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Comparing jQuery Grid Plugins: Masonry vs Isotope vs Packery vs
Gridster vs ShapeShift vs Shuffle.js
Last updated on M a r c h 2 2 , 2 0 1 6by T w a i n T a y l o r
Grid-style layouts came into the spotlight when Pinterest grew popular. They became a hot
favorite in the UX community for displaying photography portfolios, thumbnails of products on
ecommerce sites, and article snippets on blogs. While they are primarily used to display text and
image content, there’s a fair share of products that have adopted these plugins to build data-
driven dashboards. Here are two examples – Geckoboard, and Freeboard – which use the grid-
style or modular layout:
(Related Read: 5 Dashboard Design Trends to watch out for)
If you’re building a dashboard, and are considering a grid-style layout, there are numerous
options available. It would take a lot of searching, browsing, and note taking to research the entire
gamut of plugins. However, to save you some time, I’ve created a comparison table of the popular
jQuery plugins that do the job, and do it well.
Features Masonry Isotope Packery Gridster ShapeShift Shuffle.js
IE8+ IE8+ IE8+ IE9+ IE9+ IE7+
Drag & drop
Licensing MIT Commercial Commercial MIT MIT MIT
3824 2511 144 312 45 17
Github forks 1283 965 125 688 264 50
Github commits 375 727 324 253 397 132
To summarize the table, Masonry was the plugin that started it all, and has inspired many other
plugins out there. After the success of Masonry, its creator @desandro, then added to the list
Isotope, and Packery, which have additional features like filtering, and drag & drop. Together,
these three plugins rule the roost of grid-style plugins.
However, if you’re willing to explore other options, there’s a long list of plugins both open source,
and commercial ones to pick from. I’ve listed three of the better plugins – Gridster, ShapeShift,
and Shuffle.js in this comparison. They make excellent alternatives to the top three.
About which plugin to finally choose, if you’re building an enterprise app, you need reliability first
and foremost. In that case, Masonry, Isotope, and Packery have the biggest community, and won’t
leave you stranded with issues. If you’re more concerned about a particular feature like having
both filtering and drag & drop capability, go with one of the other plugins like Gridster, or
ShapeShift. But remember, Gridster isn’t responsive.
If you want to leave no stone unturned in your search for the perfect plugin, here’s a list of
plugins, both free and commercial, that didn’t make it to the comparison but are worthy of
mention – Nested, Mason.js, Cube Portfolio, Megafolio, Gridalicious, Woomark, Smart content
placer, and Freetile.
As you can tell, it’s a crazy world out there. There are plugins galore, each with its own take on
grid-style layouts. It’s not an easy task to choose one plugin that will fit your project just right.
However, I’ve laid out the top ones in this post, and I hope they’ll cut short some time from your
Chime in with your comments on which is your favorite plugin, and why.
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3 Insanely Great Dashboards from Recorded Future – Predictive
Analytics Part 4
Last updated on F e b r u a r y 2 7 , 2 0 1 4by T w a i n T a y l o r
Over the past few weeks we’ve been discussing predictive analytics at length. We started with an
overview, and then went on a tour of 9 unique businesses that are leading innovation in predictive
analytics. So far, we’ve just set the stage for this and the next post, where we take a focused look
at the visualization methods and concepts used in predictive analytics products today.
In this post, we do an in-depth review of three dashboards created by Recorded Future, who we
previously mentioned as being the poster child of predictive analytics. When it comes to
visualizations, Recorded Future has some of the best dashboards around. Let’s dive straight into
the three dashboards.
1. Dashboard comparing online mentions of three companies
The first dashboard compares 3 companies – Apple, Google, and Microsoft – based on the
coverage they’ve received from a multitude of sources on the internet. It picks out forward-
looking statements about these companies within articles, and plots them in a chart. While it’s a
marvel that they can extract such unstructured data, and plot it accurately on a graph, it only gets
more interesting when we explore the kind of visualization they’ve used.
It starts with a ‘Customize view’ option which allows users to customize the chart by a variety of
pre-set filters like product, country, language, and a host of other criteria. This enables extremely
targeted research. Further, you can segment the chart by positive or negative sentiment based on
the type of analysis being conducted. But it doesn’t stop here.
There’s an interactive legend which can be moved across the chart area, making you want to play
around with it. Hovering over a company refreshes the chart to show results only for that
company. Very intuitive, and informative. Finally, being a bubble chart, the legend doesn’t leave it
to the user to guess what the size of the bubble means, it clearly indicates that the small bubbles
are a single mention, and the bigger ones are 3 mentions. This adds a lot of perspective, when
viewing the chart.
