Up Your Game With Interactive Data Visualizations

In an increasingly data-driven world, the way we visualize the massive amounts of information is a key concern. While static data visualizations have their uses, today’s data visualization tools offer a significant upgrade thanks to their customizability as well as their capacity to generate fully interactive dashboards.

Interactive data visualizations grant you several benefits, ranging from the purely aesthetic to the more practical. They make dashboards engaging, accessible, and turn massive data sets into easily interpretable visual tools. With such a powerful tool, it’s also important to understand the best ways to maximize its potential and derive actionable insights. Incorporating these tips and strategies can help you take your interactive visualizations to the next level.

Getting the most out of your interactive data visualization

An interactive data visualization is a tool used to visually express a set of data in an easy-to-interpret manner. Interactive data visualizations let users manipulate data sets to help them discover better insights. A well-designed interactive visualization adds significant value by displaying information in a new light while empowering you to explore data sets easily and effectively. Using some of these basic strategies can help you produce the best possible interactive data visualizations:

  • Consider the end-user for your visualization – Data visualizations in dashboards are tailored for specific audiences—managers, end-users, accountants, HR, and so on. Understanding their specific needs helps uncover the best possible visualization choices as well as the right data and way to express it.
  • Think about the story you’re telling – The data you apply will largely dictate the type of visualization you can use, and the story that data tells will be important to generate the right interactive element for it.
  • Keep visualizations simple – Interactive visualizations work best when they are focused, concise, and eliminate unnecessary graphics, text, and other elements that take away from the data itself.
  • Choose visualizations that can be easily updated – Interactive visualizations in dashboards rarely display static data. Choosing an interactive data visualization that can’t easily be updated will be difficult to use more than once and lose its value in a dashboard.

Dashboard Design

What kind of visualizations should you use?

There are several excellent examples of interactive visualizations that can quickly upgrade your dashboards and deliver better insights:

  • Sankey Diagrams – These visualizations are excellent for understanding and mapping the flow of data or objects. Sankey diagrams are most commonly used to measure web traffic, data flow between network nodes, or energy flow and consumption.
  • Tree Ring Diagrams – These visualizations are ideal for illustrating and mapping out hierarchies between nodes and how data interacts in a network.
  • Collapsible Trees – These tools show decisions as branching paths from an initial point, allowing viewers to map out possible outcomes through several iterations.
  • Heat Maps – These types of maps are excellent when you have data based on geographical locations. For example, comparing per capita rates on a city map can be made dynamic by highlighting heat points on the map, or allowing for comparison between two locations.

When should you use interactive data visualizations?

Interactive visualizations are not a silver bullet for dashboards, but there are several situations where they can add significant value to your data analysis.

One common use of interactive visualizations is understanding the flow of visitors through a website. By deploying an interactive data visualization, a company can track each individual’s journey through their website, including how long they spent on a page, when they left, and which pages they visited. Users can also view aggregate data to understand which pages are popular and which are losing the most viewers.

In IT, an interactive visualization could highlight different network configurations, as well as show chokepoints of data and areas where the architecture could be improved. Moreover, a tree ring diagram could visualize the relationship between different parts of the network.

For analysts, collapsible trees could show potential outcomes of different investment or risk decisions. Other interactive visualizations could expedite data comparison and create a more holistic view of different data sets to make them easier to explore. (Check out the interactive example below).

Investment Portfolio- Financial Dashboard

In financial dashboards, interactive visualizations could provide a simpler way to view investments in aggregate and then broken down into specific assets or different categories. Additionally, they could track investments over time and allow for higher specificity.

Creating dynamic dashboards

Interactive visualizations offer an easy way to fashion dashboards that are relevant, useful, and engaging. By adding a dynamic element to your data displays, you can add value to your data, keep users interested, and empower them to discover better insights. In a world that is constantly changing, using visualization tools that can reflect these shifts can help you stay ahead of the crowd.

