Eric Kavanagh: Okay folks, welcome back to DM Radio. It’s time for the roundtable. It's always my favorite part of the show. And Mark Madsen, during the break there, had a good idea to find out where we are going in the next year or two in terms of data visualization techniques, technologies. So Rich Penkowski of Deloitte, I will throw it out to you first. What do you think is in the near term and what are you going to see in terms of innovations in data visualization?
Rich Penkowski: Well Eric, I think the biggest thing that’s going to be happening is, I believe, demand around in-memory computing to support visualization. That is the thing that’s going to be the hottest. I think as more organizations are becoming sophisticated around the kinds of questions they want to get answered from their data that they could not answer before, they are going to find that the limiting factor is computing power to actually combine all that data based on velocity, volume, and variety in a way to actually use the new visualization software products to their advantage.
So I firmly believe that that’s going to be, kind of, the hottest area that we see begin to take off. And I believe that a lot of major vendors in that space are making some very substantial investments and getting some pretty exciting products to market.
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New Data Visualization Functionality
Eric Kavanagh: Okay good. And let’s see, we will just go in the order of the guests that we had them in. Byron Igoe of InetSoft, what’s coming down the pike for you guys in terms of data visualization functionality?
Byron Igoe: Sure. I mean I think that the big thing in the near term is going to be a more seamless experience, transitioning between visual representation and other ways of viewing the data. Because, as everybody has been saying, there are different outputs for different purposes for different contexts to answer different questions. So I think unifying and making the experience easier for the analysts is going to be very important going forward.
Eric Kavanagh: Okay. And let’s see, Doug Cogswell, what do you think?
Doug Cogswell: Yeah. I agree with Rich on the in-memory capacity and its flexibility in how to link tables. I would also add in flight tracking and bookmarking so somebody does all this interaction. We need to be able to really accurately keep track of what they have done and let them go back to different selection states. The third area that we see is color management. We do a lot with color now but there is a whole lot more that can be done. And fourth, and certainly not last, is better integration with predictive modeling because people can visually explore data to a point but if you have 300, 400, or 500 fields, that breaks down. As a result, the predictive modeling can help people call out what really matters, which they can't just see visually.
Eric Kavanagh: Okay that makes sense. And let’s see. Suzanne from Tableau, what do you see coming down the pike?
Suzanne Hoffman: I see that collaboration is still on the forefront. I see that people want to share the information and they want to share it in real-time with Big Data.
So the speed of access to the information should improve while moving ETL processes, sort of, out of the way so it doesn’t have to be a bottleneck or a process that you have to go through to actually get to the data.
Speeding Up Data Visualization Access
The data should have to be retrieved from massive amounts and then be able to collaborate whether you are on a desktop, you are sitting in the corporate LAN or you are out in the field and you are on a mobile environment such as an iPad or an Android device. So I really think that whole group of processes or talents going forward are going to shape the industry.
Mark Madsen: Well I think you can take a lot of what was just said and roll it up into the ideas of user experience; making the tools work more the way people do than the way developers think people do. And that’s (53:31 inaudible) cognitive science research and data visualization, data research, and what the most appropriate thing is, but it also ties into decision support research.
There are ways to present data visually that can skew perceptions, pre-attentive perceptions one direction or another. So the simple presentation of information in two different contexts will lead people on the same datasets to two different conclusions ultimately making tools that are smart enough to either have a different set of guardrails, not just data guardrails, but guardrails on how the tool itself works to avoid those types of situations.
I think all of that stuff, we can throw under user experience and tool design because we aren’t building these things ourselves but rather companies like Tableau and ADVIZOR and so forth are building them for us.