At the bottom of the chart, there’s an interactive scroll bar which can be moved to set the
timeframe for the chart. Just above the scroll bar are two calendar thumbnails to enter an exact
data – that’s very considerate for those users who are particular about exact date ranges. In the
background of the scroll bar is a lightly shaded area chart that represents the total number of
mentions or data points, and how it’s changed over the years. This packs a lot of information in a
dense manner, allowing for a very high-level analysis at a glance.
At the bottom-right corner of the chart there’s a small, easy-to-miss speedometer gauge that
shows what percentage of total data points are being seen currently. Hovering over the gauge
pops a tooltip with the exact number of data points being seen, specifying the percentage
number. That little piece of data puts the entire chart in perspective.
Now, coming to the main chart itself, it’s divided into three stacks, one for each of the companies,
which makes comparison very easy. Users can easily see which companies get more mentions
over time, and compare them across different time periods. In the background of the bubbles is
an area chart showing the number of mentions over time, similar to the one in the scroll bar, but
bigger. It’s also easy to spot changes within a particular year. The bubbles plot three parameters
of data – time (year of mention), quantity (number of mentions), and entity (Apple, Google, or
The bubbles have interactive tooltips which appear on hovering over them. This serves to quickly
scan all bubbles and probe deeper into those that are interesting. When a particular bubble is
clicked on, it pops up a snippet card with tons of information for further research. The card starts
with the date for context, is titled with the main phrases from the article, below which is an
excerpt from the article. Clicking on ‘See references’ shows another card with more specific
information like the country of the mentioning website, and exact URL. At the bottom of the card
is a ‘Related Questions’ section which suggests researching on parallel, similar topics, which can
be an interesting tangent from the current topic. This type of layered presentation of information
is key to designing great dashboards. It doesn’t overwhelm users with all the information at once,
and gives them the flexibility to choose the route they’d like to take. Much like the choose your
own adventure novels we grew up reading.
This seemingly simple dashboard has behind it the workings of intricate algorithms that crawl the
internet to make sense of unstructured, and semi-structured data. Naturally, the visualization
methods used to create the dashboard are many and diverse. The chart types used are bubble
chart, area chart, and stacked area chart, and speedometer gauge. It also employs features like
interactive legend, scroll bar, solid coloring, tooltip, and cards. The dashboard exemplifies how to
use many different visualization elements, and yet keep presentation simple and elegant. The
data sorting features as present in the ‘Customize view’, interactive legend, and scroll bar give the
user tremendous control over how much and what type of information is being displayed. The
varied chart types, and interactive features used make the dashboard meet its objective of
facilitating open-ended, exploratory research. This dashboard is a stellar example of how to
visualize predictive analytics data the right way.
2. Map-based dashboard plotting Russian military activity
The next dashboard from Recorded Future plots similar text data from websites, but uses a
different approach – maps-based visualization. There are some similarities between this and the
earlier dashboard like the ‘Customize view’, interactive legend, speedometer gauge, and scroll bar.
These are a staple of Recorded Future dashboards.
There are a few variations between this and the previous dashboard. For example, the ‘Customize
view’ option now has options to present the data in the form of pie charts or heatmaps. While this
is a useful feature, this is one of the few details I think the dashboard gets wrong. What it calls pie
charts are actually bubble charts. Pie charts are made up of at least 2 data points, and are used to
show a breakdown of total data. The bubble charts used here show volume of data based on their
size, and visualize the mentions in a cumulative rather than comparative manner. Also, I’m not a
big fan of the heatmap feature in this dashboard as it yields little insight. Heatmaps work well
when there’s a large number of data points, which isn’t the case in this dashboard. But perhaps
on a more popular topic over a longer period of time, a heatmap may work well to get a bird’s-eye
view of the regions with most buzz.
Just as the previous dashboard, the bubble chart here uses tooltips to show a brief snippet of the
type of content it contains. Mousing-over a bubble gives the user an overview of the info it
contains, prompting the user to probe further.
If there are multiple data points within a bubble, clicking on it shows a drilled-down visualization
in the form of a line chart layered over with bubbles again.
Then, clicking on a bubble shows a card rich with data and links for further analysis. This is similar
to the card we saw in the previous dashboard, and is a staple of Recorded Future dashboards.