Dashboard Design

Source: Up Your Game With Interactive Data Visualizations

Talend Summer’18 Release: Under the Hood of Talend Cloud

Today on July 19, we released Talend Summer ’18, which is jam-packed with cloud features and capabilities. We know you are going to love the Talend Cloud automated integration pipelines, Okta Single Sign-On, and the enhanced data preparation and data stewardship functions…there is so much to explore!

Taking DevOps to the Next Level with the Launch of Jenkens Maven Plug-in Support

DevOps has become a widely adopted practice that streamlines and automates the processes between Development (Dev) and IT operations (Ops), so that they can design, build, test, and deliver software in a moreagile, frictionless, andreliable fashion. However, the conventional challenge is that when it comes to DevOps, customers are not only tasked with finding the right people and culture, but also the right technology.

Data Integration fits into DevOps when it comes to building continuous data integration flows, as well as governing apps to support seamless data flows between apps and data stores. Selecting an integration tool that automates the process is critical. It will not only allow for more frequent deploying and testing of integration flowsagainst different environments, increase code quality, reduce downtime, but also free up DevOps team’s time to work on new codes.

Talend Cloud has transformed the way developers and ops teams collaborate to release software in the past few years. With the launch of Winter ’17, Talend Cloud accelerated the continuous delivery of integration projects by allowing teams to create, promote, and publish jobs in separate production environments. An increasing number of customers recognize the value that Talend Cloud brings for implementing DevOps practice. And now they can use the Talend Cloud Jenkins Maven plug-in in this Summer ’18 release, a feature that lets you automate and orchestrate the full integration process by building, testing, and pushing jobs to all Talend Cloud environments. This in turn further boosts the productivity of your DevOps team and reduces time-to-market.

Security and Compliance made Simple: Enterprise Identity and Access Management (IAM) with 1 Click

 If you are an enterprise customer, you are likely faced with the growing demands of managing thousands of users and partners who need access to your cloud applications, at any time and from any devices. This adds to the complexity of Enterprise Identity and Access Management (IAM) requirement: meeting security and compliance regulations and audit policies, minimizing IT tickets, and only giving the right users access to the right apps. Single Sign-On (SSO) feature helps address this challenge.

In the Summer ’18 release, Talend Cloud introduced the Okta Single Sign-On (SSO) support. SSO permits a user to use one set of company login credentials to access multiple applications at once. This update ensures greater compliance with your company security and audit policies as well as improve user convenience. If you are with other identity management providers, you can simply download a plug-in to leverage this SSO feature. 

The other security and compliance features worth mentioning in this release are the Hadoop User Impersonation for Jobs for the cloud integration app, and the feature that enables fine-grained permissions on the sematic types definition, both will provide greater data and user visibility for better compliance and audit, see this release note for details.

Better Data Governance at Your Finger Tips: New Features in Talend Data Preparation and Data Stewardship Cloud Apps

The Summer ’18 release introduces several new data preparation and data stewardship functions. These include:

  • More data privacy and encryption functions with the new “hash data” function.
  • Finer grained access control in the dictionary service for managing and accessing the semantic types.
  • Improved management in Data Stewardship, now that you can perform mass import, export and remove actions on your data models and campaigns, allowing you to promote, back up or reset your entire environment configuration in just two clicks.
  • Enhancements in the Salesforce.com connectivity that allows you to filter the data in the source module, by defining a condition directly in your Salesforce dataset and focus on the data you need. This reduces the amount of data to be extracted and processed. Making the use case of self-service cleansing and preparation of Salesforce.com data even more compelling.

Those functionalities make cloud data governance a lot simpler and easier.

To learn more, please visit Talend Cloud product pageor sign up for a Talend Cloud 30-day free trial.

For more exciting updates, you can pre-register for Talend Connect 2019.

The post Talend Summer’18 Release: Under the Hood of Talend Cloud appeared first on Talend Real-Time Open Source Data Integration Software.

