Chapter Five: Data and evidence


How does data and analysis support place-based approaches?

Data and analysis are critical throughout place-based approaches to enable partners to:

  • see opportunities or challenges unique to that community
  • identify and map potential solutions
  • demonstrate the impact on the local community as part of the Monitoring, Evaluation and Learning (MEL) framework.

If you are working with a place-based initiative, it’s important to have a high-level understanding of how data can support an initiative at every stage – from gathering data, to interpreting and presenting data to stakeholders.

What is meant by data, analysis and other key terms?

The key data and analysis terms below provide definitions of key terms used when talking about data and evidence. These definitions are critical to understanding what can be shared outside government, and the role government plays to help community partners access, analyse or interpret information as part of a place-based approach.

Key data and analysis terms


The facts and statistics captured according to agreed standards for reference or analysis:

  • qualitative data – non-numerical data that can be observed and recorded, such as interviews or case studies about the local community’s experience. This can include Indigenous knowledge systems, experiential knowledge and expertise (lived experience), and practice-based knowledge.
  • quantitative data – expresses a certain quantity, amount or range, such as the number of presentations to a local hospital’s emergency department.

Brings order and structure to data by cleaning, transforming, manipulating, summarising, and reducing it to an interpretable form.


When data is processed, organised, structured or presented in a particular context to make it useful.


Meaning is given to data by drawing statistical conclusions, building relationships and implications, and making decisions.


A body of facts and information that show whether a hypothesis is true or not true.


What is gained by analysing data in an organised way, to understand the context of a particular situation, draw conclusions and find opportunities for improvement.


When the data collection, analysis, information or interpretation is not reflective of the real world.

What can data tell us?

Data and information can support place-based approaches in many ways. For example, helping to identify and prioritise opportunities or challenges in an area and to track progress towards identified outcomes and impacts achieved.

The four major categories of data analytics and how they relate to place-based approaches are shown below. The type of analytics you use will depend on the question you need to answer.

Categories of data analytics and how they relate to place-based approaches


Asks: What is happening?

Example in a place-based approach: Real-time data showing the rate of people experiencing disadvantage in a specific community.


Asks: Why is it happening?

Example in a place-based approach: Exploring the unique cause(s) of disadvantage in community.


Asks: What might happen in the future, based on analysis of previous trends?

Example in a place-based approach: Forecasting future rates of disadvantage in that community, with or without certain responses or supports.


Asks: How can we make it happen?

Example in a place-based approach: Exploring different responses or supports to identify the most effective or appropriate for this unique community.

Data throughout the lifecycle of a place-based initiative

Data and evidence can help you measure progress, support continuous improvement and evaluate an initiative. It is important to be aware that data and information will be used differently at different stages to continuously enhance the relevance and success of the place-based initiative (Australian Institute of Family Studies, 2015).

The section below shows how data and analysis can be used at each stage of developing and embedding a place-based approach.

Opportunities to use data and analysis in a place-based approach

Stage 1. Identify if a place-based approach is beneficial

How data and analysis can be used:

  • build consensus
  • understand local challenges and strengths
  • measure the current state

Stage 2. Assess Readiness

How data and analysis can be used:

  • build consensus
  • understand local challenges and strengths
  • measure the current state

Stage 3. Develop a shared vision and plan for change

How data and analysis can be used:

  • define scope
  • match resources to needs
  • forecast key dates and milestones
  • define and measure success
  • develop benchmarks

Stage 4. Implement together

How data and analysis can be used:

  • track output or outcomes
  • report against key performance indicators
  • build and enhance forecasts
  • identify potential issues or challenges

Stage 5. Embed a culture of learning and continual improvement

How data and analysis can be used:

  • identify opportunities for improvement vs cost and impact of changes
  • measure benefits of improvements
  • generate connection, shared understanding, and innovation across place
  • analyse impact/outcome as part of evaluation and review
  • map investment to impact
  • reflect on lessons learned

Stage 6. Celebrate and communicate success

How data and analysis can be used:

  • present data to demonstrate progress and impact

Key principles of data collection and analysis

Data sources

Data and information can be used to provide a picture of a community’s experience, how a place-based initiative is tracking against agreed outcomes, and to inform decisions around next steps. To best achieve this, use a range of data sources, including quantitative and qualitative.

The types of data used to effectively track progress with place-based approaches include Indigenous knowledge systems, experiential knowledge and expertise (lived experience), practice-based evidence and qualitative research.

Data quality

Data needs to be a sufficient quality to support evidence-based planning, decision-making and review. The information you collect should be authentic and relevant to place or practice, gathered with care and respect for the source (in line with privacy requirements), able to be understood and described, and have sufficient breadth and/or depth.


Data needs and objectives should be planed early, such as when you design the MEL framework. This will help ensure that the data is valuable to the work. When thinking about the MEL framework and the kind of data needed, consider the following:

  • What data or information will you need, and when, to monitor the initiative?
  • What impact are you aiming for and how will it be demonstrated?
  • What is already known about the target community or cohort, and how can you develop an accurate understanding of the current state?
  • How can problems in the initiative be detected after it has commenced?

