Business Of Collaboration
•
5 likes•1,742 views
As many organisations have discovered, implementing a technology solution by itself rarely results in more effective collaboration and knowledge sharing. Sustainable implementation of Enterprise Social Software systems requires: 1. Understanding of how and why successful knowledge-sharing communities and networks perform. 2. A system that implicitly acknowledges the constraints (time, process) and motivations (reciprocity, reward) that individuals experience within such networks. 3. A blended approach where technology seamlessly supports the behavioural characteristics that will encourage users to self-organize, collaborate and co-create.
Recommended
Recommended
More Related Content
More from Collabor8now Ltd
More from Collabor8now Ltd (20)
Recently uploaded
Recently uploaded (20)
Business Of Collaboration
- 1. The Business of Collaboration Creating The Conditions For Success November 2011 New Paradigms For Collaboration and Knowledge Sharing Steve Dale: steve.dale@collabor8now.com Twitter: @stephendale Twitter: @collabor8now
- 4. Is Collaboration the same as Knowledge Sharing?
- 5. Friends Family Suppliers Work colleagues Customers Expert Expert The world we know The world we don’t know
- 6. Friends Family Suppliers Work colleagues Customers Expert Expert The world we know The world we don’t know
- 7. Your Social Graph Friends Family Suppliers Work colleagues Customers Expert Expert The world we know The world we don’t know
- 9. Who should I follow or connect to? What networks do I join?
- 10. Who should I follow or connect to? What networks do I join? Where do I find the conversations most relevant to ME?
- 13. This is the era of the social network, where anyone can have a voice and everyone wants to be heard
- 14. Photo courtesy of Will Lion http://www.flickr.com/photos/will-lion/2595497078/sizes/z/in/photostream/
- 15. The Social Network Paradox
- 16. The Social Network Paradox
- 18. I want relevant information to find me!
- 19. If only we could intelligently aggregate and integrate our social graph with our interest graph...
- 20. Local communities Adult social care HR Local communities Carbon Reduction Open/linked data IM & KM Adult social care HR Local communities Health Informatics Carbon Reduction IM & KM Health Informatics Smart Cities Smart Cities HR HR Smart Cities Smart Cities Health Informatics HR IM & KM Adult social care Adult social care Local communities IM & KM Health Informatics Smart Cities Health Informatics Adult social care Smart Cities Open/linked data Open/linked data
- 21. Local communities Adult social care HR Local communities Carbon Reduction Open/linked data IM & KM Adult social care HR Local communities Health Informatics Carbon Reduction IM & KM Health Informatics Smart Cities Smart Cities HR HR Smart Cities Smart Cities Health Informatics HR IM & KM Adult social care Adult social care Local communities IM & KM Health Informatics Smart Cities Health Informatics Adult social care Smart Cities Open/linked data Open/linked data
- 22. Aggregating and Categorising Local communities Adult social care HR Local communities Carbon Reduction Open/linked data IM & KM Adult social care HR Local communities Health Informatics Carbon Reduction IM & KM Health Informatics Smart Cities Smart Cities HR HR Smart Cities Smart Cities Health Informatics HR IM & KM Adult social care Adult social care Local communities IM & KM Health Informatics Smart Cities Health Informatics Adult social care Smart Cities Open/linked data Open/linked data
- 23. Aggregating and Categorising Adult social care HR Carbon Reduction Adult social care HR Adult social care HR Carbon Reduction HR Adult social care HR Adult social care Health Informatics Health Informatics Health Informatics IM & KM Health Informatics Health Informatics IM & KM IM & KM IM & KM Smart Cities Local communities Smart Cities Local communities Smart Cities Smart Cities Local communities Open/linked data Smart Cities Open/linked data Local communities Open/linked data
- 24. Aggregating and Categorising Adult social care HR Carbon Reduction Adult social care HR Adult social care HR Carbon Reduction HR Adult social care HR Adult social care Health Informatics Health Informatics Health Informatics IM & KM Health Informatics Health Informatics IM & KM IM & KM IM & KM So, as a doctor, I will probably Smart Cities be interested in the networks, groups and Local communities Smart Cities websites that are talking Local communities Smart Cities about health informatics Smart Cities Local communities Open/linked data Smart Cities Open/linked data Local communities Open/linked data
- 25. ..and I want to see what is trending (hot topics)
- 26. ..and I want to see what is trending (hot topics)
- 30. I’ve tasted the future, and it’s not....