Source: Talend Summer’18 Release: Under the Hood of Talend Cloud by analyticsweekpick

Mitigating the Threat of Hackers to Your Supply Chain

The rate of digital disruption has recently skyrocketed across every industry, helping accelerate global expansion and automating mundane, menial tasks. Coupled with this is the fact that as businesses grow globally, we have seen an alarming uptick in cybercrime. Because organizations have become increasingly digitalized, they are opening themselves up to threatening landscapes where their […]

The post Mitigating the Threat of Hackers to Your Supply Chain appeared first on TechSpective.

Source: Mitigating the Threat of Hackers to Your Supply Chain by administrator

2018 Data and Visualization Gift Ideas

We’re continuing our tradition of the annual data gift guide. These are some of our favorite books and gift ideas for the data scientist, designer or analyst in your life.

While you’re here take a look at the Juicebox product page to see what it looks like unwrapped.

Happy Holidays!

Screen Shot 2018-11-20 at 19.53.04.png

New Books We Love

Books we read in 2018

Data Fluency Image.jpg

Classic Data Books

We’re a little biased in this category, but these are the books on our desks that we refer to all the time.

Data Fluency – Thinking about changing how your team or organization works with data?This is the book for you.

Storytelling with Data – This one already feels like a classic. It provides simple, clear guidance on chart usage and storytelling. Hard not to reference it in the midst of a project.

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy – This is the book that keeps us grounded. Despite how much we think data is delicious and fun its serious too.

The Man Who Lied to His Laptop: What We Can Learn About Ourselves from Our Machines – A seminal read on learning about interactions between humans and machines.

Visualize This: The FlowingData Guide to Design, Visualization, and Statistics – Nathan Yau’s book that teaches us something new every time we pick it up.

The Truthful Art: Data, Charts, and Maps for Communication – We love all of Alberto’s books, but this one is our favorite. Wonderful examples throughout the book.

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Art & Posters

Infographics, Maps, Data Art & More

Data Viz Game.jpg

Data Nerds

This is a term of affection during the holidays.

Originally Posted at: 2018 Data and Visualization Gift Ideas

5 Steps to Transform HR with Predictive Talent Analytics

Every story has a beginning, and the story of Human Resources began in the 1950s (remember Personnel?). A lot has changed since then: the technology boom, four workforce generations, drastic changes in world economics and in the way we all work. Because HR is still so young in comparison to other mainstay professions like law or medicine, it is still facing its first round of major changes.

Big data, performance standards, talent analytics, talent management, performance and employee data, these have all had a major effect on how HR functions since these data points didn’t even exist at the beginning of the HR story. While predictive analytics is new territory for many HR pros, there are some best practices around predictive analytics to help transform your HR department into a more agile and proactive organizational entity.

1. Assemble Your A-Team
First things first… You have to decide who is going to be on your team. Who has the skills and the knowledge needed? Which employees have the training to interpret the analytics? More and more companies have begun to hire specialized data scientists who have the skills and training to not only interpret the data but translate it as well. Travis Wright (@teedubya), a Chief Marketing Technologist, said:

“These specialists are a crucial part of ‘competitive intelligence,’ which is a new and quickly growing industry. The actual job description can vary from company to company, but the most common task is mining data (of course). ‘Big data’ was a big buzzword in 2014, but it will always remain a vital part of any company. Data is useless if it’s not ‘mined,’ which means optimally collected, analyzed, organized, and activated.”

Unfortunately, many companies cannot afford their own data scientist. Look for those who know their way in and around the HRIS or ATS and those with project management experience (since this will be a longer project than even self-professed “data geeks” will have the patience for).

2. Find Your Square One
Every stepping stone needs a benchmark. To move forward in the transformation, you have to understand your background. Likewise, talent analytics can’t become “predictive” without first assessing the current situation of your pipelines. Simply stated, HR leaders need to understand immediate needs to determine future talent needs. For the HR Professional, this means taking the time to make a map of the processes your department or team repeats over and over and then pinpointing efficiencies (or the lack thereof) in both process and tools.

