Memorable Data Visualizations

Memorable Data Visualizations

There is something about the science of data visualization that absolutely captures our imagination. In fact, we used the science of sight and the power of human pattern recognition to create VisualCue in the first place. We saw that there was  problem with spreadsheets and the standard graphs and charts that were used to visualize them- they weren’t very memorable or easy to understand.

So we used icons and colors to present that same information in a different way. After the very first VisualCue experiments in 2007 we discovered a very interesting side effect of our presentation layer: people remembered the data more easily after having seen it in VisualCue.

This week we stumbled across a research paper written last year that finally sheds some light on why.

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Augmented Data Visualization

Last Friday we wrote about augmented reality for pretty obvious reasons: seems everyone out there is playing Pokemon Go and thus are thinking about different ways that technology can interact with the world around us.

True to VisualCue form we did a little digging after we wrote about it on Friday and are happy to present the results of a weekend of research and reading into exactly how the worlds of augmented reality and data visualization are linked.

We’ll go into greater detail below but just for now let’s put the main point first: the successful blending of augmented reality and data visualization is going to depend on mitigating both visual and information overload.

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The Do's and Don'ts of Dashboards

In the April 2016 issue of Computers in Libraries author Susan Gardner Archambault brings up a number of interesting points about the use of data visualization in one of mankind’s most storied and worthwhile inventions: the library.

When most of think about libraries we tend to think of shelves of books and people telling us to be quiet no matter how silent we were being.

This article, however, not only sheds light on the modern library but contains useful information for anyone, even those not currently contemplating starting or improving their own library, with some of the major do’s and don’ts of what goes into a good dashboard. And who better to educate us than those who work in the library?

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The Death of the Dashboard

VisualCue is a friendly company. Honestly. At heart we are just a bunch of data enthusiasts on a mission to help the world use information better.

But here’s the thing- as much as we might like to be friendly and get along with everyone there is an undercurrent in our mission statement. We believe that the way the world is currently using data could use a little work- if everything were perfect there would be no reason for us to even be here.

We believe that we are at the forefront of progress at VisualCue. And sometimes progressing into a brighter future means leaving some things behind. And that can be a painful conversation to have- eye opening, yes, and necessary, but nevertheless uncomfortable because it admits that we aren’t perfect at something important.

We’re here to help you with that conversation.
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9 steps to a successful data analysis

In 2o11, Peter Huber wrote a book about what we (the collective we, or the we of data analysis enthusiasts) have learned about the science and art of data analysis over the past 50 years.

To anyone who is interested in data visualization we highly recommend reading this book. Over the course of about 200 pages Huber carefully outlines exactly what data analysis is, what the current challenges are and how they can be overcome.

One of the aspects of this work that we find particularly interesting is the fact that he gives us, in great detail, a road map or checklist of activities that should be completed, in a particular order, in order for any data analysis project to be successful and meaningful.

Allow us to examine Huber’s checklist and provide our own insights into how we can help.

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VisualCue 2.7 Release Notes

It’s one of those skills that no one seems to fully appreciate and yet could solve so many problems: listening. If everyone could listen, truly listen, to each other and learn from each other just imagine what we could accomplish.

What does this have to do with a data visualization and analysis platform like VisualCue?

Plenty. Because VisualCue is a platform we are constantly shaping, changing, evolving and improving the formula to make your VisualCue experience better and provide the single best analysis tool we can.

How do we know what to change and what to improve on? Simple- we listened to you. Here are the changes you requested that you will see in VisualCue 2.7- coming June 30th.

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Visual Data Exploration

Visual Data Exploration

In the most recent issue of the Harvard  Business Review we saw an article that we just had to read: Visualizations That Really Work.

Being pretty obsessed with all things data visualization everyone in the office read the article and we were immediately impressed with everything that we read, but we also had a few questions and insights of our own that we could share.

We have been at this data visualization game for pretty much one reason: we realized a long time ago that using data to make smarter decisions and improve your business was going to be a key competitive advantage in the future. It’s just nice to have some third-party confirmation that we were right all along.

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Can you eat big data?

It all started with an article we found about robot shepherds on large Australian cattle farms.

As with any new implementation of technology, the cattle farmers had a problem. According to the article, on a particularly large cattle farm the cows were too spread out for the ranchers to adequately monitor them and they were losing many animals to injury and illness as a result.

The robot shepherds used advance thermal imaging and other methods “to detect changes in body temperature and walking gait” in an effort “to improve the quality of animal health and make it easier for farmers to maintain large landscapes where animals roam free” according to the robot’s designer.

That got us thinking: what kind of data does a cow generate?

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Asking Smart Questions

It’s one of those clichés everyone knows: you ask someone wise for answers and they respond with “you should first learn to ask the right questions.”

This might cryptic and confusing but, believe it or not, there is wisdom in learning to ask the right questions: asking the right questions can lead to the right answers. But if you don’t have the right questions then you could be spinning your wheels endlessly searching for the answers to questions that might not even lead to the most benefit.

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The End of the Lone Wolf

It’s a pretty common trope in media today, probably because it’s such a romantic figure: the lone wolf. Just thinking about it conjures up sentiments of independence, ferocity, determination and power. The lone wolf is master of all he or she surveys, successfully carving out a place of dominance in an unforgiving wilderness.

What a lot of baloney.

Anyone who’s read Kipling’s The Jungle Book will know that the strength of the wolf is in the pack, and that without their pack a lone wolf would starve before too long.

But the idea of the lone wolf is even more pernicious when you take that mentality and place it into a business scenario. Yet, unfortunately, that is precisely what we are creating with sales representatives when we evaluate, judge and compensate them based on only one or two KPIs. An entire sales force of lone wolves, each of them slowly starving to death.

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