- 31. I’ve tasted the future, and it’s not.... http://www.flickr.com/photos/matthewpearson/311285574/ext
- 32. I’ve tasted the future, and it’s not.... Apps! http://www.flickr.com/photos/matthewpearson/311285574/ext
- 36. Enterprise App Stores •Empowering the user for self-service
- 37. Enterprise App Stores •Empowering the user for self-service •Easy to use conduit of software, services and data
- 38. Enterprise App Stores •Empowering the user for self-service •Easy to use conduit of software, services and data •Model widely understood by developers and consumers of software
- 39. Enterprise App Stores •Empowering the user for self-service •Easy to use conduit of software, services and data •Model widely understood by developers and consumers of software •Recognition that one size doesn’t fit all (e.g. the lobotomised corporate PC)
- 40. Enterprise App Stores •Empowering the user for self-service •Easy to use conduit of software, services and data •Model widely understood by developers and consumers of software •Recognition that one size doesn’t fit all (e.g. the lobotomised corporate PC) •Life-cycles for apps potentially short: discarded when no longer useful/relevant
- 41. Take -aways 18
- 42. Take -aways • More people suffering “Social Network Fatigue” - desire for one place to do business, 18
- 43. Take -aways • More people suffering “Social Network Fatigue” - desire for one place to do business, • Enterprise Social Software (ESS) solutions must integrate with legacy systems and business processes. 18
- 44. Take -aways • More people suffering “Social Network Fatigue” - desire for one place to do business, • Enterprise Social Software (ESS) solutions must integrate with legacy systems and business processes. • ESS must add value - more fluid knowledge flows, decision support etc. 18
- 45. Take -aways • More people suffering “Social Network Fatigue” - desire for one place to do business, • Enterprise Social Software (ESS) solutions must integrate with legacy systems and business processes. • ESS must add value - more fluid knowledge flows, decision support etc. • Mashups and Enterprise App Stores will become increasingly important for business agility 18
- 46. Take -aways • More people suffering “Social Network Fatigue” - desire for one place to do business, • Enterprise Social Software (ESS) solutions must integrate with legacy systems and business processes. • ESS must add value - more fluid knowledge flows, decision support etc. • Mashups and Enterprise App Stores will become increasingly important for business agility • Develop for mobile, think PC, not other way 18
- 47. Email: steve.dale@collabor8now.com Twitter: @stephendale Twitter: @collabor8now More information: www.pfiks.com Telephone: +44207 016 8843
Editor's Notes
- \n
- What is the question that connects the images?\n\nCollaboration pre-supposes that we have someone to collaborate with - in this example the person on the other side of the see-saw. The see-saw will only work with the collaboration of the people involved; in this instance, the child at each end of the see-saw.\n\nKnowledge sharing makes no assumptions about collaboration; it’s possible to share knowledge with people we don’t know, e.g. by posting something to an on-line forum, or writing a blog about something we have seen or read or experienced. We may not know who is going to read our missive, or what value they may place on it. The posting might lead to some form of collaboration with the readers/consumers, but that is not necessarily the primary purpose for knowledge sharing. \n\nTo explain the choice of image in this slide, i.e. the sergeant major drilling his troops, I wanted something which emphasised the importance of knowledge sharing in certain circumstances. In this instance, we might assume the sergeant major is responsible for passing on such knowledge that will enable his troops to survive in a war situation, or in other words, applying knowledge that could be the difference between life or death.\n\nSo, I’m postulating that “collaboration” is different to “knowledge sharing” in that they have different objectives, though clearly there will be some element of knowledge sharing in a collaborative environment.\n
- What is the question that connects the images?\n\nCollaboration pre-supposes that we have someone to collaborate with - in this example the person on the other side of the see-saw. The see-saw will only work with the collaboration of the people involved; in this instance, the child at each end of the see-saw.\n\nKnowledge sharing makes no assumptions about collaboration; it’s possible to share knowledge with people we don’t know, e.g. by posting something to an on-line forum, or writing a blog about something we have seen or read or experienced. We may not know who is going to read our missive, or what value they may place on it. The posting might lead to some form of collaboration with the readers/consumers, but that is not necessarily the primary purpose for knowledge sharing. \n\nTo explain the choice of image in this slide, i.e. the sergeant major drilling his troops, I wanted something which emphasised the importance of knowledge sharing in certain circumstances. In this instance, we might assume the sergeant major is responsible for passing on such knowledge that will enable his troops to survive in a war situation, or in other words, applying knowledge that could be the difference between life or death.\n\nSo, I’m postulating that “collaboration” is different to “knowledge sharing” in that they have different objectives, though clearly there will be some element of knowledge sharing in a collaborative environment.\n