Next, we come to the zoom feature of the map, which is a default feature of Google Maps, the
mapping platform used for this dashboard. This is particularly useful to drill deeper into the most
active region like the former Soviet Union.
You’ll notice this region is densely packed with bubbles, and a mouse-over doesn’t work to get
glance through all the information contained in that area.
With a couple of clicks, you can zoom into the region and explore it at a granular level.
On the lower-right corner of the dashboard, we notice a useful ‘Not on map’ bubble which
includes all data points that couldn’t be plotted on the map. Clicking into it allows to explore
some apparently orphaned data. It may be that Recorded Future’s algorithms couldn’t find an
exact location to plot them on, or that there were too many locations to pick from. Only the folks
from Recorded Future can comment on this. However, what’s significant about this minute detail,
is that it could easily have been left out as it doesn’t fit into the pre-designed structure of the
dashboard like the rest of the data. But instead, realizing it can still contain nuggets of useful
information, it’s included in an unobtrusive manner. This is a significant factor when dealing with
predictive analytics. A lot of the data can be unstructured, or semi-structured, but that alone
need not disqualify it from being visualized. An inclusive mindset towards data is imperative
when creating a predictive analytics dashboard.
3. Bubble chart visualization of related terms
Both previous dashboards use traditional visualization methods like bubble charts, area charts,
speedometer gauges, and maps. This last dashboard, however, visualizes data in an unorthodox
form of bubbles that are randomly ordered and are linked to each other by connectors or nodes,
creating an intricate web of connected bubbles.
In this dashboard, the ‘Customize view’ box is key to the visualization. Clicking on it shows many
more dimensions of data than the previous two data points. This shows that the data being
visualized here is more unstructured than the previous examples, and a rigid visualization
framework can’t be applied to this data. You can start from something as simple as a ‘Country’
and ‘Person’ and get more complex visualizations by adding parameters.
The next most important part of the dashboard is the legend. With the numerous types of data
being plotted, it’s easy to lose track of what each bubble represents. The legend adds much
needed stability to this otherwise wacky visualization
Lastly we look at the bubbles itself, and they seem to be ready to get into a flurry of movement
when a cursor approaches them, repelling and attracting each other like restless little magnets.
This makes for a very lively visualization, and we can let the kid in us gaze in wonder for a while
before actually resuming our quest for insight.
Mousing-over a bubble highlights that bubble along with all the nodes and bubbles related to it.
This is where the exploration starts, when a user can view a term, and information related to it
like country, people, or any other parameter as selected from the ‘Customize view’ box.
Hovering over some bubbles shows an image, which makes for an even more visual experience.
While this dashboard scores high on visual appeal, it’s doesn’t immediately yield usable insights,
and can be categorized as experimental. However, these types of visualizations are becoming
popular, and are the new breed of visualizations stemming from the big data revolution. While
nobody seems to have figured out exactly how to put them to use, they can’t be discounted as
useless either. As we get familiar with unstructured, and semi-structured data, these types of
visualization could become mainstream, and change the way we consume data.
With that, we come to the end of this long, rather entertaining, and hopefully insightful post on
how Recorded Future is setting the bar for top-notch visualization in predictive analytics. These
are just three of a big list of dashboards in the Recorded Future gallery, which you may want to
check out. The gallery has a number of really interesting topics to explore.
Next week, we’ll take a look at how other businesses are visualizing predictive analytics data in
their dashboards. While Recorded Future is indeed the pinnacle, there are quite a few interesting
dashboards from other companies, which we’ll be talking about in our next post.
If you enjoyed reading this, be sure to check out the other posts in this series:
Part 1 – Predictive Analytics: No More the Way of the Analytics Ninjas
Part 2 – 5 Businesses on the Frontier of Predictive Analytics
Part 3 – 4 More Businesses on the Frontier of Predictive Analytics
Part 5 – Stripping Down the Gorgeous Sift Science Dashboard
Part 6 – 9 Ways We Use Predictive Analytics Without Even Knowing It
Stripping Down the Gorgeous Sift Science Dashboard – Predictive Analytics Part 5
Predictive Analytics: No More the Way of the Analytics Ninjas
5 Businesses on the Frontier of Predictive Analytics – Predictive Analytics Part 2
Data Digest – NYT Uses Real-time Data to Get Personalized
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Stripping Down the Gorgeous Sift Science Dashboard – Predictive
Analytics Part 5
Last updated on D e c e m b e r 2 0 , 2 0 1 3by T w a i n T a y l o r
Following up on the hugely popular previous post titled ‘3 Insanely Great Dashboards from
Recorded Future,’ here’s an in-depth look at a dashboard from another company doing cutting-
edge work in predictive analytics – Sift Science.