Refer to Chapter Four: Monitoring, Evaluation and learning to learn more about planning a MEL framework.


Use a wide range of data sources to capture a sufficiently diverse range of experiences and avoid data bias. This is different to the question of quality: a high-quality data set may not sufficiently capture the full range of perspectives you need for your work.

Remember that many factors combine to form an individual’s identity and experience, and intersectionality should be considered in data collection, analysis and use.

Ensure your datasets and analysis report a high-level perspective, as well as the impact of key priority groups within the community, for example, LGBTIQ+, CALD, people living with disability and young people. Experiences within small subsets of the community may significantly differ from the average or majority experience. For example, population-level statistics may not capture:

  • cyclical variations such as seasonal agriculture workers
  • impacts of unusual situations such as pandemics
  • unique experiences, such as the multitude of reasons a student may disengage from education.

Tips to incorporate diversity into your data collection and analysis:

  • use a variety of data sources, including government-owned data and community- owned data
  • ensure your data adequately reflects the make-up of the community and can offer deeper analysis than just population-level outcomes
  • utilise data from many different departments and levels of government, and from local organisations and partners
  • look for case studies and quotes from community members in addition to data, as well as capture stories of impact at the local level. These can add a powerful new dimension to your analysis, help unpack the impact of certain outcomes, and capture new perspectives
  • use a combination of traditional data sources and stories of impact to build a more complete picture.

First Nations Data Sovereignty

When working with First Nations communities, it is critical to understand what data sovereignty means to the First Nations communities you are working with and how to operationalise it.

Principles for Aboriginal data sovereignty are outlined in the OCCAAARS Framework:

  • ownership
  • control
  • custodianship
  • access
  • accountable to First Nations
  • amplify community voice
  • relevant and reciprocal
  • sustainably self-defining.

The MEL toolkit provides further guidance regarding First Nations Data Sovereignty, and includes has case study where the OCCAARS Framework has been applied to data governance in a process involving government stakeholders.

Collaborative sense-making

Place-based initiatives provide an opportunity for ‘collaborative sense-making’. You may already use a collaborative approach to gather data, and broaden how it is conceived and used. For example, valuing community collected data and information that reflects the needs and opportunities within a local community.

Involve the community in the analysis and interpretation of this data to help ensure their unique experiences directly inform decisions and actions.

Collaborative work with communities should involve outreach with local community members and groups, local government, service agencies and Aboriginal agencies. This can provide richer detail and complement existing datasets by providing greater context.


Be clear about the purpose and value-add of data before you gather, analyse and disseminate it. Your MEL framework will help you plan your data needs and objectives.

The Victorian Government has published guidance specifically in relation to the Victorian Family Violence Data Collection Framework.


Data sets can contain private or sensitive information about people’s health, lives and experiences. Read the ‘Sharing data’ section in this chapter for more detail about what to consider.

The Office of the Victorian Information Commissioner has published guidance about information privacy and sharing. You may wish to consult with your department’s internal ethics and review boards (or similar function) to ensure that you are abiding by expected standards.

Publicly available data

DataVic contains a range of existing, publicly available Victorian Government data that may help inform the place-based approach. There are additional tools and resources at the end of this chapter with other sources of information.

Refer to Chapter Two: Working with local communities and government agencies and Chapter Three: Working with diverse communities to learn more about stakeholder collaboration.

Sharing data

Ensuring safe and secure data sharing to support community outcomes

The Victorian Government has made a commitment to increase public access to government data and ensure it is easy to find and use, through DataVic. It states:

“The Victorian government recognises the benefits associated with mandating a whole of government approach to the availability of Victorian government data for the public good.

The DataVic Access Policy provides direction on the release, licensing and management of Victorian Government data so that it can be used and reused by the community and businesses.

The purpose of the DataVic Access Policy is:

  • to enable public access to government data to support research and education, promote innovation, support improvements in productivity and stimulate growth in the Victorian economy
  • to enhance sharing of, and access to, information-rich resources to support evidence-based decision making in the public sector”

The full policy and supporting guidance help clarify which data sets are to be made available and which must not.

The Victorian Government is committed to embedding the effective use of data and information in place. Recent whole-of- government initiatives and work underway through the Victorian Centre for Data Insights, the Victorian Public Sector Data Sharing Framework, Victorian Data Sharing Act 2017 and the Centre for Victorian Data Linkage, are demonstrating how data can be used for localised policy planning and service design.

This relies on departments and agencies taking steps to comply with the DataVic Access Policy and making data available. However, the process for releasing data may differ depending on the proposed use for the data. Restrictions that may apply to making datasets available include what the end-product or result may look like, the sensitivity of the data, or the data management standards of the organisation(s) set to access that data. This is discussed further below.

You should carefully consider data sharing legislation guidelines and frameworks to make sure data can be safely shared and consult with your department’s data custodian or business area.

What do I need to do before I share any data?