- What is the question that connects the images?\n\nCollaboration pre-supposes that we have someone to collaborate with - in this example the person on the other side of the see-saw. The see-saw will only work with the collaboration of the people involved; in this instance, the child at each end of the see-saw.\n\nKnowledge sharing makes no assumptions about collaboration; it’s possible to share knowledge with people we don’t know, e.g. by posting something to an on-line forum, or writing a blog about something we have seen or read or experienced. We may not know who is going to read our missive, or what value they may place on it. The posting might lead to some form of collaboration with the readers/consumers, but that is not necessarily the primary purpose for knowledge sharing. \n\nTo explain the choice of image in this slide, i.e. the sergeant major drilling his troops, I wanted something which emphasised the importance of knowledge sharing in certain circumstances. In this instance, we might assume the sergeant major is responsible for passing on such knowledge that will enable his troops to survive in a war situation, or in other words, applying knowledge that could be the difference between life or death.\n\nSo, I’m postulating that “collaboration” is different to “knowledge sharing” in that they have different objectives, though clearly there will be some element of knowledge sharing in a collaborative environment.\n
- Most of us are happy to collaborate and share ideas with the people we know (i.e. the definition of “collaboration”). \n\nBut what about the huge untapped resources and expertise that we don’t know about? We may get to hear about people in this “unknown world” via recommendations or word of mouth, but how do we connect and engage with them? How can we know what we don’t know? How do we find the answers to our questions in this “unknown world”?\n\nIf nothing else, this is where the power of social networks comes to the fore. We have the tools and technology to be able to “crowd-source” our questions. Social media tools such as Twitter or Quora make it easy to post queries to a largely anonymous network of people in the hope that someone will have the answer or the appropriate knowledge and experience we are seeking. By engaging and connecting with the people that respond we can grow our personal network, often referred to as our “Social Graph”.\n\nBetter still if the system or network we have joined can suggest contacts for us, based on what it knows about us, either explicitly (our digital identity and personal profile), or implicitly (our digital footprint, i.e. our ‘likes’, the people we have connected with and the on-line places we have visited).\n\n\n
- Most of us are happy to collaborate and share ideas with the people we know (i.e. the definition of “collaboration”). \n\nBut what about the huge untapped resources and expertise that we don’t know about? We may get to hear about people in this “unknown world” via recommendations or word of mouth, but how do we connect and engage with them? How can we know what we don’t know? How do we find the answers to our questions in this “unknown world”?\n\nIf nothing else, this is where the power of social networks comes to the fore. We have the tools and technology to be able to “crowd-source” our questions. Social media tools such as Twitter or Quora make it easy to post queries to a largely anonymous network of people in the hope that someone will have the answer or the appropriate knowledge and experience we are seeking. By engaging and connecting with the people that respond we can grow our personal network, often referred to as our “Social Graph”.\n\nBetter still if the system or network we have joined can suggest contacts for us, based on what it knows about us, either explicitly (our digital identity and personal profile), or implicitly (our digital footprint, i.e. our ‘likes’, the people we have connected with and the on-line places we have visited).\n\n\n
- Most of us are happy to collaborate and share ideas with the people we know (i.e. the definition of “collaboration”). \n\nBut what about the huge untapped resources and expertise that we don’t know about? We may get to hear about people in this “unknown world” via recommendations or word of mouth, but how do we connect and engage with them? How can we know what we don’t know? How do we find the answers to our questions in this “unknown world”?\n\nIf nothing else, this is where the power of social networks comes to the fore. We have the tools and technology to be able to “crowd-source” our questions. Social media tools such as Twitter or Quora make it easy to post queries to a largely anonymous network of people in the hope that someone will have the answer or the appropriate knowledge and experience we are seeking. By engaging and connecting with the people that respond we can grow our personal network, often referred to as our “Social Graph”.\n\nBetter still if the system or network we have joined can suggest contacts for us, based on what it knows about us, either explicitly (our digital identity and personal profile), or implicitly (our digital footprint, i.e. our ‘likes’, the people we have connected with and the on-line places we have visited).\n\n\n
