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Why information needs are not met

The diversity of stakeholders that provided input to the NASR is reflected in the range of reasons why their information needs are not being met. In many cases the information needs of stakeholders are specific and only relevant to their work program, industry or organisation. However, there were a number of consistent reasons raised by stakeholders as to why their information needs are not being met.

The reasons can be categorised as:

· barriers: such as the cost of data collections and accessing data

· structural issues with statistical assets: such as spatial deficiencies (e.g. coverage and scale), temporal inconsistencies and gaps, timeliness of releases, accuracy of estimates, and coherence

· availability of specific information: such as data on supply chains, value adding and markets, gross value of production and volume of production for specific commodities, domestic usage and consumption of commodities, land tenure, land use status, on-farm greenhouse gas emissions, on-farm technology, and innovation

Cost
Cost was reported as a major barrier by stakeholders in meeting their information needs. The main cost barriers to obtaining data were the:

· cost of collecting data, including industry’s ability to fund data collection

· cost of analysing data

· cost of accessing data through subscription or “user pays” information portals

Several of the public submissions noted the high cost to industry of using ABS as a data collection service provider. As a result, some industry bodies are investigating the feasibility of developing industry-owned and operated data collections. However, there was recognition of data quality issues that arise due to low industry participation (where participation is voluntary, not mandatory).

A number of public submissions also raised the issue of the costs of accessing information provided by private information services providers.

Timeliness
A number of public submissions highlighted the impact of delays in data production or ‘out of date’ data for them. The main barriers reported were:

· challenges using data for various reporting time frames (yearly or five yearly)

· timeliness of current publications

· impact of the time gap to accommodate sector and other changes

Consistency
Lack of consistency between datasets across time and spatially was raised as an issue in many public submissions. Comparability of data between different data sources also created difficulties in comparing and integrating data from multiple sources. Some of the main issues related to temporal and spatial consistency include:

Temporal consistency

Public submissions identified the need for long term, ongoing datasets in order to generate meaningful analyses of trends and changes over time. Temporal concordance issues included gaps in time series data and changes to methodologies that affected the ability to make comparisons from one point in time to another.

Spatial consistency

A consistent theme in submissions was the need for greater flexibility in the availability of data at a variety of spatial scales. Issues relating to spatial consistency included the:



· need for finer level geographies for agricultural commodities

· lack of consistent classifications

· lack of concordance (when changes occur)

· need for clearer classifications

· need for spatial boundaries that align

· lack of spatial consistency between datasets

· consistency of variable descriptions and spatial boundaries

Stakeholders expressed concern that different sources of information produced different outputs for a given variable. Where there were questions regarding the accuracy or reliability of data, there was no way to check or validate the veracity of this data. In many cases for survey estimates, sample sizes were very small for some industries and commodity groups, increasing the level of sampling error. The accuracy and reliability of data collected by industry was reported as being affected by poor response rates. Time series data were also regarded as unreliable due to different methodologies being employed at different points in time, variables collected over time varied and units of measurement changed.

Relevance

Relevance refers to how well a statistical product meets the needs of users, in terms of concepts measured and populations represented. A number of public submissions raised relevance issues in describing data that was not as useful as it could have been. These relevance issues included:

· data that was not fit for purpose and needs to be integrated from a range of sources and ‘massaged’ to make it useful

· finer level geographies required

· lack of disaggregated agricultural commodity data

· discoverability of data from key providers was difficult

· legal and legislative constraints on reusing/sharing unit level data (privacy/confidentiality)

· high level of expense to access data

· inappropriate format for target use

Sampling/Sample size/Coverage
A number of stakeholders raised issues regarding sampling, sample size and coverage in primary data collections, including:

· changes in survey frames and definitions (methodology)

· potential duplication with state based collections

· high respondent burden from surveys

· duplication of data being collected

· limited scope of collections

· data not available to respondents

A number of public submissions also identified the need for some form of supported education program to support the effectiveness of the NASIS. This education support would extend beyond raising awareness of the statistical assets that exist in the system, to, for example, best practice guidelines for undertaking an assessment of the need for a new data collection activity.

Provider/Respondent engagement
Stakeholders described the importance of engaging data providers (or survey respondents) in being able to produce statistics that are meaningful and useful for the sector. Overall, public submissions discussed the need to improve the relationship between data collectors and data providers which would resolve engagement issues, including:

· reluctance to provide data

· perception that personal information may not be treated confidentially

· lack of understanding by data providers as to why data is being collected and how it will be used

· receive similar survey forms from multiple organisations

· receive an unacceptable number of different survey forms to complete

· receive clearly different survey forms, but in the one short time period