Archive for the ‘Visualization’ Category

  1. Healthcare .  In this visualization, GE took 500,000 records from the millions in its electronic medical record database, and calculated the out of pocket and insurer cost of a handful of chronic conditions by age.  One of the few both uses of and displays/designs of a radar graph, the visual changes as one increases and decreases age.  This also has a predictive analytics component in that it answers a user’s question “What if I develop a case of hypertension, what will that cost me when I’m 65?”  Like any good analytic, being able to see the data brings up more actionable and specific questions that this analytics doesn’t answer but the data set could.
  2. Digital Media.  This visualization is part of The New York Talk Exchange, a visualization project developed by the Senseable City Lab at MIT.  Perhaps the potential applications of the analytics are as explosive or more than the specific data they used in this case.  The analytic shows starting or sourcing neighborhood within NYC and to where their communications via the AT&T network were destined.  Users can see the frequency distribution of endpoints, and by comparison who was talking to whom geographically across sister boroughs.  Imagine creating a site map of your web site, application, team or workflow, and seeing the frequency of where the user, function, business process or capital goes next.  Predictive analytics can say: “If we invest in area x where is that capital, profit opportunity, or waste most likely to go next, and how does that change if I make another investment?”  Perhaps only this specific visualization is best for that type of comparative predictive analytic.
  3. Retail.  According to Well Formed Data, Sankey Diagrams and stacked bar charts informed this (4MB .pdf download) time series visualization of how medical journals in related fields merged into a cohesive ‘basket’ of journals in the emergent field of neuroscience.  While on the surface not retail related, it points to a very compelling—and as yet a visualization I’ve never seen produced— which would explain how specific products that drive volume and profit affinitize into specific types of market baskets.  Replace a) each journal with a specific retail product, which the user can color code at run-time for visualization, perhaps color-coded for on/off promotion, b) the eigenfactor of each journal which is represented by the weight/width of the specific line with the amount of profit or volume the item produces—again the user can choose profit or volume or some other measure at run time, c) the ten or so portfolio of ending blocks of journals as specific types of market baskets, and d) the breadth of starting lines moves from medical disciplines to aisles or departments in a store.  In effect what you have here as a decade long time series one has compressed into a single, specific shopping trip.  Data can include many trips, a single store, one day or years of data.  The predictive angle is to be able to answer questions like “If I promote this item, does it move away from its core ‘7 items per basket, quick trip’ basket into a ‘destination item weekly stock-up’ basket?”  One can also look historically how different store consumers shop categories, volume vs. profit items, and more.

These predictive analytic visualizations start with healthcare as the least complex and become more complex.  Depending on how frequently the user needs the data updated, the amount of data (in the digital media/telco example clearly tens or hundreds of gigabytes), the processing (in the retail example, calculating eigenvalues in an analytic that could be analyzed hourly, especially for promotional out-of-stocks, for example), and the speed required means a predictive analytic visualization is not something to try at home with an off-the-shelf database platform and hardware.

Any lover of 1980’s music (or anyone who listens to the radio) can work their way through this flow chart.  Guessing what’s going to happen tomorrow or next year looks a little more like this.  What they have in common is movement is iterations of events occurring in time represented graphically.  For data visualization, this continues to be a struggle, how can one show particular variable impacts without a video?  There may be a long wait for improvements toward ‘the perfect dashboard’.

Why is this important?  One example is the Federal Stimulus Package of 2009 has a significant focus on workforce development.  The current approach might surprise someone not in the field.  The granularity in working with individuals is heartening: student information system users want to be able to watch students (in this case workers, often in a community college or other re-training program) longitudinally.  Imagine a graph auto-scrolling to the right showing lines for income, education, hours working per week, employment or other benefits received, language skills—then add lines for macro events such as employment rates in that county, state or country, and other data.  While a more quantitative analyst might want to group students together to look for trends or who are leading or lagging indicators, the individual educators, program managers, Department of Labor professionals and others want to be able to see time passing—a dashboard ‘case history’ or ‘life story’ of helping an individual succeed in transitioning from the old economy to the new, from an older life to a newer one.

How will it look?  While there are amazing folks out there doing static knowledge visualization, this is different, and it’s harder to find folks doing it well.

–          There’s nothing wrong with using the old clock metaphor.  We see clocks so many times that it’s surprising more analysts and presenters don’t leverage the icon burn-in.

–          Traditional bar graphs can be functional, but again it forces on to move their eyes and mind to the right to express the flow of time.  Tufte made this representation famous, while the time aspect is more similar to bar graphs where one imagines time progressing, we can see relative changes in space and other variables in time.

–          Proper respect goes to my old company MicroStrategy for using the latest Web 2.0 technologies to produce cool dashboards like this (including a longitudinal scroll bar of sorts in the upper right) which, because of their large partner base, are commonly seen in analytic software now.

–          Imagine a static print view of the image seen in this fun video at about 1 minute 8 seconds in with the cascading, serial flow from the printers.  The cascade concept, whether here or elsewhere, leverages the natural burn-in from our experience with gravity and entropy: things tend to go down, and they tend to move from the start in any direction they can flow (in a graph, the y axis is symbiotically a wall, and moving across x is the only direction anything can flow).

–          We’ll probably have to rely on manually scrolling for a while, as one can do at the bottom of this dashboard.

–          The really nice solution can be seen here at 2:15 in the video (even better with tracers at 3:20) which is brought to you by, guess who, Google.