- Most of us are happy to collaborate and share ideas with the people we know (i.e. the definition of “collaboration”). \n\nBut what about the huge untapped resources and expertise that we don’t know about? We may get to hear about people in this “unknown world” via recommendations or word of mouth, but how do we connect and engage with them? How can we know what we don’t know? How do we find the answers to our questions in this “unknown world”?\n\nIf nothing else, this is where the power of social networks comes to the fore. We have the tools and technology to be able to “crowd-source” our questions. Social media tools such as Twitter or Quora make it easy to post queries to a largely anonymous network of people in the hope that someone will have the answer or the appropriate knowledge and experience we are seeking. By engaging and connecting with the people that respond we can grow our personal network, often referred to as our “Social Graph”.\n\nBetter still if the system or network we have joined can suggest contacts for us, based on what it knows about us, either explicitly (our digital identity and personal profile), or implicitly (our digital footprint, i.e. our ‘likes’, the people we have connected with and the on-line places we have visited).\n\n\n
- Most of us are happy to collaborate and share ideas with the people we know (i.e. the definition of “collaboration”). \n\nBut what about the huge untapped resources and expertise that we don’t know about? We may get to hear about people in this “unknown world” via recommendations or word of mouth, but how do we connect and engage with them? How can we know what we don’t know? How do we find the answers to our questions in this “unknown world”?\n\nIf nothing else, this is where the power of social networks comes to the fore. We have the tools and technology to be able to “crowd-source” our questions. Social media tools such as Twitter or Quora make it easy to post queries to a largely anonymous network of people in the hope that someone will have the answer or the appropriate knowledge and experience we are seeking. By engaging and connecting with the people that respond we can grow our personal network, often referred to as our “Social Graph”.\n\nBetter still if the system or network we have joined can suggest contacts for us, based on what it knows about us, either explicitly (our digital identity and personal profile), or implicitly (our digital footprint, i.e. our ‘likes’, the people we have connected with and the on-line places we have visited).\n\n\n
- Most of us are happy to collaborate and share ideas with the people we know (i.e. the definition of “collaboration”). \n\nBut what about the huge untapped resources and expertise that we don’t know about? We may get to hear about people in this “unknown world” via recommendations or word of mouth, but how do we connect and engage with them? How can we know what we don’t know? How do we find the answers to our questions in this “unknown world”?\n\nIf nothing else, this is where the power of social networks comes to the fore. We have the tools and technology to be able to “crowd-source” our questions. Social media tools such as Twitter or Quora make it easy to post queries to a largely anonymous network of people in the hope that someone will have the answer or the appropriate knowledge and experience we are seeking. By engaging and connecting with the people that respond we can grow our personal network, often referred to as our “Social Graph”.\n\nBetter still if the system or network we have joined can suggest contacts for us, based on what it knows about us, either explicitly (our digital identity and personal profile), or implicitly (our digital footprint, i.e. our ‘likes’, the people we have connected with and the on-line places we have visited).\n\n\n
- Most of us are happy to collaborate and share ideas with the people we know (i.e. the definition of “collaboration”). \n\nBut what about the huge untapped resources and expertise that we don’t know about? We may get to hear about people in this “unknown world” via recommendations or word of mouth, but how do we connect and engage with them? How can we know what we don’t know? How do we find the answers to our questions in this “unknown world”?\n\nIf nothing else, this is where the power of social networks comes to the fore. We have the tools and technology to be able to “crowd-source” our questions. Social media tools such as Twitter or Quora make it easy to post queries to a largely anonymous network of people in the hope that someone will have the answer or the appropriate knowledge and experience we are seeking. By engaging and connecting with the people that respond we can grow our personal network, often referred to as our “Social Graph”.\n\nBetter still if the system or network we have joined can suggest contacts for us, based on what it knows about us, either explicitly (our digital identity and personal profile), or implicitly (our digital footprint, i.e. our ‘likes’, the people we have connected with and the on-line places we have visited).\n\n\n
