Recently, ran across DBPedia, an organization that is turning Wikipedia entries into RDF, the language used for the semantic web. DBPedia has actually has downloadable datasets based on Wikipedia that are available today. These are datasets that can be queried with existing tools and linked to other datasets. Wow!

Even if you aren’t a Wikipedia fan, this is really a big step forward for the enterprise. Think about the amount of knowledge that exists in your organization that isn’t captured, but is critical to your operations. It has always been a big pain to try and sit down and do formal knowledge engineering. However, most people are comfortable enough with a Wiki to sit down and start typing. For a small organization this might not be such a big deal, but for a larger enterprise this could provide some very useful information.

The first time your Director or CXO can make a financial decision based exclusively on the information from your company wiki, it will have proved its worth. Until then, it is just another trendy tool. The work that DBPedia is doing is an important step in making this a reality.

One thing I appreciated in the article was its realistic tone, which recognized the existence of shadow systems and the role that they play in the real world. While the article focused on spreadsheets, this doesn’t mean that they are bad and that centralized CRM is good (as a matter of fact I might argue the opposite in many cases). The real issue being discussed is the risk that occurs when information architecture doesn’t match the needs of the organization.

Looking Globally

For many low-importance, one-off operations, a spreadsheets is fine. It can be shared easily, and usually owned by one person. However, what normally happens is that someone sharp (and usually not someone in IT) decides to do try to build some infrastructure around a spreadsheet without considering the consequences. It is quick, cheap, and easy to do, to a point. The problem is that the point when a spreadsheet becomes unmanageable is often well after the point where an organization depends on its output to function effectively.

For any system, an information architect needs to consider:

• Cost
• Effectiveness
• Visibility
• Traceability
• Mangageability
• Quality
• Auditability

Furthermore, the situation needs to be considered from a forward-thinking perspective. That is, we need to try to understand how the landscape is going to look a few years down the road, and to make our systems flexible enough so that we don’t put our organization into a bind if we are wrong.

Spreadsheets are normally built only considering cost and effectiveness, something that is only discovered after time has gone on. It is often the case that a system would be much more effective living in a database, BI tool, or custom software application when considered over time.

The site is a comprensive look at a huge number of different ways to visualize information, ranging from the artsy (such as the graphic facilitation) the the mundane (pie chart) to the technical (entity-relationship diagram) to the silly (knowledge map).

The UI is very good, and examples of each different type of visual tool, and a nice overall classification of the different types of visual tools. This could be very helpful if you are stuck trying to figure out how to communicate information but don’t know exactly where to start.

Take a gander …

It would be incredibly boring for Google simply to replicate Excel and Word in a web format, unless you are an HTML groupie. However, there are some very, very interesting features that I think really turn the traditional office application on its ear. The first thing that caught my eye was the Google Lookup function, which allows one to incorporate search information dynamically into documents. The second thing was the Google Finance function, which allows financial information to be leveraged as well.  The third thing was the ability to embed portions or entire spreadsheets into a blog or web page.
Very cool stuff, and very interesting results. One could imagine this type of thing being leveraged with Froogle, Maps, or other service, within a document, presentation, or spreadsheet. Low cost, high reward stuff. However, there are some ramifications with using this type of information. For example, the spiffy new spreadsheet you put together for your boss could be modified by outside influences (like a Google Bomb).

Worth a look, at least.

It is an interesting idea, where you …

1. Upload your dataset to Swivel.
2. Share your data with other users.
3. Chart your data, making it available on the web.
4. Compare your data with the datasets from other users.

Interested? Here is a reasonable example and a not-so-reasonable example of what Swivel can do.

It was a little bid odd to me, at first. Why would I want to provide my data to a web site so that I could look at it the same way that I can on my own computer? Well, I can think of a couple of reasons …

Some Analysis

First, you can share your data with anyone who wants it. This sharing takes two parts, display and anaysis. You can display your data on the web, for everyone to see. Second, you can combine your data with the data that other people have uploaded and perhaps learn something that you didn’t already know.

Now, sharing might be good, and it might not be, depending on your point of view. An enterprise might want to keep it’s information secret, and that makes sense. However, an interesting thing about most data is that it is subject to the network effect, big time. Two unconnected data points might mean something, but you can’t really be sure. However, a hundred data points indicate a trend or a correlation.

Second, you can use a community to learn more about your own data, as well as your correlations. At the bottom of each page, there is a rating that gives a group evaluation of how related and comments about how useful the comparison actually is.

Overall

A site like this could be useful, especially if there could be a concerted effort to provide good information, such as the census, demographic, and geographic information from the US government. It is still in its very rough stages, and it hard to say how much value will come out of combining disparate data sets. Also, I security, information quality, and analysis will be a huge issue here. Still, an interesting idea.