Concept Mapping- Integrating Technology into Decision Making

6 Nov

Whether it’s open source data, big data, BI or any similar technology, the key point of using any of the former is to make “better” decisions. So, some of the present assumptions which underpin this point are that more data equals better decisions and with more data you stand the chance of making better discoveries.

The problem is, how do you effectively integrate these technologies into a current decision making process? Given the capacity for human error, even by experts, and that for any decision aid to be successful it MUST allow meaning to be drawn in way which acknowledges current practices and culture, how do you leverage new technology as a decision aid?

This is where the marketing and industry reports tend to stop and fail to address- how do you integrate new technology into the cognitive process of decision making? One such method is concept mapping, and it’s been used to great effect in weather forecasting.

Concept mapping requires the elicitation and representation of the knowledge people draw upon, in a profession, to make a decision. Its skilled work as it aims to draw out tacit and semi tacit knowledge, rules of thumb which people apply with regularity but remain largely unaware of. As an example tie your shoelaces whilst talking through the reasons for making each step, it’s harder than you think!

The extracted knowledge is then formed into short concept nodes, drawing from Candrell et all’s (2006) example, a concept for weather forecasting “Gulf of Mexico Effects” would be simply “Fog”. The concepts are then assembled into a hierarchy and linked together by casual relationships. For example

Node-Fog

Link- Leads to

Node-Rain

So, when you see fog, it has the potential to be an indicator for rain. The next step is to verify this indicator, to examine how applicable it is within this context; at this point it’s possible you’ll need more data.

The concept map will take you to a link which has the real time data feed (for example) of the current weather system. The map will also contain the concepts and links which other forecasters have used to confirm\ disconfirm similar predictions. Our example forecaster may “discover” something new, a novel way of examining the data which produces a faster more accurate test for assessing the potential of fog to produce rain. This gets added to the concept map and builds up the domain knowledge.

Concepts maps are a method of integrating expertise, current decision making frameworks and new technology. They capture rules of thumb, case studies, analytical tips and most importantly they capture and integrate discoveries. As the user learns, so does the organisation and from my perspective the most important point- it supports what people currently do well and doesn’t drown out expertise via a technology change programme.

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