Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.

Modality of enterprise search and discovery capability model

225 Aufrufe

Veröffentlicht am

After presenting at conferences in the US and Europe, I have made some minor modifications to the 'Modality of Search' model I posted a few months ago. Version 2 below. This proposes on new way in which to view Enterprise Search and Discovery Capability. It is early days, but there is some evidence the model and narrative is capable of changing the mind-sets of senior executives, leading to a different approach towards 'search' within the organization. It also positions such technologies as Google, Apple SIRI, Microsoft Delve, IBM Watson and Chatbots big data.

Veröffentlicht in: Business
  • Als Erste(r) kommentieren

Modality of enterprise search and discovery capability model

  1. 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. 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. 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. 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)