- Social networks have proliferated over the past 4 or 5 years. Some have been more successful than others. Remember that even a blog can be a form of social network, and we now have over 200 billion of these (yes, more than the population of the planet!)\nNew users can be intimidated by large/mature social networks which have lots of users and content, and where engagement and conversations protocols have been established. \n
- Social networks have proliferated over the past 4 or 5 years. Some have been more successful than others. Remember that even a blog can be a form of social network, and we now have over 200 billion of these (yes, more than the population of the planet!)\nNew users can be intimidated by large/mature social networks which have lots of users and content, and where engagement and conversations protocols have been established. \n
- Social networks have proliferated over the past 4 or 5 years. Some have been more successful than others. Remember that even a blog can be a form of social network, and we now have over 200 billion of these (yes, more than the population of the planet!)\nNew users can be intimidated by large/mature social networks which have lots of users and content, and where engagement and conversations protocols have been established. \n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- But are we beginning to see the onset of “social network fatigue”? Each new social network adds to the internet background noise. Search engines have never really delivered on the promise of relevant information, and many of us resort to serendipitous discovery of key information and conversations - it’s a bit ad hoc, where knowledge discovery is more by accident than design.\n
- So, the signal to noise ratio is pretty poor at the moment and the ever-increasing volume of information hitting the Internet is likely to make it even worse. \n
- It’s a strange paradox that now we have the capability of easily creating new websites and blogs without the need for any programing skills, what we really want now is one place to view and interact with all of this information. A recent (Sept) audit of LinkedIn illustrates the problem:\n\n26 Alumni groups\n32 Corporate groups\n20 Conference groups\n132 Networking groups\n16?Nonprofit groups\n196 Professional groups.\n\nA total of 422 groups. How do you know which group(s) to join to be sure of getting the best answer to your questions? Maybe ‘all of them’ is the answer!\n\n\n
- If we want relevant information to come to us, we have to (a) tell the system something about ourselves (our digital identity and profile), (b) enable access to the sources of information that might be useful and (c) spend some time identifying and validating the sources we like and trust. We can’t leave everything to technology – what you get out is proportional to what you put in!\n\n
- If we want relevant information to come to us, we have to (a) tell the system something about ourselves (our digital identity and profile), (b) enable access to the sources of information that might be useful and (c) spend some time identifying and validating the sources we like and trust. We can’t leave everything to technology – what you get out is proportional to what you put in!\n\n
- If we want relevant information to come to us, we have to (a) tell the system something about ourselves (our digital identity and profile), (b) enable access to the sources of information that might be useful and (c) spend some time identifying and validating the sources we like and trust. We can’t leave everything to technology – what you get out is proportional to what you put in!\n\n
- If we want relevant information to come to us, we have to (a) tell the system something about ourselves (our digital identity and profile), (b) enable access to the sources of information that might be useful and (c) spend some time identifying and validating the sources we like and trust. We can’t leave everything to technology – what you get out is proportional to what you put in!\n\n
- If we want relevant information to come to us, we have to (a) tell the system something about ourselves (our digital identity and profile), (b) enable access to the sources of information that might be useful and (c) spend some time identifying and validating the sources we like and trust. We can’t leave everything to technology – what you get out is proportional to what you put in!\n\n
- If we want relevant information to come to us, we have to (a) tell the system something about ourselves (our digital identity and profile), (b) enable access to the sources of information that might be useful and (c) spend some time identifying and validating the sources we like and trust. We can’t leave everything to technology – what you get out is proportional to what you put in!\n\n
- If we want relevant information to come to us, we have to (a) tell the system something about ourselves (our digital identity and profile), (b) enable access to the sources of information that might be useful and (c) spend some time identifying and validating the sources we like and trust. We can’t leave everything to technology – what you get out is proportional to what you put in!\n\n
