1.1 The Need for Groundwater Data Standards

Each of the principal groundwater data providers in Australia have independently established database structures, suited to their particular functions and priorities. Likewise, users have set up their own ways of dealing with groundwater data. The diversity in which groundwater data is stored and transferred unnecessarily complicates natural resource management. A generic data transfer standard would bridge the gap between user and provider. From a user perspective, such a standard would greatly reduce the time presently required to reformat data. Instead of dealing with the multitude of formats currently output from suppliers, the user only has to deal with a single standard format. Considering that many users obtain their data from many databases this will significantly boost productivity. This is particularly true for those dealing with regional groundwater provinces that transcend state borders. The current trend of data management being directed to a larger number of smaller organisations only heightens the need for standards. A generic standard also simplifies the data transfer process from a data provider perspective. A nationally recognised data format would be supplied instead of attempting to accommodate the formatting requirements of individual clients.

The establishment of standard structures and protocols for groundwater data has other benefits. The possibility of misinterpretation by users would decrease if data is uniformly structured, defined and documented. For example, using a standard coordinate system for the transfer of spatial data avoids confusion in terms of the projection details, such as the central meridian, standard parallels and spheroid. Establishing conventions in data management (eg. negative values for artesian head measurements, positive for water levels below the ground surface) avoids misinterpretation of data. Proper metadata procedures give the data supplier an opportunity to describe the reliability and limitations placed on the dataset.

Financial savings can be realised when common data structures allow organisations to share the development costs of support software. Data custodians and users alike will benefit from the experience and contributions of others in developing software and hardware configurations to support a standard data structure. The pooling of resources will allow the current rapid technological advances to be realised by a larger audience. Software developers can target products within a broader and better-defined market.

In other disciplines, the cost savings associated with introducing standards-based information management have been significant. The work of the Petrotechnical Open Software Consortium (POSC) is the classic example. Their business model estimates that the adoption of POSC standards has led to a reduction in oil production costs by 1 to 3 US dollars per barrel. This may not appear to be much, but when applied to the 1996 Australian production figures amounts to an annual saving of over US$400 million.