I’ve spoken in some detail about what Sift Science does in my previous post. As a quick reminder,
Sift Science helps e-commerce businesses fight fraud using the power of machine learning and
predictive analytics. They collect diverse data, glean signals from it, and assign a ‘Sift score’ to
every user which predicts how likely it is that a particular user is a fraudster.
Now moving to their dashboard, its purpose is simply to alert an e-commerce website owner of
suspicious activity, and prompt them to take action at the earliest. Let’s break it down, and see
how well the dashboard does its job.
Right up top, the dashboard has two sections – Summary and Action. The Summary section has
the most important information about the user in focus. What immediately catches the eye is the
Sift score in large font, made even more noticeable by the block of solid red color surrounding it.
What’s interesting to notice is the intentional use of a decimal for the score. This subtly helps
build credibility for the score in the viewer’s mind. Around the score is some vital information
about the user, which gives the viewer a chance to quickly assess the background of the user. This
information is downplayed to direct attention to the score. The Sift score is the most crucial piece
of information in this dashboard, and choice of formatting does justice to its importance.
This section prompts the viewer to mark the user as fraud or not based on the information in the
previous Summary section. The viewer immediately notices the apt use of colors red and green
for the buttons, and can guess what they imply with very little cognitive load.
When dealing with fraud, the time to response is of critical importance. In a time-sensitive
environment as this, having the Actions section right next to the Summary section makes the
dashboard actionable and intuitive. In fact, with just these two sections at the top, the dashboard
achieves its purpose of informing the viewer, and prompting them to take action. The viewer can
ignore the rest of the dashboard and still do what they came for. The use of colors, formatting,
and layout all serve to reduce the delay between analysis and action.
User Network section:
The rest of the dashboard elaborates on the Summary section. The next ‘User Network’ section
draws connections between this user and other suspicious users. This is at the heart of how Sift
Science works, trying to correlate all available data points to identify suspicious patterns, and
assign a data-driven, accurate Sift score.
This section is interactive, allowing the viewer to click on each detail, and view just the data that
interests them. The use of connecting lines makes this information easier to understand at a
glance. In fact, the graying of details that are not selected allows the viewer to gauge the level of
suspicion without even clicking through – the more the number of connecting lines, the more the
Suspicious Signals section:
If the previous section is a teaser on how Sift Science identifies suspicious behavior, this section
takes the hood off, going into the juicy details. The first two columns here show a list of data
points captured by the system. Viewers can click in for more details. This data is downplayed in a
gray background to give priority to the next column.
The next and more colorful column is the closest glimpse we get of the secret sauce Sift Science
uses to predict fraudulent activity. For each of the data points collected, Sift Science predicts the
likelihood that this user is fraudulent. This is done in a logical, data-driven manner by assigning a
number to each prediction. The signals are arranged in order or priority with the most suspicious
ones on top. Complex algorithms running in the background do the heavy lifting and arrive at a
number that’s presentable, and easily understood by the viewer of the dashboard.
The use of color across the dashboard is exceptional. We saw the use of red and green earlier,
which was used for an ‘either/or’ decision. In this section, with a range of data, the colors orange
and red show different levels of intensity. Importantly, in all three sections we’ve seen so far, the
captured raw data is always shown in gray, and when there’s insight gleaned by Sift Science, or
action to be taken by the viewer, it’s highlighted in solid color tones. This intelligent use of colors
serves to highlight Sift Science’s value add as a product.
Recent Events section:
The final section of the dashboard gets to the minute details that were not part of the previous
section. A filter allows the viewer to segment the data they’d like to see. This is perhaps the most
‘regular’ part of the dashboard, and rightly so. It simply has all the raw data that the viewer can
deep dive into if they choose to.
Concluding thoughts & suggestions:
While I was mostly gazing in appreciation of this dashboard, a few suggestions did spring up
along the way. First, while the dashboard stands out as an effective text-based dashboard, there
are sections that may benefit from visuals. The Sift score works well as is, but it could
communicate the sore more intuitively and powerfully via an interactive gauge. Similarly, the
‘Suspicious Signals’ section has perhaps the most fascinating pieces of data in the dashboard, but
instead of using hard-to-read text like ‘101x’ and ‘65x’, a bar chart (horizontal) would reduce
cognitive load on the viewer, and further bring out the significance of this data.
