Enterprise 2.0 and Collective Intelligence
Enterprise 2.0 Adoption Patterns
In a previous article “Game Theory for Enterprise 2.0 Adoption” we took a look at some simple ideas for improving the intelligence of profiles in Enterprise 2.0 platforms. There we looked at some simple modifications that can be applied to any platform to have the system improve the Adoption Rate. This article will review how to improve the intelligence of things, such as documents and links. These patterns can also include activities outside the platform. You can learn more about Enterprise 2.0 Adoption Patterns here.
The whole is greater than the sum of it’s parts
Collective intelligence is a shared or group intelligence that emerges from the collaboration and competition of many individuals. Collective intelligence appears in a wide variety of forms of consensus decision making in bacteria, animals, humans, and computer networks. [Wikipedia] This goes beyond “User Contributed Content”.
Enterprise 2.0 Pilots and Collective Intelligence
We can also see that time will improve the Collective Intelligence of Enterprise 2.0 Platforms in this article “Enterprise 2.0 Pilot: Yes, No” by Emanuele Quintarelli. Most of this article covers the pros and cons of Enterprise 2.0 Pilots, which is important to understand.
How Much Scale Is Needed in Enterprise 2.0 Employee Adoption?
In this article “How Much Scale Is Needed in Enterprise 2.0 Employee Adoption?” by Hutch Carpenter, we see the levels of participation required for multiple types of Enterprise 2.0 software to deliver value. Hutch also does a great job on explaining the purposes of these software packages for your Enterprise Platform. I recommend his article and following him on Twitter @bhc3
Improving the Intelligence of Enterprise 2.0 Platforms
- Add a “date field” for the updated date. Display “recently updated documents” list on the documents dashboard.
- Add a “date field for the last viewed date. Display “recently viewed documents/links” list on the enterprise dashboard. Members and Community Managers can easily see which documents/links are active in real time.
- Add a “int field” to store the count of document/link views. Display “most viewed/clicked documents/links” list on the enterprise dashboard. Members and Community Managers can easily see the most active documents/links, which can be a trigger to find and/or add similar content. This will also help members identify and possibly remove content with lower values.
- Display a limited “recently viewed by” in the document details view. The limit works with time, while the list surfaces activity. This encourages connections. Members can find “like minded” colleagues along with discovering related documents/links.
- Display “recent activity widget” Most users enjoy seeing the intelligence value of documents/links in visual ways. You can dynamically pull in these stats and display chart and graph widgets of this valuable information.
- Add two “int fields” to store the document/link ‘like’ counts. One filed to store the number of voters, the other to store total vote count. This will support a “star rating feature” or a simple “like rating feature”. A simple “like” feature will support finding the average by dividing total against count. You could also create a separate database table the stores all your rating/like data.
The key to taking advantage of automatically adding intelligence to your platform assets, especially bookmarking tools, is to always pass the request to your recommendation engine before redirecting to the requested asset. This type of behavior is similar to what you see in http://bit.ly, the popular link shortening tool used mostly on Twitter. My thoughts on apps in the Enterprise, “E 2.0 Apps: if they can’t be measured, then they have no business on the Enterprise Platform.”
These are just a few ideas on adding that “addictive property” to an Enterprise 2.0 Platform. These simple ideas will also help platform managers identify weak assets and key assets on the platform. Enterprise 2.0 Adoption Patterns are key to increasing the total value of the platform.
Mind Reading Enterprise 2.0 Platforms
Collective Intelligence and Recommendation Engines
- What if the platform delivered value directly to you through benefits you can use right now?
- What if the platform made your job super easy?
- What if your colleagues were so addicted to the platform, they stopped clogging your inbox?
- What if all your colleagues used the platform, but you did not?
- What if your new job requires the efficient use of Enterprise 2.0 tools?
- What if the platform actually made your life easier?
- What if you discovered that the more you use the platform, the more it understood what you wanted?
Imagine this. Yesterday you logged into your Enterprise 2.0 Platform and wrote an article about Augmented Reality. Today you are thinking about forming a group to discuss your topic and you are wondering if there are other people in your organization interested in working together to learn more. You login to your Enterprise Dashboard and see several colleague connection recommendations that are interested in Augmented Reality. You also see a widget that recommends several groups: “AR World”, “AR Bytes”, “Human Interaction Tech”, “Mobile Apps”, “Band of Geeks”. You see another widget that recommends a list of external resources about Augmented Reality. You might feel a little freaked out at this point and start wondering how did this platform read my mind?
This is the same type of technology that had made Google Billions of dollars. We also see this type of technology used at Amazon to improve the user experience and boost sales. Google hit the JACKPOT with their “sharable content objects” known as AdSense, these micro mashups are embedded in web pages all over the internet. It is important to understand the incredible value of “sharable content objects” and mashups, but we will save that for another time. Other good examples include LinkedIn and NetFlix.
Programming Collective Intelligence
Adding simple rating, view, and point counters will improve the user experience and help breath intelligent life into Enterprise 2.0 Platforms. These type of patterns are also supported in game theory. You can take this a step further by creating behavior algorithms for recommendation engines with well known mathematical formulas. Many of these metric formulas are available here on Wikipedia. These formulas were a bit daunting for me, so I bought an amazing book that explains how to quickly build intelligence into Enterprise 2.0 Platforms.
Programming Collective Intelligence: Building Smart Web 2.0 Applications
This book takes theory of Collective Intelligence and breaks it down into tasty byte sized morsels of tantalizing apps for improving the experience on any platform. The author carefully crafted several enjoyable sections and includes step by step guidance for creating multiple types of recommendation engines. I recommend buying this book, before your next update. Here is the quick overview list of just a few topics covered in this very helpful book.
- Collaborative filtering techniques that enable online retailers to recommend products or media
- Methods of clustering to detect groups of similar items in a large dataset
- Search engine features–crawlers, indexers, query engines, and the PageRank algorithm
- Optimization algorithms that search millions of possible solutions to a problem and choose the best one
- Bayesian filtering, used in spam filters for classifying documents based on word types and other features
- Using decision trees not only to make predictions, but to model the way decisions are made
- Predicting numerical values rather than classifications to build price models
- Support vector machines to match people in online dating sites
- Non-negative matrix factorization to find the independent features in adataset
- Evolving intelligence for problem solving–how a computer develops its skill by improving its own code the more it plays a game