VisualCue is a data visualization platform: any strides we make in operational intelligence, analytics or plugged-in management come as a direct result of seeing your data in a new way.
And make no mistake: our Tiles, Mosaics and Cues are definitely a new way to look at data. Dynamic icons and colors representing thresholds and KPIs grouped in Tiles which are then placed on maps or diagrams is as far removed from a bar chart as moth is from an eagle.
And with any new technology, there is always lots of development and constant refinement to make it the best it can be.
To that end we have an entire team of talented software developers and data specialists constantly working to make VisualCue even more amazing. Today we’re happy to give you a sneak peek at some of the most impressive updates and new features we have coming to VisualCue in our latest 2.7 release.
by The VisualCrew
Think about what a heartbeat is for a second. It never stops (until the day it does, but for more data on that check out last week’s post), it’s the constant background hum to everything we do.
Social media can be a lot like a heartbeat for the entire planet. It’s always on and if for some reason it ever did stop you’d feel very real fear that the world had ended. It’s the constant background noise we’ve all become so accustomed to: every second we send out 6,000 Tweets. Every minute, we upload 300 hours of video to Youtube. Every day over 58,000,000 photos are uploaded to Instagram. That’s a lot of data.
And of course wherever there’s data there are amazing ways to look at it.
“Big data” is a catchphrase in both the tech and business worlds. Referring to the vast amounts of data generated by connected technology, big data is a tool that many businesses can use to make their advertising and other marketing efforts more effective. Data and using data for analytic purposes is not new, but what is new is the vast amounts of data now available to us, and that data has come available largely due to the Internet of Things (IoT).
The more devices and machines get connected to each other and the Internet, the more data is going to flow through those devices into the pool of “big data.” But what is the relationship between the two? Are they two sides of the same coin, as Tamara Dull of SAS implies, or are they connected but different? While they may not be the same thing, the Internet of Things and big data are definitely connected.
We’ve mentioned before about how much data we, as a species, seem to be collecting these days. There’s data out there for just about everything you can imagine from Mozart to movies.
This week we were back on one of our favorite sites, FlowingData.com, and got caught up in all of the data surrounding a topic that, while decidedly macabre, is nevertheless on everyone’s mind every now and then.
Lock the doors, bolt the windows and gather your courage because this week we’re looking at just what a dangerous place the world can be.
When most people see a VisualCue Tile or Mosaic for the first time the very first thing they notice are the colors attached to each of the key performance indicators or cues in the Tile. Our standard stoplight pattern of red, yellow and green is almost universally understood as it relates to poor, dangerous or adequate levels of performance.
But these colors can mean so much more. In fact, they are arguably one of the most innovative, valuable aspects of the entire VisualCue platform.
On Fridays, we generally share our favorite data visualizations from the past week.
Today we’d like to break from tradition, but we have a fantastic reason for doing so.
For those who don’t know, VisualCue’s main offices are in Orlando, Florida. Lately this region has seen a huge spike in technology patent applications, and it caught the attention of local media. We were interviewed along with our patent attorneys at Lowndes Drosdick Doster Kantor Reed, P.A. to discuss what it means for VisualCue and the entire Central Florida region.
Take a look at the business intelligence, management or operations solutions provided by IBM, Domo or Tableau and you will discover a trend. While each claims to be totally different and unique, they all drill down to basically the same thing: rows and columns. Endless reports. Maybe an area graph. At the very best it’s a dashboard that gives you only high-level information.
It’s a sad fact: if you want to get to the actionable data in any large data set you will find yourself being squeezed into these silos of data. A report for this key performance indicator, a separate report for another. You are left with the unhappy task of attempting to put the puzzle pieces together to answer even the simplest questions surrounding “what can I do to help this employee’s performance?” or “why is my fleet’s mileage up this month?”
This problem is deeply rooted into the very fabric of modern data consumption, and it comes from one main disconnect between how we think and how we are currently looking at data.
Here’s an interesting fact for you: most of the world’s population lives in cities and will continue to do so in to the future.
According to Reuters, “In 2010, a total of 80.7 percent of Americans lived in urban areas, up from 79 percent in 2000. Conversely, 19.3 percent of the U.S. population lived in rural areas in 2010, down from 21 percent in 2000.”
That makes it incredibly likely that you are reading this article right now from one of those wonderful, crowded, bustling cities. As such, this week we are proud to present a data visualization tribute to city folk!
We aren’t the least bit surprised that the advent of streaming data sources has seen an equal rise in the number of analytics solutions claiming to be just as fast.
But recently we also came to a realization: they aren’t. Data is coming into these programs so fast and from so many different sources that by the time any kind of analysis is completed the moment has passed- in essence, you can only work with yesterday’s data.
We were wondering how you would put the “real” back in “real time,” in other words what would it take to create an analytics engine that anyone could understand as fast as the data itself changed?