Additionally, while the text is laid out very well across the dashboard, telling the story of this
fraudster in the form of well-presented data points, there is opportunity for the same story to be
told in more powerfully using a visual. For example, an area chart showing the progressive growth
of the Sift score over time, with each suspicious activity, will serve to visualize the same story in a
way that immediately resonates with the viewer. I don’t at all suggest using gimmicky visuals that
rob this dashboard of its subdued, yet intense energy. Rather, visuals that stick to, and in some
ways enhance the existing design philosophy may be a welcome addition.
In summary, this dashboard oozes predictive analytics, and boasts of a refreshing design that
does justice to the advanced technology powering it. Starting with the most vital information up
top, it gradually and charmingly reveals deeper layers of insight at every step. The exquisite blend
of colors serves not just to accurately interpret the data, but more importantly, to project the
strengths of the product. With this dashboard, Sift science gives us a lesson on effective
With that we end our review of another impressive dashboard, and are almost at the end of our
series on predictive analytics. If you enjoyed reading this, be sure to check out the other posts in
Part 1 – Predictive Analytics: No More the Way of the Analytics Ninjas
Part 2 – 5 Businesses on the Frontier of Predictive Analytics
Part 3 – 4 More Businesses on the Frontier of Predictive Analytics
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10 Tips for Data Visualization from New Relic’s Real-time
Last updated on F e b r u a r y 2 6 , 2 0 1 4by T w a i n T a y l o r
This is the fifth post in our series on Real-time Data Visualization.
In businesses globally, perhaps the first and most important use of real-time analytics is seen in
the IT department. Keeping all servers, and applications up and running at all times is no mean
task. IT professionals rely on real-time dashboards day in and day out to help them with their
time-sensitive, critical, and complex job of managing a business’s IT infrastructure.
All IT monitoring applications today place dashboards front and center of their user experience.
Yet, being an industry with decades of pedigree, some of their dashboards can betray outdated
approaches. However, one application that is relatively new in the space, but has still managed to
set itself apart from the pack is New Relic. Their progressive approach to application performance
is visible from their dashboards, most of which are real-time. In this post, we’ll be looking at New
Relic’s real-time dashboards, and learn a few lessons from their approach to solving application
The first dashboard we’ll look at is the Overview dashboard. This is the most viewed dashboard,
and is the first place a user would look to get a sense of the performance of the application at any
1. Prioritize information
One glance at the Overview dashboard, and you’ll notice that it’s packed with a lot of information.
A closer look shows how all this information is organized by priority. In the top-left corner of the
dashboard is the ‘Browser page load time’ section, which is the most vital information for a web
application. Just below that is a world map, showing the Apdex, or Application Performance
Index, across different countries. Just these two sections of the dashboard are enough to gauge
the situation in a few seconds. This is a feature of all successful real-time dashboards, they
prioritize all the available information, and show the user the most important information first.
Whether it’s their Overview dashboard, or any of the drill-down dashboards, which we’ll look at
soon, New Relic sticks to this simple rule.
2. Choose the right chart type
The main chart uses a stacked area chart to show 4 different metrics that constitute the overall
page load time. This information could have been shown with 4 separate gauges, for example, but
that would have made comparing the metrics more difficult. It could have used a stacked column
chart, which would better show the total time spent rendering pages. However, that might have
made the visual look choppy. It could have used a regular line chart with each line having a
unique color, which would have worked fairly well. However, using a stacked area chart most
clearly communicates which of the metrics shows most latency. The colors help the user
differentiate the four metrics, and decide which needs their attention first. [Related infographic:
Selecting the right chart type for your data]
3. Use tooltips for accuracy
Hovering over the main chart shows a tooltip with information about the application
performance. This tooltip is used to convey the total time spent rendering. This is well thought
out as it can be hard to decipher the total value from 4 different data points. However, the most
interesting feature of this tooltip is that it corresponds to the tooltips in the other two smaller
charts in this dashboard. Moving across the chart updates all 3 tooltips. This makes for an
interactive experience, and importantly, enables deeper analysis. This brings us to our next point
4. Always show data in context
Looking at the map, and the first chart tells the user what they need to be aware of at any given
time. However, looking around at the two smaller charts, the user gets even more signals about
the application performance. Given how time-sensitive the information is, and the number of
factors involved, taking the time to present real-time data in context can speed up analysis, and
save the user a lot of cognitive effort. This method of presenting information in context is used
repeatedly and effectively across New Relic’s dashboards.
