From spatial information to Spatial Knowledge Infrastructure

By Anthony Wallace on 21 June, 2017

Written by Jon Fairall, foundation editor of Position magazine.

For the last twenty years, one of the fundamental preoccupations of the spatial industry has been the creation of a spatial data infrastructure.

It’s not hard to see why. Spatial data is expensive to collect. It needs to be used efficiently. But looking into the future, people in the industry are starting to worry that an SDI is not enough to extract the full value from the data.

Data and the way it is managed was one of the underlying themes at the recent conference of the Cooperative Research Centre for Spatial Information (CRCSI). The organisation is developing a White Paper that is proposing a new generation of spatial infrastructure based around the premise that we don’t need information; we need knowledge. The subtle difference between the two concepts may be one of the key drivers of the next generation of workers in the industry.

A new way of looking at datasets may be fundamental to the next step change in productivity.”

A bit of context helps. At the very highest level, one of the key concerns about the economy – in Australia and globally – is sluggish or non-existent growth in productivity. The political and economic tantrums we are facing in the second half of this decade are due at least in part to sluggish growth in wages for the bottom half of society. For a decade now, real growth has been concentrated among the rich. Some of our foremost thinkers fear for the future of democratic institutions as a result.

If this analysis is even half-way correct, the implication is that a lot rides on increasing productivity. In economics, productivity is taken as output per unit of input. It balances all the economic inputs – usually divided into labour and capital – against revenue. One of the central aims of business is to minimise one and maximise the other, but it is, of course, also a central concern of government departments that they should do what is required of them with the minimum of labour and capital.

Historically, improvements in the way computers are used have been one of the main drivers of productivity. Arm your workers with computers and the amount they can produce skyrockets. You need less of them, so the labour input is reduced. Those workers that are left produce more, so revenue increases. Likewise, if you can automate your machinery, you can produce much more while spending less.

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But a number of analysts have pointed to slowing productivity growth as evidence that computing’s low hanging fruit has been well and truly picked. The secretaries have been replaced by word-processors. The mail room guy has been replaced by email. The good news, according to publicity on the CRCSI’s website, is that “organisations that currently manage data will see staff time savings of between 50-70 per cent,” by using new and improved methods of gathering and massaging data.

How? The normal panaceas of the hardware vendors – bigger hard drives; faster processors; don’t really seem to cut it. Do you really need more data? More memory? Without applications to justify them, such changes hardly seem to matter. What would make a difference? In both government and business circles, there is a growing suspicion that data holds the key. In particular, a new way of looking at datasets may be fundamental to the next step change in productivity. il The new paradigm is called Knowledge i Management which is broadly defined as the process of creating and managing the knowledge necessary to run a firm or meet some objective. The aim is to focus the collective knowledge of participants on a d problem. It’s been around as an academic discipline since the 1990s but it only now starting to percolate into business thinking.

The thing about knowledge management that makes it interesting to readers of Position Magazine is that it presupposes accurate, authoritative data and, for many interesting applications, it presupposes spatial data. Spatial knowledge infrastructure So what might a spatial knowledge infrastructure, or SKI for short, look like? Perhaps one way of looking at this is that spatial knowledge management is what happens when spatial data infrastructure meets social media. To see why, think about the supply chain model behind existing spatial data infrastructures.

Necessarily, an SDI uses a unidirectional work flow. For each dataset there is a defined custodian who creates and is responsible for the data. The custodian has its own business reasons for creating the data, nevertheless it makes the data available to other players in the industry in an agreed format. But one of the key focuses of knowledge management is that it is participatory.

It’s a platform that is designed to elicit various types of knowledge from a variety of actors, massage the resulting data and then present it back to users in an agreed format. In the terms we would understand in an SDI, there is no custodian, or at the very least, the custodian isn’t the only one creating the data. There may not even be one person or organisation with responsibility to manage the data. Instead, there are multiple data gatherers all contributing to the data set…

This is the first half of Jon Fairall’s article on Spatial Knowledge Infrastructure, which first appeared in issue #87 of Position magazine. In the second half, Fairall explores the benefits of SDK and how organisations can start to reap these benefits.