How did the New Knowing group approach the definition of data?

We decided to start with the synergies right away and address issues of e.g. data/information overlaps as they occurred. Each participant had contributed with a definition of data-sharing synergies before the event; and it appeared this worked well to establish some boundaries before the event.

These definitions were:
Martin Woolley:
1. The systematic and creative integration of disparate information systems for the common purpose of generating new knowledge.
2. The fabrication of group purpose through the interweaving of disparate information sets.
3. Systematic methods for creatively mining and pooling raw information across time, space, subject domains and organisations, to common ends.

Ken Fairclough:
Short version - Un-caged, serendipically mappable data roaming free in an ecological metaphor.
Technical version - Timely, context wrapped data that is serendipically mappable i.e actively capable of open intra-domainal transfer.
Data inhibiters - Ownership, Extinction, Black-holes, Fusion, (Absolute - Relative), QME

Mathilda Tham:
An auspicious coming together of the intangible where the result is bigger or more significant than a linear summing up of the individual components.

Was it useful to bring specific examples of data-sharing synergies?

The examples were important as an introduction process within the group – they allowed us to quickly understand where we each came from. The examples also served to introduce and locate some themes that we then went on to discuss further. While sharing the examples, we came up with more examples.

With more time, and perhaps a slightly ‘calmer’ atmosphere, we might have taken the mapping of examples further, visually searching for overlaps etc. Such a shared visual map, might have afforded a more focused discussion.

As facilitator/participant I found time was only sufficient to make quick notes on post-it notes roughly organised in:

• Understandings of data
• Data sharing inhibitors
• Data sharing benefactors or vehicles

How did the specific examples inform the discussion about data and data-sharing?

These themes included:

• There is a perceived hierarchy of data, where traditionally hard data has been valued over soft data;
• In recent years the grey area between controllability and creativity is increasingly recognised;
• The notion of accurate versus true data may be helpful in a more creative research tradition;
• A continuum between relative and absolute data; for speedy reactions to data notions of e.g. cooler or hotter can be more helpful than a number;
• Data is in motion – data is not linear;
• The nature of an organisation’s culture (e.g. destructively competitive within) influences the efficacy of data-sharing;
• Data can be used as a smoke screen for dysfunctional organisations;
• Without the right question data is useless;
• Useless data is not necessarily valueless data;
• Metadata, the context wrapper for data, is essential for access to and use of data as it filters data;
• There needs to be a standard for metadata;
• Languaging is needed, perhaps in the guise of metadata, to help data move from one context to another;
• There can be data surplus;
• Much data is generated that already exists, because it has not been wrapped in such a way that we can access it;
• A problem needs to be implemented before solution is implemented – ‘wrong data’ points to wrong problem and wrong solution.

What was the New Knowing group's specific definition or approach to data and data-sharing synergies?

We came in with definitions of data-sharing synergies and did not explicitly redefine them during the session. However, New Knowing served a lense through which we explored, and it informed the concepts that came out:

• The use of prediction, intuition, speclation to generate and label data, weak signal processing;
• The need for a serendipic space for data generation and gathering (95% of all data is generated serendipically, Ken Fairclough);
• Describing data as field, colour, shape, texture, signal (rather than e.g. number);
• We compared weak signal processing to speed reading;
• The need for a ‘medium’ to pick up weak signals;
• Sustainable data – for data to be ‘sustainable’, i.e. be accessible over time and across contexts, the metadata should include (in addition to date, source, unit etc) such speculative descriptions of possible future application that the researcher coding them intuitively can foresee. Martin Woolley described such an evolving data system as an inverted Google mechanism;
• Sustainable data needs to be intelligent;
• With the notion of building in further opportunities in metadata follows the question of ownership;
• While ‘new knowers’ trust serendipically generated and soft data, the data may need repackaging to lend authority in other, more conventional knowing cultures;
• The question ‘is it helpful’ may be more relevant than ‘is it accurate’ when looking at a data set.

Please view the NewKnowing working data poster

Please view the EnvisioningRedesignedposter
Please view the LanguagingRedesignedposter
Please view the PushingDoingRedesignedposter

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