- If we want relevant information to come to us, we have to (a) tell the system something about ourselves (our digital identity and profile), (b) enable access to the sources of information that might be useful and (c) spend some time identifying and validating the sources we like and trust. We can’t leave everything to technology – what you get out is proportional to what you put in!\n\n
- If we want relevant information to come to us, we have to (a) tell the system something about ourselves (our digital identity and profile), (b) enable access to the sources of information that might be useful and (c) spend some time identifying and validating the sources we like and trust. We can’t leave everything to technology – what you get out is proportional to what you put in!\n\n
- If we want relevant information to come to us, we have to (a) tell the system something about ourselves (our digital identity and profile), (b) enable access to the sources of information that might be useful and (c) spend some time identifying and validating the sources we like and trust. We can’t leave everything to technology – what you get out is proportional to what you put in!\n\n
- If we want relevant information to come to us, we have to (a) tell the system something about ourselves (our digital identity and profile), (b) enable access to the sources of information that might be useful and (c) spend some time identifying and validating the sources we like and trust. We can’t leave everything to technology – what you get out is proportional to what you put in!\n\n
- If we want relevant information to come to us, we have to (a) tell the system something about ourselves (our digital identity and profile), (b) enable access to the sources of information that might be useful and (c) spend some time identifying and validating the sources we like and trust. We can’t leave everything to technology – what you get out is proportional to what you put in!\n\n
- If we want relevant information to come to us, we have to (a) tell the system something about ourselves (our digital identity and profile), (b) enable access to the sources of information that might be useful and (c) spend some time identifying and validating the sources we like and trust. We can’t leave everything to technology – what you get out is proportional to what you put in!\n\n
- This is clearly where the likes of Facebook (groups, Timeline) and Google+ (Circles, Sparks) are heading, but neither have yet achieved a ‘simple’ way of doing it. \n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Most of us will be more concerned with what the information is and whether we can trust it rather than where it is. So, do we have to worry about the “where” if we can develop some form of interoperability between systems and networks? RSS/Atom feeds and tagging are only part of the answer. We need a system that can extract meaning from the data (e.g. entity extraction) that will enable ontologies to be created and terms to be categorised for faceted search and discovery.\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Entity abstraction, aggregation and categorisation. If our profile is up to date, the Enterprise Social Software system should be able to locate, aggregate and categorise the information that we would find relevant and useful by matching terms against our profile data (who we are, where we work, what we’re interested in, etc.). Precision can be further improved by monitoring our ‘digital footprint’, i.e. the knowledge/information assets that we have ‘liked’, recommended or downloaded. If we layer on top of this the aggregated behaviour patterns of all the users, we can leverage the opportunities provided by “collective intelligence” to identify “good’ content. \n\nProducts/vendors such as Amazon do this all of the time, using explicit data (the user bought an item) and implicit (users who bought this items also looked at these items). Tracking of a user’s progress through a website is not rocket science and is a fundamental part of any web analytics software. Inject a bit of entity extraction and you start to establish the foundations of a system that can begin to ‘intelligently’ connect information with people and people with people.\n\n\n
- Liking, +1 or ‘tweeting’ not only enables sharing of information, it can be fed into ‘trending engines’ that will aggregate and categorise the crowd-sourced data to show hot topics and trends. Again, the technology is well established, but little use is made of it in many Enterprise 2.0 systems. How nice it would be if, for example, your job entailed commissioning adult social care services and you could see the trending conversations on adult social care on your Enterprise 2.0 dashboard. This feature is built into the Intelligus platform using a combination of the open source application Carrot2 and the proprietary PFIKS matching engine.\n
- All of the prior discussion refers to an environment (social media, social networks) that are already in place, and for technologies, systems and applications that are currently being delivered in many Enterprise Social Software systems. But what of the future? Where is all of this taking us?\n
