and use data to improve client outcomes
  • Create a data culture
  • Future-proof data infrastructure
  • Break down data silos
  • Ensure transparency in data collection, access, and use
  • Manage privacy proactively using a privacy-by-design approach

Initiatives/Areas of Focus

These are the heart of the Data Strategy. The Initiatives or Areas of Focus identify what an agency is going to do as part of its Data Strategy.

There are many different areas an agency can focus on as part of the Data Strategy. The particular areas of focus will depend on the outcomes of the state analysis, the scale of data activities within an agency and the resources available.

Examples of Initiatives/Areas of Focus include:

  • Improving data governance
  • Establishing career pathways for data scientists
  • Building skills and capabilities
  • Ensuring consistent data management across the data lifecycle
  • Supporting IT (infrastructure, architecture)
  • Remediating legacy data
  • Facilitating data migration
  • Improving process management and transformation
  • Developing and implementing standards and metadata
  • Improving the culture and environment around data management
  • Ensuring appropriate data security
  • Establishing privacy governance to guide management of personal information

Each Initiative or Area of Focus should be associated with one or more of the Strategic Objectives. If an initiative cannot be aligned to a Strategic Objective, then it is likely not a priority.

It is important to identify who within the agency is responsible and accountable for each of the initiatives. It may be a position within a team or it may involve collaboration between different areas. To ensure accountability and responsibility, ownership of initiatives must be clearly indicated so all staff know who is responsible and accountable for each initiative and it can be factored into business planning.

Example 1:

Vision Statement: Data is F.A.I.R (findable, accessible, interoperable, and reusable).

Strategic Objective 1: Ensure transparency in data collection, access, and use.

Initiatives to meet Strategic Objective 1:

  • Increase data management skills across the data lifecycle
    • Responsibility: Learning and Development team
  • Release data as open where appropriate
    • Responsibility: IT, Data teams, Publishing team
  • Use data platforms to increase data accessibility
    • Responsibility: Data team, IT, Web Services team
  • Ensure all data is catalogued to increase discoverability and promote reuse
    • Responsibility: Publishing team, Cataloging team, IT, Data teams

Example 2:

Vision Statement: Data drives decision making to achieve policy outcomes.

Strategic Objective: Build first class data analytics capability.


  • Establish career pathways for data scientists
    • Responsibility: Learning and Development Team, Senior Management, Supervisors
  • Establish a Community of Practice for analytics
    • Responsibility: Data teams, Agency Data Champions
  • Sponsor data ‘hack-a-thons’ involving analysts and policy officers
    • Responsibility: Senior Management, Agency Data Champions

Measureable outcomes/Indicators of success2

A good Data Strategy should include ways to measure success or progress. These performance indicators are useful evidence for management to ensure ongoing support, as well as identifying where changes or resources are needed.

The measures should directly reflect the initiatives within the strategy. Measures can be qualitative or quantitative, noting that data management lends itself well to quantitative measures. Quantitative indicators are also easier to measure and compare across the lifespan of the Data Strategy. They can also be persuasive for senior management.

Regular ‘check-ups’ or review points should be incorporated into the roadmap so progress can be tracked and adjustments made if objectives are not being met.

External stakeholders and data users may also be considered when developing success indicators.



  • 50% of data remediated
  • 65% of staff completed data management training
  • 95% data catalogued internally


  • Staff understand the value of data to the agency
  • Staff are confident in their ability to access data required for their job
  • Agency is referred to as a ‘data leader’ by stakeholders


A Roadmap is a complementary product to the Data Strategy. The Roadmap details the various initiatives and the timelines for completion. This allows an agency to ‘map out’ the flow of activities and ensure they occur Office of the National Data Commissioner 13 in a systematic order, particularly if activities are dependent on others. It is useful to identify those responsible and accountable for each initiative on the Roadmap and the relevant timelines, so people can see visually who is responsible and accountable for what and when.

The Roadmap provides an overarching structure to tasks and will form a core component of work programs moving forward. How each individual task is completed is up to the individuals and areas responsible for that initiative.

