Here is the Read/Write/Web review of the service.

My intention here is mainly to road-test the service to see if it can be a viable tool in the “Community Partnership Development” toolbox. Test it out for yourself. Vote on some ideas that are already there, or ad your own ideas about Decision 3D.

This is not to say that groups of people cannot think and behave similarly. A group of people that share a common interest or objective is called a community, not a demographic segment.

There are many distinctions between a community and a demographic segment. Let’s look at a few and consider why the continued use of demographics makes less and less sense in today’s connected society.

Interests vs. Attributes

By definition, a community is created based on shared interests. If a company can identify a community whose shared interest is relevant to the company’s objectives, you have a nice hand in glove fit. Here is a self-defined group of people that are already talking about something the company wants to learn about.

On the other hand, a demographic segment based on attributes, is just a random heap of people that will have a variety of opinions on any given topic.

Many vs. Single

By definition, individuals can only be associated with a single demographic group at a time. It is kind of hard to be “35 – 44″ and “45-64″ at the same time. The problem with this is that if your interests and opinions differ from the majority of your “Group”, then you have essentially no input.

An individual’s opinion can be represented many times by being affiliated with multiple communities.

Choice vs. Defined

People choose to be a member of a community or not. It is their choice to engage and be heard, or not.

As a demographic statistic, you are defined by a characteristic that has nothing to do with your thoughts or opinions, and you have no option but to be affiliated with a specific group.

Engage vs. Research

Companies are looking for information from groups. Gathering information from a community is a rather straight-forward, and cost-effective process. Simply join in the conversation the community is already having. A company can just listen, and obtain significant understanding. Actively engaging in the community conversation yields even more benefits.

Getting information from a demographic group is a tedious and expensive process; focus groups, surveys, etc. Once you gather the data and process the information, you only have a snapshot in time, that is probably already out of date. To maintain current data, the process needs to be repeated over and over again.

Emergent vs. Stable

Communities are emergent, meaning that they form spontaneously as necessary, and disappear as the need diminishes. This means that when you find a community, it is by definition active, full of energy and ideas.

Demographics are nice and stable. Men are men, women are women, a nice straight-forward way to collect data. IMHO this leads to a false sense of security about the information you collect, because there is an underlying premise that stability connotes meaning, and therefore is good.

Current vs. Unknown

Because of their emergent nature, communities inherently deal with what is current. Discussions are about current events; conversations address current issues.

A demographic group has no center, so there is no focus of discussion, or even a discussion for that matter. The group only exists in the data tables of the demographic researchers. The only conversation among the “group” is one that is forced and artificial, in settings like focus groups. So what do you really know?

Conclusion

This all reminds me about the old joke that asks why do dogs lick their (well you know) …because they can. Why do companies collect demographic data … because they can, not because it provides the best insight.  I believe that all the nice structured data collected in demographic research provides a false sense of security to company decision makers, and ultimately does a disservice to the company’s stakeholders.

Engaging with communities is new, it feels soft,  non-analytic and not very comfortable to company decision makers, but in the long run it will lead to better decisions and yield better results than over-reliance on demographics.

Thomas Vanderwal just posted his long awaited commentary on Microsoft Sharepoint. It is an intriguing and insightful reporting of what Sharepoint users have reported to him over the past couple of years. In a nutshell, Thomas’s final commentary is that Sharepoint does some things well, just don’t call it social software. In his own words:

SharePoint does some things rather well, but it is not a great tool (or even passable tool) for broad social interaction inside enterprise related to the focus of Enterprise 2.0. SharePoint works well for organization prescribed groups that live in hierarchies and are focused on strict processes and defined sign-offs. Most organization have a need for a tool that does what SharePoint does well.

The post prompted a rebuttal from Bil Simser. Bil’s main counterpoint is built around the idea that though Sharepoint is not excellent in all areas, it does integrate all of its functions, which is a plus. In his words:

SharePoint is a lot of things and like a lot of “suites” it does a lot of things pretty good. Some pretty good, some great, some not so great.

Bil goes on to discuss how a best of breed approach only shifts time and expense over to the integration side as opposed to initial development of an overall enterprise system.

Focus on Requirements Definition

The underlying thread in both of these posts is that understanding and delivering user needs and requirements is paramount in delivery of any system. For any organization to begin a conversation about what information technology it needs with a discussion of the technology itself is a recipe for failure. The discussion of solutions must begin with a clear definition of business objectives. The discussion of technology should be the last link in the chain of conversations.

Maybe Hugh says it best:

In my mind Decision Due Diligence has more of a personal feel than you get from an overall network analysis. The process specifically accounts for the emotions and behaviors of the players, or nodes in SNA jargon, along with the actual mechanics involved in passing information between nodes. So the importance of links, from a Decision Due Diligence perspective, is to a large degree self-contained. On the other hand the importance of links in SNA is a function of where the link exists with respect to other links.

