1.
Multi-purpose utility
‘Classic’
Search Query-Result List
Business Intelligence
Complex Reasoning,
Learning by comparison,
contradiction
and analogy
Search Driven
Work Task Dashboards &
Real Time Social Activity Feeds
Simple ‘factual’ Answers
Constructivist
Information
containers &
associations
Concepts,
entities &
associations
User centric
Personalization
Task centric
SEARCH AND FIND
Lookup/Known Item Search
Time saving
EXPLORE AND DISCOVER
Exploratory Search
Learning/Creativity Value
Positivist
Increasing context
Information
Architecture
Supporting natural
language interactions,
visualization, actions,
prediction, prescription,
suggestions &
serendipity
Organizational norms (Information and Learning Culture)
Paul H Cleverley, Robert Gordon University (2016)

2.
Brief narrative of the model 1/3
• y-axis left
• Continuum, from containers (images, documents, web pages) to objects, names, entities
within those containers.
• y-axis right
• Information organized ‘around you’ your ‘bubble’ towards a more business
task/phenomenon centric view.
• x-axis top
• From a lookup/known item where there is a right answer to exploratory (directed-
undirected) multi-faceted, complex, no right answer ‘exploratory’. After Marchionini 2006.
• x-axis bottom
• Generally more context from left to right. Left hand side tends to seek information that is
known, right hand side tends to surface information that can be used to contruct ‘new
knowledge’ From ‘time saving’ to ‘value / wealth creation’.
Paul H Cleverley, Robert Gordon University (2016)

3.
Brief narrative of the model 2/3
• Bottom left hand corner
• Typical digital libraries, Internet search (moving to the right as content is more tailored and
personalized). Such as Google Scholar, Google and Bing.
• Top left hand corner
• Moving towards answers not containers of information. Rich answers provided by (for
example) by Google Knowledge Graph for example (e.g. the weather), Wolfram Alpha moving
towards right and chatbots.
• Bottom right hand corner
• Personalized activity feeds and dashboards. For example, the ‘LinkedIN’ of the business
world, Microsoft Delve.
• Top right hand corner
• Convergence with business intelligence. Complex reasoning, regression, SVM, neural
networks. For example IBM Watson, Wipro Holmes, Word2vec, Glove, Tensorflow.
Paul H Cleverley, Robert Gordon University (2016)

4.
Brief narrative of the model 3/3
• Information Architecture
• Underpinned by Knowledge Organization, Curation, Machine Learning. A
move to more interactive natural language, visualization to cater for
information overload and smaller devices, need to suggest interesting,
surprising, unexpected.
• Culture
• An information culture appropriate to the business needs and opportunities
• Enterprise search and discovery capability may boil down to Organizational
learning, at the user, support and executive level.
Paul H Cleverley, Robert Gordon University (2016)