5. Include a string of events to tell the story
While the map, and charts give the user a good understanding of what’s happening, when
something’s going wrong, it’s imperative to know what action needs to be taken. A great
dashboard gives not just the right signals, but also visibility into the cause or origin of the issue at
hand. The Overview dashboard does this with the ‘Recent Traces’ and ‘Recent Events’ sections.
These sections give the user clues on what events in the past few minutes could have triggered
any issues. This way the user understands not just the ‘what,’ but the even more important ‘why.’
With just this information, the IT professional may have all they need to improve the Apdex.
However, New Relic provides many levels of drill-downs in their dashboards for most scenarios
which require a lot of poking and prodding around before a solution is found.
6. Colors can direct users to critical information
Colors can easily go wrong in a dashboard when overused, or used without purpose. However,
when colors correspond to the innate associations we have towards them, they can be very
powerful. For example, the world map uses red, green, and yellow to show application
performance. Similarly, the chart in the top-right corner shows bands of red, yellow, and green to
categorize the Apdex score. Additionally, in the ‘Recent events’ section, events that are ‘Critical’
are marked with a big red dot. New Relic uses colors to draw users’ attention to critical
information, and ensures it doesn’t distract the user from their objective of speeding up app
7. Drill-down for data gold
The overview dashboard allows users to get a bird’s eye view of the application performance. If a
user needs to deep dive, clicking on any section of the Overview dashboard shows additional
dashboards that focus on specific aspects of the application performance. This ensures the user is
not overloaded with information in a single dashboard, but can get to the exact information they
need in a click or two. [Related read: How to create an intuitive drill-down interface.]
8. Sort or allow sorting
The drill-down dashboard, shows detailed stats on ‘Page load time.’ The bar chart in the top-left
corner shows the different factors that affect page load time. Notice that this chart is sorted in
descending order. This is imperative for a user to focus on the factors that need immediate
attention. Also, above the chart is an option to sort by other parameters. This type of sorting is
seen across the New Relic dashboards including in tables. Users crave sorting as it’s a great way to
prioritize information, and take action where needed.
9. Offer multiple flavors of visualizations
To the right of this bar chart, we see a stacked area chart, and above it an option to visualize this
data as a bar chart, or as percentages. This is a great way to enable varied insights from the same
set of data. This is particularly effective in a case like this screenshot, where one metric in the
chart hides all others. In this case a bar chart may give more visibility into each metric. Care
should also be taken to allow only visualizations tha are appropriate for the type of data being
visualized. In this instance, for example, a pie chart wouldn’t have been a good alternative as it
wouldn’t show the trend over time. [Related read: Choosing the right chart type: Column charts vs
Stacked Column Charts.]
10. Take the user back in time
Real-time dashboards are udpated every few seconds, and the data visible at any given time is just
for a brief window of the past few minutes, or hours. New Relic’s real-time dashboards, for
example, are updated every 15 to 30 seconds, and show data over the past 30 minutes. However,
a user may need to go back a few hours, or maybe even a few days to spot the origin or cause of
an issue. In this case, it’s important to provide a date range filter for the dashboard. Many
dashboards today place this feature at the top-right corner. This feature is becoming as natural to
us as a web browser’s minimize and close buttons are. The New Relic dashboard excels at how
intuitive it makes the date range adjustment, with a simple click and drag option.
While writing this post, I took a look at many IT monitoring dashboards out there, but none came
close to the elegance, and sheer usability of the New Relic dashboards. It stands out as an
exquisitely designed dashboard, that takes real working conditions into consideration. It
maintains an intense focus on giving the user the most critical information right now, and even
prompts them to take immediate action. It uses a variety of chart types, interactive features, and
design elements, yet manages to keep the dashboard simple and purposeful.To read more about
real-time analytics, get our white paper ‘The Ultimate Guide to Real-time Data Visualization.’
If you’re interested in dashboard design, or if you routinely use dashboards, check out our
reviews of dashboards from innovative companies like Recorded Future, Cleartrip, Toshl, Sift
Science and many more.
Kudos to the friendly IT/Operations guy at FusionCharts, Nishant Seth, who contributed some
valuable ideas in the making of this post.
Finally, what do you think of New Relic’s dashboards? Are there any other real-time dashboards
that have impressed you? Chime in with your comments below.
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