You should develop an information (or data) sharing agreement before developing any interfaces or providing data securely by electronic means. The agreement should stipulate what data can be shared for what purposes and under what conditions. It should be approved by the custodians of the data being shared. If data you plan to share includes personal or sensitive information, the agreement should clearly specify the permitted purpose for sharing the information, and how this complies with privacy legislation, as well as any restrictions on use or access including any on-sharing permissions or restrictions.

Where can I go for more information?

The Victorian Public Sector Data Sharing Framework provides guidance for sharing data across the Victorian public sector, as well as a Heads of Agreement for Victorian agencies to share data.

The Office of the Victorian Information Commissioner (OVIC) also provides a helpful high-level overview of information sharing including potential risks and why protecting data is so important. The links to three of these guides are include below:

Relevant Victorian and Commonwealth legislation, regulations and guidance are also included in the ‘Additional resources’ section of this chapter.

Presenting and interpreting data

The way you present and interpret data helps others to see the conclusions you are trying to draw based on your analysis. Always present data with the appropriate context and supporting information so it is not open to misinterpretation.

Geospatial mapping

Geospatial mapping means pulling together a variety of datasets about a place to solve a problem or identify solutions. Assembling and looking at data from a place-based perspective can provide a more comprehensive picture of place and be particularly useful to identify and pinpoint areas of need and potential solutions.

A geographic information system (GIS) can be used to plot data on a map, visually integrating locational data (where things are) with all types of descriptive information (what patterns, services, people or other characteristics are there). Geospatial mapping can help to understand relationships and context, or interpret the interaction between space and place.

For example, geospatial mapping could help you see the range of services available in the local area compared to the level of service usage, particular demographics and the rate of access to adequate transport.

Your department may have a spatial infrastructure system including web and desktop mapping applications together with spatial databases and services that can integrate location into business applications or workflows.

Visualising data

Data visualisation can be an effective way to show complex information in a simple, digestible way. It can help you tell the ‘story’ of a place- based initiative, including why it exists, and what it is aiming to achieve.

Visualisation can include:

  • graphs
  • flow charts
  • Venn diagrams
  • infographics.

Best practice principles for data visualisation include:

  • Quality – base visualisations on high-quality data and capture a diverse range of datasets to present an accurate picture of the whole.
  • Accuracy – provide an interpretation that is accurate within the context of the whole dataset – that is, avoiding any bias.
  • Match visual to data type – choose the right chart or visual to ensure your interpretations are clear to the audience. This guide by TowardsDataScience provides further advice.
  • Description – Where possible, provide a written interpretation or description of the visual, so the audience cane make sure they understand the graphic correctly.
  • Clarity – Ensure your visual provides an unambiguous interpretation, meaning different readers would draw the same conclusion based on your graphic. This information by HubSpot on data visualisation may help.
  • Consistency – Use the same set of visual rules to compare data. This allows the audience to read the entire set of visuals as a story, and easily understand and draw conclusions.
  • Accessibility – Design with equality of access in mind to ensure a diverse range of users can easily, confidently and accurately interpret your content. This is about inclusivity, not disability. Any presentation or visualisation of data must meet accessibility standards, and Victorian Government legislation requires all internal and external documents and services to comply with the WCAG 2.1 Accessibility standards and the Australian Human Rights Commission’s Disability Discrimination Act 1992. Your department will have resources and training on how to create accessible content.

Brand Victoria guidelines

The Brand Victoria Guidelines set expectations and standards for publishing materials with Victorian Government logos, including colours, fonts and layouts, which are already approved under accessibility standards.

Challenges with place-based data gathering

Some common challenges to keep in mind when collecting data in place-based approaches include:

  • Access to meaningful and up-to-date data about the local area is consistently raised as a key challenge by place-based initiatives.
  • It is often difficult to find data that is sufficiently detailed at the community level and it may not exist or be collected regularly by any organisation.
  • Some initiatives are unable to source sufficient expertise in data or data analytics. Think about your role in supporting an initiative’s capability and capacity in this area.

Case study: Maranguka Justice Reinvestment Project (New South Wales)

One recent notable example of efforts to monitor and report data at the local and regional comes from Bourke, NSW, where a community-led collective impact initiative has been developing a Justice Reinvestment approach from 2013, to reduce the high rate of offending and incarceration.

The Maranguka Justice Reinvestment Project, as with collective impact approaches generally, aims to provide data-driven interventions, so data collection and analysis are needed to inform local community decision-making about how and where to deploy resources.

Significant effort has been put into setting up local data monitoring systems, as under a collective impact approach outcomes are monitored by a shared measurement system, ensuring they are valued, used and transparent for the community and all project stakeholders involved. This case highlights the benefits of external `data experts’ working with local communities and government agencies to build effective and accessible local data platforms to support place-based approaches.


Additional tools and resources


Data sharing enablers

Legislation and compliance



National level

Data sharing enablers

Legislation and compliance


Publicly available data

Data sources not (yet) available on DataVic, including state and federal government data are outlined below.

Rental affordability

Community safety

Population and forecasted changes

Student health

Community unemployment

Community disadvantage