InetSoft Participates in DM Radio Broadcast on Data Visualization

This is the transcript of DM Radio’s program titled “The Eyes Have It: Ten Reasons Why Data Visualization Rocks.”

Jim Ericson is in a meeting today so our usual co-host won't be with us but he sends his best wishes, and I am sure he will be checking the show out later.  But we have our very own Mark Madsen on the show today.   Mark Madsen of Third Nature is going to be our guest host along with Rich Penkowski of Deloitte.  We will also be hearing from Doug Cogswell of ADVIZOR Solutions, Suzanne Hoffman of Tableau, and Byron Igoe of InetSoft.

So a couple quick notes, we tweet with a hashtag @DM Radio.  Please do not be shy. Go ahead, and tweet during the show.  I see a number of good tweets up there already.  Feel free to tweet your questions as well for our guests and we will try to get to those during the course of the program.  So with that, let's bring in our expert guest host of the day, Mark Madsen of Third Nature.  Welcome back to DM Radio.

Mark Madsen:  Hi Eric.  Thanks for having me back.

Eric Kavanagh:  Oh absolutely.  So what's your favorite reason for why data visualization rocks?

Mark Madsen:  I don’t know.  I think to start with, it's just the shift from data and BI as this kind of static model of presentation to something that’s interactive.  When you think about BI and the Data Viz that is embedded in BI, it's like graphs and charts. It's very static.  It's kind of like the way we used to do word processing where you typed your words, and then you formatted them after the fact.  BI is kind of like typesetting for numbers, and the interaction of the Data Viz tool changes that.

Eric Kavanagh:  I like that.  That’s a very good analogy.  So I mean there are lot of factors that kind of came into this to making that happen.  I mean we are seeing in the last few years visualization tools that are much more interactive, I guess one reason being that you can pull more data in that just sits underneath the surface there, and it allows you to kind of drill around.  I mean I think of things like slider bars, for example, are some of my favorite tools for being able to create a truly multidimensional view of data as opposed to just the sort of two-dimensional view of a spreadsheet or other kinds of things like that, right.

Mark Madsen:  Right.  Actually a spreadsheet is a great example.  There is a difference -- when you think about spreadsheets, there is actually a good thing about spreadsheets.  There, what you see is what you get.  If you change a number of formula on a spreadsheet it's there in front of you, and it changes; there is physicality to the data.  

And in the BI environments, these environments are very design, save, run, view models, right.  You get to design a query or drag some numbers on then you have to save and execute the thing, and then you get the results back.  It's the old sort of batchy interaction model.  It's very broken for somebody who is trying to work with a train of thought.  And I think that’s one of those things that’s changed.  It makes data, it makes information a lot more tangible to a person than the conventional tool designs that we have worked with.

Data Visualization in Sound and Music Cognition Research

Sound and music cognition research seeks to understand how humans perceive, process, and respond to auditory stimuli, including musical structures, rhythms, and timbres. This field spans neuroscience, psychology, computational modeling, and musicology, often generating complex and multidimensional datasets. Researchers collect information from sources such as electroencephalography (EEG), functional MRI (fMRI), motion capture of performers, acoustic analyses, and behavioral experiments. Data visualization plays a critical role in this research by transforming raw measurements into interpretable forms that reveal patterns, relationships, and anomalies that might otherwise remain hidden.

One of the primary uses of data visualization in music cognition is the analysis of temporal and spectral features of sound. Time-frequency plots, spectrograms, and waveform visualizations allow researchers to examine the dynamic structure of musical stimuli. For example, visualizing the harmonic content or onset timing of notes helps scientists identify perceptually salient features and investigate how listeners detect pitch, rhythm, or timbre changes. These visualizations not only aid in hypothesis testing but also provide a way to compare compositions, improvisations, or experimental sound stimuli across conditions, performers, or participant groups.

Neuroscientific studies of music perception heavily rely on visualization to interpret brain activity in response to sound. EEG and MEG data produce large arrays of voltage signals over time, often across multiple scalp channels. Heat maps, topographical plots, and time-frequency representations help researchers identify brain regions and temporal windows associated with specific musical events, such as beat synchronization or tonal expectation violations. Similarly, fMRI studies use volumetric and surface-based visualizations to show areas of activation during listening or performance, linking neural patterns to cognitive and emotional responses. Effective visualization is essential for detecting subtle differences in neural encoding between individuals or experimental conditions.

Behavioral data, including tapping accuracy, reaction times, and movement synchrony, are also visualized to study cognitive and motor responses to music. Scatter plots, line charts, and circular diagrams of phase alignment can reveal how participants entrain to rhythms or coordinate with other performers. Multi-dimensional scaling and clustering visualizations are often used to represent perceptual similarity ratings or emotional responses to musical excerpts, allowing researchers to see patterns in subjective evaluations. Such visualizations help translate complex perceptual phenomena into interpretable trends that inform both theory and practice.

Advanced computational modeling in music cognition generates additional visualization needs. Simulations of auditory processing, predictive models of expectation, or network models of rhythm perception produce high-dimensional output that benefits from interactive or dynamic visualizations. Researchers use 3D plots, network graphs, and interactive dashboards to explore relationships between model parameters, predicted responses, and empirical data. These visual tools support iterative model refinement and allow audiences to grasp the implications of computational predictions intuitively.

Finally, visualization is critical for communication and collaboration in this interdisciplinary field. Music cognition research often involves teams of psychologists, neuroscientists, composers, and engineers, each with different expertise. Visualizations provide a common language for sharing results, discussing hypotheses, and planning experiments. They also enhance publications, conference presentations, and educational materials, making complex datasets accessible to broader audiences.