- There is much more to say about new paradigms for collaboration and knowledge sharing, but I will conclude with a few words about the growing importance of ‘Apps’. With apologies to those who don’t know who Peter Kaye is and his oft-repeated reference to Garlic Bread being the future! Maybe do a quick search on YouTube and all will be revealed!\n\n\n
- There is much more to say about new paradigms for collaboration and knowledge sharing, but I will conclude with a few words about the growing importance of ‘Apps’. With apologies to those who don’t know who Peter Kaye is and his oft-repeated reference to Garlic Bread being the future! Maybe do a quick search on YouTube and all will be revealed!\n\n\n
- There is much more to say about new paradigms for collaboration and knowledge sharing, but I will conclude with a few words about the growing importance of ‘Apps’. With apologies to those who don’t know who Peter Kaye is and his oft-repeated reference to Garlic Bread being the future! Maybe do a quick search on YouTube and all will be revealed!\n\n\n
- There is much more to say about new paradigms for collaboration and knowledge sharing, but I will conclude with a few words about the growing importance of ‘Apps’. With apologies to those who don’t know who Peter Kaye is and his oft-repeated reference to Garlic Bread being the future! Maybe do a quick search on YouTube and all will be revealed!\n\n\n
- There is much more to say about new paradigms for collaboration and knowledge sharing, but I will conclude with a few words about the growing importance of ‘Apps’. With apologies to those who don’t know who Peter Kaye is and his oft-repeated reference to Garlic Bread being the future! Maybe do a quick search on YouTube and all will be revealed!\n\n\n
- There is much more to say about new paradigms for collaboration and knowledge sharing, but I will conclude with a few words about the growing importance of ‘Apps’. With apologies to those who don’t know who Peter Kaye is and his oft-repeated reference to Garlic Bread being the future! Maybe do a quick search on YouTube and all will be revealed!\n\n\n
- There is much more to say about new paradigms for collaboration and knowledge sharing, but I will conclude with a few words about the growing importance of ‘Apps’. With apologies to those who don’t know who Peter Kaye is and his oft-repeated reference to Garlic Bread being the future! Maybe do a quick search on YouTube and all will be revealed!\n\n\n
- There is much more to say about new paradigms for collaboration and knowledge sharing, but I will conclude with a few words about the growing importance of ‘Apps’. With apologies to those who don’t know who Peter Kaye is and his oft-repeated reference to Garlic Bread being the future! Maybe do a quick search on YouTube and all will be revealed!\n\n\n
- There is much more to say about new paradigms for collaboration and knowledge sharing, but I will conclude with a few words about the growing importance of ‘Apps’. With apologies to those who don’t know who Peter Kaye is and his oft-repeated reference to Garlic Bread being the future! Maybe do a quick search on YouTube and all will be revealed!\n\n\n
- As usual, Dilbert is pretty much attuned to what is happening in the business world. I would argue that most organisations haven’t yet grasped the full impact of the App market, and may view this as being the exclusive domain of the on-line gamers. In fact, (IMHO) it is shaping up to be one of the most disruptive technologies to appear since the start of the social media wave.\n
- The trends reinforce the view that apps are becoming ubiquitous in how we work and play. Note that all of these apps are developed for mobile devices.\n
- Enterprise App Stores will provide a trusted source of business-ready apps that can be delivered to a rapidly changing work environment. The end device is less important than the application. The mantra must surely be “develop for mobile, but consider the PC”, and not the other way around.\n
- Enterprise App Stores will provide a trusted source of business-ready apps that can be delivered to a rapidly changing work environment. The end device is less important than the application. The mantra must surely be “develop for mobile, but consider the PC”, and not the other way around.\n
- Enterprise App Stores will provide a trusted source of business-ready apps that can be delivered to a rapidly changing work environment. The end device is less important than the application. The mantra must surely be “develop for mobile, but consider the PC”, and not the other way around.\n
- Enterprise App Stores will provide a trusted source of business-ready apps that can be delivered to a rapidly changing work environment. The end device is less important than the application. The mantra must surely be “develop for mobile, but consider the PC”, and not the other way around.\n
- Enterprise App Stores will provide a trusted source of business-ready apps that can be delivered to a rapidly changing work environment. The end device is less important than the application. The mantra must surely be “develop for mobile, but consider the PC”, and not the other way around.\n
- I think this slide says it all!\n
- I think this slide says it all!\n
- I think this slide says it all!\n
- I think this slide says it all!\n
- I think this slide says it all!\n
- \n