2 There are some established frameworks for developing measures of success that agencies can use, such as OKR (Objectives and Key Results), KPI (Key Performance Indicators) or SMART (Specific, Measureable, Achievable, Relevant, Time-bound) goals.

Endorsement and governance

To influence cultural change, the Data Strategy must be supported by the agency executive. This can be done through a number of methods, such as clearance and endorsement processes (e.g. Executive Committees), but also through forewords/introductions, branding and launches, or a combination of these.

Governance and oversight of the Data Strategy is important to ensure progress and accountability. Agencies may wish to set up a new governance structure specifically for the Data Strategy, or may use existing governance structures.


There are a number of factors that are critical to successful implementation of a Data Strategy:

  • Ensure the Data Strategy is aligned with the agency’s mandate, vision and/or business objectives. This will ensure the Data Strategy remains relevant and change will occur in conjunction with other agency strategies/directions.
  • Have a champion, or several champions, within an agency who can bring everyone along on the journey and promote the Data Strategy. If your agency has a CDO and/or Data Champion then they may be the best placed person to lead this work.
  • Aim high, but be realistic about what can be achieved. Some strategic objectives and initiatives may be suited to future versions of a Data Strategy.
  • Change and improvements take time. Don’t be disheartened if it takes some time for initiatives to gain traction within your agency.
  • Showcase successes. Focusing on successes, even small ones, provides evidence of progress. This is a compliment to those who have made the changes and also helps bring ‘nay-sayers’ along on the journey as they can see benefits of the Strategy.
  • Review the Data Strategy periodically and don’t be afraid to adjust initiatives if they aren’t working. A Data Strategy is a medium-to-long term plan and it’s normal for plans to require adjustments as time passes and new information is discovered.
  • Test the environment. Large agencies, in particular, may have differing levels of data usage and maturity, dependent on roles, business processes and systems that govern different work areas.
  • Data Strategies will need to iterate over time and agencies should consider transitioning to a new Strategy as practices improve.

Finally, it is useful to share and compare Data Strategies with other agencies. The challenges faced by one agency are likely faced by others. When beginning work on a Data Strategy it can be helpful to approach other agencies for a copy of their strategy. To help foster transparency and trust in government, agencies may also consider making their Data Strategy, or portions of it, publically accessible as appropriate. There are a number of Australian Government agencies and international agencies, which have already established and published a Data Strategy:

Where can I find more information?

There are interest groups across the APS that can provide useful advice:

  • Data Champions Network, facilitated by Department of the Prime Minister and Cabinet.
  • Data Governance Special Interest Group, facilitated by National Archives of Australia.
  • The Australian Government Linked Data Working Group (AGLDWG) is a community of Commonwealth Government experts and champions, with invited non-voting participation of individuals, corporations and other entities.
  • There is an Open Data Tool Kit on, which provides information about publishing open data, including guidelines for an Open Data Strategy.
  • Queensland Government has provided information on their audit of information management maturity. This is one example of a maturity assessment in practice.

The National Archives of Australia runs the Check-Up Plus for agencies every year as part of annual reporting requirements. Agencies could use the results of this survey to inform improvements within their Data Strategy.

Questions to ask:

  • Who should lead or create the Data Strategy?
  • Who needs to be convinced of, and promote, the benefits of our Data Strategy?
  • Who needs to be involved in developing our Data Strategy?
  • What is an appropriate and achievable vision for data in our agency?
  • How does our Data Strategy align with government data policy?
  • How does our Data Strategy align with internal policies / strategies?
  • What is the current state of agency data maturity and capability? How will this assessment be undertaken?
  • What are the overarching strategic objectives related to our agency that will guide the strategy?
  • What initiatives or focus areas are most relevant and achievable for our agency?
  • Do the vision statement, strategic objectives and initiatives or focus areas align well with each other?
  • Have measures and indicators of success or progress been defined in a meaningful way?
  • Who needs to endorse our Data Strategy for it to take effect?
  • Does a roadmap exist to guide the implementation of the Data Strategy?
  • What activities in the strategy will support cultural change within the agency?
  • How will we promote the Data Strategy and ensure continued engagement?