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Metadata, Data and Sense-Making

All government, business, and non-profit organisations collect voluminous amounts of metadata and data. Traditional techniques struggle to make sense of this overabundance of data, lack of metadata and its context. 
This situation contrasts with that prevailing some years ago, when a lack of data was a key concern for decision-makers. 

Additional challenges faced now by many data-rich organisations are that data is industry-specific, of variable quality, is not discoverable or connected, and also lacks context. As a result, most data cannot be reliably used or shared. 

Enterprises need to manage data responsibly and ethically, but they also want to look for ways to increase its usefulness, its return on investment, and its effectiveness for evidence-based decision-making. 

Additionally, enterprises seek minimisation of human intervention in data management, whilst increasing the quality of the metadata, data, and their context, via machine learning (ML) and through human-in-the-loop processes.


Improving metadata and data

SURROUND’s mission is to intelligently discover, connect, and reason about organisational knowledge assets to provide the backbone for informed and contextually-aware strategic and operational decisions. 

Customer Story

The customer

On behalf of the Intergovernmental Committee on Survey and Mapping, Land Information New Zealand engaged SURROUND to manage a consortium of companies to develop a consistent model for exchanging digital cadastral survey information between the survey industry and government land administration agencies.

The problem

Surveyors producing cadastral datasets for exchange with jurisdictions in Australia and New Zealand need to cater for numerous data models using several software products, whereby the data models are not harmonised across jurisdictions. Existing standards and associated data models are generally not enabled to cater for 3D Cadastral Datasets, although some software vendors do provide 3D Cadastral Information Capabilities.

Currently, exchanging digital cadastral survey information between the survey industry and government land administration agencies is not enabled by a single information model that enables easy data sharing across jurisdictional boundaries. The survey industry must cater for multiple data models across Australia and New Zealand because of lack of harmonisation across existing data models.

There is an increasing need for 3D survey data across the jurisdictions.

The solution

SURROUND was engaged by LINZ to lead a consortium of companies to produce an information model for a harmonized 3D Cadastre data exchange. Throughout the production of the 3D Cadastre Model, we engage with cadastral data stakeholders across jurisdictions and cadastral software vendor stakeholders, to ensure collaborative modelling.

The resulting 3D Cadastre Model will enable harmonisation of cadastral data across jurisdictional boundaries, using the Surveyors’ choice of cadastral software product. Exchange of cadastral datasets between surveyors and government land administration agencies (including across jurisdictional boundaries) is facilitated, and is as efficient as possible for the survey industry and for governments.

Our product suite includes

Insights and helpful links 

  • The Health Knowledge Graph (HKG) - knowledge graphs layered with complex reporting capabilities regarding disease types and hospital capabilities in specific communities.
  • Data Fabric (DF): is the broad collection of knowledge and information assets than an organisation collects and uses to meet its objectives. SURROUND delivers the capabilities that underpin the key pillars of a comprehensive data fabric, as explained by Gartner.
  • Language Curation (LC): is the management, selection, organisation, and presentation of the use of language, typically at an organisation level, that incorporates general and specialised language usage. 
  • Error Checking (EC): an intelligent technique to validate the technical use of language, to decrease ambiguity, to increase the consistency of metadata and data elements, and to validate that end-to-end processes have been successfully undertaken.
  • Discrete Global Grid System (DGGS) Converter (DGGSC): the DGGS provides a canonical view of geospatial data as a set of region points. The DGGSC transforms latitude and longitude into two-dimensional region points. It also converts latitude, longitude, and elevation into three-dimensional region points. The DGGSC can also perform these transformations in the opposite direction. When combined with the dimension of time in the SOP, the result enables the ability to reason over the rate of change over space and time. This capability is very useful in predicting future scenarios such as the impacts of natural disasters, and concepts such as long-term unemployed people or community health.