3. Identify Valuable Numbers
There’s a lot of data out there for your business to look at. Just in an ATS alone, there are thousands of records, each with multiple data points. But you can’t analyze every piece of data that comes across the table or assign it the same level of importance. That’s why it is necessary to filter and funnel the kind of data you will evaluate. Key metrics should primarily involve internal data, but only the data that is truly relevant to your talent analytics. However, external data can be valuable when benchmarking within industries, creating compensation models and figuring supply and demand, so don’t throw it on the trash heap just yet. The primary issue for executives, however, is that only 27% feel they have the expertise necessary for talent analytics and only 13% have the systems to do so.

4. Sharing is Caring
Who needs to know the findings? Who needs to know the information you’ve just assembled? Stakeholders who have the power to make decisions based on this information rely on you to share the information so they can collaborate and make those decisions. That’s why these analytics need to be filtered, so they can be translated into actionable tasks. Beware sharing a messy pile of data points; instead, decide with your team what issues you need to identify and solve for X. Then deliver key insights that can translate into guiding parameters for your company.

5. Training, Training, Training
Big data has become prevalent in decision making. Talent analytics, as a facet of big data, is a predominant resource for HR professionals. They need to understand how data, statistics and analytics can benefit them in the hiring process and employee development processes. Offer your team training opportunities so they can develop their skills and become comfortable with that data. While there is a dearth of data scientists out there, analytics tend to be quite personal to the company, so train in-house and use your analytics vendors for additional learning.

Although HR is a rather young entity and still has a lot to learn, predictive talent analytics is the first major change specific to HR. This step creates an opportunity for organizations to become proactive versus reactive in their decision making. When you assemble the right personnel and define the starting point from which to benchmark, you can begin to share the data with stakeholders and train your HR professionals to analyze the data. These 5 steps can help company executives make the move toward data consumption as a way to influence their decisions based on a higher level of insight.

– See more at: http://blogs.infor.com/infor-hcm/2015/05/5-steps-to-transform-hr-with-predictive-talent-analytics.html#sthash.ymHbUAnw.dpuf

Originally Posted at: 5 Steps to Transform HR with Predictive Talent Analytics by analyticsweekpick

Periodic Table Personified [image]

Have you ever tried memorizing periodic table? It is a daunting task as it has lot of elements and all coded with 2 alphabet characters. So, what is the solution? There are various methods used to do that. For one check out Wonderful Life with the Elements: The Periodic Table Personified by Bunpei Yorifuji. In his effortm Bunpei personified all the elements. It is a fun way to identify eact element and make it easily recognizable.

In his book, Yorifuji makes the many elements seem a little more individual by illustrating each one as as an anthropomorphic cartoon character, with distinctive hairstyles and clothes to help readers tell them apart. As for example, take Nitrogens, they have mohawks because they “hate normal,” while in another example, noble gases have afros because they are “too cool” to react to extreme heat or cold. Man-made elements are depicted in robot suits, while elements used in industrial application wear business attire.

Image by Wired

Originally Posted at: Periodic Table Personified [image]

January 30, 2017 Health and Biotech analytics news roundup

The latest in biotech and health analytics, and related topics:

Deep learning algorithm does as well as dermatologists in identifying skin cancer: The Stanford researchers began with Google’s image recognition algorithm, which they further trained with images of potentially cancerous skin lesions from the Internet.

Study: EHRs Lead to More Imaging Tests, not Less: Electronic records are intended to reduce redundancy, and therefore cost. These new results call that into question, and call for a reevaluation of the role of EHRs.

The DNA Test as Horoscope: Sarah Zhang discusses ‘lifestyle’ DNA sequencing, like a company that makes wine recommendations.

The genomics intelligence revolution: Mahni Ghorashi and Gaurav Garg discuss the history of whole genome sequencing, the role of data science in the field, and its future roles in healthcare and beyond.

