Personalized Templates for Data Visualization

This is the continuation of the transcript of DM Radio’s program titled “What You See Is What You ‘Get’ – How Data Visualization Conveys Insight.”

Jim Ericson:  A couple of things driving visualization templates are very interesting. One is that to make this real, as I was saying, some of the vendors are starting to personalize these visualizations for various roles. 

So, for example, a company like GoodData, which is an analytic mashup service provider, launched a series of products a couple of weeks ago.  The products are designed for marketing professionals, sales professionals, and campaign management professionals. The tools are intended to help them visualize things that would commonly be in the realm of their jobs.

Then, we had other folks on the list like Intensity, that very much focus on sediment analysis, or Clarabridge, who focus on Natural Language Processing and things like that.

Others were very focused on marketing analytics, which I think is really kind of the tip of the spear for a lot of businesses now.  So they are thinking of visualization in terms of, “Okay, what am I seeing in terms of sediment? What does this segment represent?”

Quite frankly, all the vendors are creating these templates – even all the way up the food chain. SAP is doing this too. A lot of vendors are doing this. I just mentioned two.

These vendors are saying, “What do these representations mean? Let’s give our users sort of a semi organized approach with a template. We can’t give them exactly what they will use because every organization is different. But this way, they can build a role based on this simple power tool.”  

And, by the way, I think this new development is very much supported by the idea of tablets, iPads, and other visualizations.

Some of our guests today will explain to us how these tablets have changed the game a little bit in terms of what we are doing with real estate and visualizations.
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But, I think, the role based stuff really shows how we are taking notions like Big Data and making them pay off, especially for those that really fit a specific role.

Eric Kavanagh:  And that's very interesting. It does seem as though having some role based construct or mapping that you can apply to raw data would be extremely useful. Otherwise, where do you even begin?

With a lot of the stuff, that’s part of the challenge really. Certainly in data mining, when you are trying to visualize patterns and data, you know basically what you are doing – you’re identifying certain characteristics of the data and you plot them against each other using various algorithms to spread out different visualizations of stuff.

But if you don’t really know how to manage that process, you can spend a lot of time just kind of twisting in the wind out there and finding false positives – I would think, right?

Jim Ericson:  Yeah. And our friends over at Enterprise Management Associates mentioned before putting out some research that a lot of the Big Data usage is now operational – how to respond to customers, how to respond to business needs, etc.

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I think that's another thing that’s reflected here. If you think about sort of the ad-hoc exploration type of data visualization as it applies to Big Data, you can kind of imagine that pretty easily on the hands of a business analyst or someone who is taking kind of long looks and building models for algorithms and preferences and turns like that.

But, for example, the demo I saw was a visualization tool was being laid over some Hadoop cluster.  This was actually formatted in a way that you could say, “Okay, Mr. Sales Manager, here are the buyers of my lowest margin products. And here is how they compare by how much it costs to ship this stuff to them.”

So if you think about outliers and say, “Well, these guys are buying low margin stuff from me.  But there are also the most expensive people I have to ship stuff too, well, what can I do about that?”

And that's a very practical application of Big Data visualization with outliers. But it points to a very obvious business issue and a potential solution.

Eric Kavanagh:  Yeah, that’s a good point.

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