Knowledge to Data Harvesting

The K2V Limited  Integrated   Data & Knowledge Life-cycle Model : The volume and complexity of data expand rapidly as they morph from their raw origins, blending with other data classes, and combining with IP to deliver a contribution for investment decisions

The K2V Limited Integrated Data & Knowledge Life-cycle Model: The volume and complexity of data expand rapidly as they morph from their raw origins, blending with other data classes, and combining with IP to deliver a contribution for investment decisions


The model illustrated above presents the upstream oil & gas sector data life-cycle (right), showing how data are transformed from their raw origins in to derivations that have more value to the business, as the business adds intellectual property (IP) to those data.  The same activities also add knowledge (left) as a consequence of the data transformation.   K2V Limited believes that the activities themselves are to a large extent generic in the oil & gas sector, allowing us to group each data item in to discrete activity related groups.   211 individual data items commonly in use in the oil & gas sector have been mapped across those activities.  Applying the knowledge & data life-cycle in this way not only shows how much value is added in the life-cycle (adding IP to the data with a clear VoI), it also allows us to recognise the "relatedness" of the data and provides a guide to how those data should be published, stored, archived or deleted.  The model facilitates more structured discussions between the geoscience community and data managers, helping to identify uniqueness in data and not waste resources on repeated or redundant items.  The model also implies that the activities themselves (Acquire, Process, Interpret, Integrate and Evaluate) provide stages in knowledge growth.  Data and knowledge remain independent and should be managed separately but they are linked through activity, which can form the basis for metadata creation.

To illustrate the principle, let us take seismic data and chart a simplified journey from raw data source to completely dissociated decision making.  Seismic are acquired and delivered to the client as raw SEG-D data (which may include offsets, gathers etc.).  SEG-D data have no intrinsic value to geoscientists (we can't visualise them in that state), so the first order derivation is to process the data (clean it up and apply an earth model) to generate usable SEG-Y data.   Geoscientists then use visual imaging software and techniques to interpret the SEG-Y data to map horizons (second order derivation), blending data from other raw classes of data (e.g. well logs below) to calibrate the horizons or to make rock property models to explain amplitude responses (Quantitative Interpretation).  Third order derivation involves a step change, comprehensively integrating data from multiple sources to generate grids, which arguably no longer need the original seismic data to sustain their own existence.  K2V Ltd calls the second order to third order transformation step (i.e. from depending on association with the raw data to becoming independent from it) the "Data Unconformity".   This data hiatus is an important concept with perceptual, data management and legal implications which will be expanded elsewhere.  For this example, the grids only retain an association with their original seismic origin through data management - grids can, in effect (like numbers) take on a life of their own if not properly managed.   The fourth order derivation concludes the dissociation journey to its destination, integrating geo-technical with geo-commercial protocols to provide the nomograms of decision making, where the real value to the business lies.  This simplified journey illustrates that data expand in complexity and volumes as we add value whilst becoming increasingly dissociated (losing association with their origins) and more difficult to manage from their points of origin.

K2V Limited supplies tools for harvesting knowledge lying dormant in large organisations and in converting that collective knowledge to value in support of decision making for the oil & gas sector (K2V: Knowledge to Value).  It is important to note here that K2V Limited is not offering any data management solutions, confining our services to knowledge and perceptions. The life-cycle model provides an overlay to existing internally resourced or outsourced data management frameworks attached to your organisation.  K2V Ltd explicitly separates data from knowledge (see here), describing them as polar opposites: data are unique and immutable, whereas knowledge is non-unique and fickle.  Knowing this means that data and knowledge have to be managed entirely differently, despite their sub-parallel growth in the life-cycle model.  However, data (without metadata) don't know that they exist or even that they have any importance to the business.  Only knowledge holders can achieve this distinction.  K2V Ltd believes that when the activity link is broken between knowledge and data, that is when the data become amorphous.  The lack of corporate memory orphans the data, challenging their validity or worse, undermining entitlement.

K2V Ltd. has developed a very simple way to associate data with knowledge by crowd-sourcing; the Data PinMap™ uses the intrinsic value of the 211 individual data items to the maturation life-cycle by mapping the data class (tied to the raw source) to data life-cycle maturity.  The tool has two functions:

1)     crowd-source knowledge holders to recollect (from their knowledge) the data they used to conduct their own investigation of an area, which includes dates, data maturity and entitlement

2)     allow the business to trace lost data and to mine amorphous data mountains with an association to context

This is how it works: knowledge holders are invited to stick a pin anywhere on the map to register key data that they are aware of.  It works in the same way as the Knowledge PinMap™ (described elsewhere), except that instead of "Function in Role", you register "Data Class" and instead of "Level of Knowledge", you indicate "Level of Maturity".  The Data Class applies generalised raw data types used in the oil & gas sector, so that when the position in the data maturity life-cycle is known, a reduced list of discrete data items can be identified.  In effect, therefore, the database draws on a taxonomy of data items generated through (and associated with) generic activities.  By crowd-sourcing the data people have worked with, a framework emerges of the data building blocks that have shaped corporate perceptions in making decisions.  In effect the tool creates metadata, which then needs to be picked up by data managers to complete the association between knowledge and data.  The Data PinMap™ provides for the first time, a tool for data managers to retro-fit metadata to their amorphous data mountains.

If you would like to know more about the Data PinMap™, please contact  Data items can be customised to match your organisation's data management framework or structured specifically to resolve critical data issues.

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The Data PinMap™ was made in collaboration with KT-mapping.