InetSoft BI Webcast: Data Mashup and Geographic Mapping

Below is more from Information Management’s Webcast by DM Radio, “The Last Mile: Data Visualization in a Mashed-Up”. This Webcast was hosted by Eric Kavanagh and included BI consultants William Laurent and Malcolm Chisholm, and InetSoft's Product Manager Byron Igoe.

Eric Kavanagh (EK): I am glad you brought in that concept of semantics. Let’s drill into that very quickly because it seems to me in the ideal world, we’re just going to dream here, the ideal information architecture, it seems to me that you would have some kind of a semantic marshalling area to help manage your meta data, right?

William Laurent (WL): Maybe or maybe not. The point I was making is that, again, not to keep dwelling on just geographic underliers, but people look at something and they know what it is. They know this a street, this is a house, this is a church, this is a park. So that information by default, you’re able to get that information without a lot of heavy lifting, or get that semantic consistency and meaning without heavy lifting in data governance processes, that sort of thing because you’re representing it visually. That’s the point.

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EK: Malcolm, let’s bring you in here as well. What do you think of our ideal scenario?

Malcolm Chisholm (MC): Well, I think I would go a little further actually. One of the things I have been wrestling with is taking what Bill said about GIS, being the most common application where geography is the fixed frame of reference. But even in financial services or even doing data administration, data governance, for instance, we need to go further so we need to do something about developing a fundamental canvas for a mashup. For instance, I’ve been doing some data discovery work recently, and I’d love to be able to set that into some kind of mashup, but I need something that visualizes the production data landscape. Maybe it could be by subject area, I don’t know.

I think you have to move beyond GIS. The thing about geographic mapping is that we’re all familiar with it. We all know what it is. There isn’t a semantic challenge to understanding it. But let’s say if I was to create the data topography of the production landscape. Then I am going to have to create some kind of canvas and communicate to people in a fairly clear way what that is. Subject area is probably a good way to go. Then I can start to go overlay things on it like applications, servers, databases, flows of data, whatever. I think a big challenge in that, however, I think that is probably something that is going to be met. I think that there is tremendous demand for it.

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