Upon reflection, I think the two approaches are perfectly complementary. If you can understand the dynamics of a specific link as well as how that link fits into the larger system, you have a powerful tool for understanding information flow within an organization.

As I was working on this post, the term Social Connection Analysis (SCA) jumped into my head, and I think that is the right label for the concept I am addressing  above, and the thing I was trying to address with @valdiskrebs in the twitter exchange you see to the right. Where the core of SNA is understanding the aggregate of all the connections in a network, the core of SCA is understanding the specifics of any one connection within the network.

From the article:

Social-networking analysis, or SNA, focuses on the mapping and measuring of flows of knowledge and information between people, groups or organizations. In other words, it’s a sophisticated and useful evaluation of the company grapevine.

If you think the approach discussed in the article may be worth looking into further, you may also want to look at our approach.

Sometimes I get ideas in my head and I can’t tell if it is something really cool or just the looney thoughts of a somewhat delirious Dad. So you have been warned. Read on at your own risk…

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It has occurred to me that when we typically try to depict, or model, networks and relationships, we put the majority of the data in the nodes. And very little of the data in the connection.

The first problem with this model is the assumption that each node is uniform and the same from any perspective. In reality every node is unique depending on your perspective. Does anyone believe that they are seen the same by their spouse as they are by their Boss or by their drinking buddies? Of course not. So how do you represent this differently.

I suggest that there is a core amount of data that does belong in the node, but the bulk of the data belongs in the connection itself. This becomes shared data between the two nodes and unique to the relationship. Graphically it may look something like this:

Obviously this concept still has a lot of development, but if you made it this far, congratulations, you must be one of the few that finds stuff like this fascinating. Drop me a note. I would love to talk about this.

• Public timeline
• Everyone you follow
• Any sub-set of people you follow!

In other words you can create very specific meme trackers based solely on the people you are interested in listening to. Here is a screenshot of a specific subset (called a Tribe in MicroPlaza) that I have set up.

This tribe is called “Social Enterprise”, where I follow, among others, @monkchips and @rhappe. You can see here that they have each recently tweeted with links.. This particular screenshot has the results sorted by time, but you can also sort by popularity.

This is an early beta release and there are a few UI issues that they are working on, but I expect those to be cleared up shortly. My understanding is that they opened up for public beta today. Try it out and let them know what you think.

As for me I plan on using it heavily. It provides the filtering of the information stream I have been looking for.

Congratulations to my friends @olivero and @slgavin for putting this together.

Streaming applications are involved in communication, and are displacing the email models that typified Web 1.0. We all known how inboxes (a la email) work: people write an email, address it to one or more people (or groups, in some cases), and then send it off. The email infrastructure delivers the mail to those addressed, who receive it in their respective inboxes:

1. The inbox model is inherently private: the email is only delivered to a select group, and others cannot see it, even if that was desired.
2. The reach of the email is completely determined by the email’s author, and it is made on a piece by piece basis.
3. The ownership of the email shifts to the recipients when it is delivered: they have to delete, or file the email, which is no longer under the control of the author.

Flow apps work very differently:

1. Streaming apps are inherently open: the premise is that users create and share information in the open. This is about supporting open discourse.
2. The recipients opt into ’subscribing’ to certain people’s streams, so the decision about access to information is made by recipients, and this decision is general, not made on a post by post basis. I call this the ‘open following’ feature, meaning anyone can choose who to follow.
3. The handling of the streamed posts does not transition to the recipients: it is still under the control of the author. Posts can be deleted, for example, or edited. And posts do not have to be ‘handled’ by recipients: filed or archived. They simply slide from the top to the bottom of the stream, and march into oblivion, without the recipients having to manage them at all. While an archive exists, it is managed automatically by the streaming application. Collectively, these features add up to an anti-inbox model

He goes on to describe two Flow Apps, Twitter and Staction in some detail.

As I read this post I began wondering if the “flow” Stowe is talking about is similar to the “information flow” I discuss on this site. My first reaction is that the flow I talk about is different than what Stowe is discussing. Now this in turn makes me realize that I need to be clear about what I mean by “information flow”.

This means that individuals and groups find it easier to solve problems from scratch than to search for help from elsewhere in the organization. So even if someone else has already solved a problem, or has significant insight, there is no effective way to find and engage them.

Reducing information viscosity is what Decision 3D is all about. The process of looking at tools and behaviors in an organization will help to optimize the flow of information to the people that need it. If, and when, connecting with others, that can help you solve your problem, is precieved as being easier than reinventing the wheel, organizations will see significant productivity gains.