Source: January 30, 2017 Health and Biotech analytics news roundup by pstein

Which New Features Do Users Want? Decoding Customer Requests

Like every other feature in your application, the world of embedded analytics is not static. The outer bounds of capabilities customers want are constantly evolving. At the same time, older capabilities like data visualizations—which customers once considered modern and innovative—are now table stakes.

Your end users will always have new requests (and complaints) about your application’s embedded analytics. It’s inevitable. And as long as everything’s working as it should— you’re keeping bugs in check, your app is reliable—most complaints are likely new feature requests in disguise.

>> Related: 5 Early Indicators Your Analytics Will Fail <<

Unfortunately, translating those requests into actual analytics features can be difficult. What do your users really want from their dashboards and reports? Decoding these complaints means adding valuable new features to your roadmap, and avoiding a panicked scramble to add them before it’s too late and your customers start churning.

Use this chart to translate common end user requests (on the left) into the analytics features your users really want (on the right):

If You’re Hearing This…

 …Then Consider Adding This Analytics Capability to Your Application

“We need insights on what’s likely to happen in the future so we can figure out how to correct issues before they become disastrous.”

“The data is great, but it’s in a vacuum and not changing the way we do business.”

“When we’re using the analytics, it feels like we have to learn an entirely new application.”

“Users dislike having to log in twice (once to the app, once to the dashboards). Plus, the application admins say it’s a pain to manage security settings in two different places.”

“When we need to update the information in the dashboard, we don’t like having to leave the app to do so.”

“We have to create multiple new reports just to view different cross sections of data, such as different product lines or date ranges. It’s tedious and inefficient.”

“Our users need to access info from the field and the dashboards don’t work well on mobile devices.”

To learn more, get our Blueprint for Modern Analytics >

Source: Which New Features Do Users Want? Decoding Customer Requests by analyticsweek

Visualization’s Twisted Path

Visualization is not a straight path from vision to reality. It is full of twists and turns, rabbit trails and road blocks, foul-ups and failures. Initial hypotheses are often wrong, and promising paths are frequently dead ends. Iteration is essential. And sometimes you need to change your goals in order to reach them.

We are as skilled at pursuing the wrong hypotheses as anyone. Let us show you.

We had seen the Hierarchical Edge Bundling implemented by Mike Bostock in D3. It really clarified patterns that were almost completely obfuscated when straight lines were used. 

Edge Bundling

We were curious if it might do the same thing with geographic patterns. Turns out Danny Holten, creator of the algorithm, had already done something similar. But we needed to see it with our own data.

We grabbed some state-to-state migration data from the US Census Bureau, then found Corneliu Sugar’s code for doing force directed edge bundling and got to work.

To start, we simply put a single year’s (2014) migration data on the map. Our first impression: sorrow, dejection and misery. It looked better than a mess of straight lines, but not much better. Chin up, though. This didn’t yet account for how many people were flowing between each of the connections — only whether there was a connection or not. 

Unweighted edge bundled migration

Unweighted edge bundled migration

With edge bundling, each path between two points can be thought to have some gravity pulling other paths toward it while itself being pulled by those other paths. In the first iteration, every part of a path has the same gravity. By changing the code to weight the bundling, we add extra gravity to the paths more people move along.

Weighted edge bundled migration

Weighted edge bundled migration

Alas, things didn’t change much. And processing was taking a long time with all those flows. When the going gets tough, simplify. We cut the data into two halves, comparing westward flows to eastward flows.

East to west migration

East to west migration

West to east migration

West to east migration

Less data meant cleaner maps. We assumed there would be some obvious difference between these two, but these maps could be twins. We actually had to flip back and forth between them to see that there was indeed a difference.

So our dreams of mindblowing insight on a migration data set using edge bundling were a bust. But, seeing one visualization regularly leads to ideas about another. We wondered what would happen if we animated the lines from source to destination? For simplicity, we started with just eastward migration. 



Cool, it’s like laser light leisurely streaming through invisible fibre optic cables. But there’s a problem. Longer flows appear to indicate higher volume (which is misleading as their length is not actually encoding volume, just distance). So we tried using differential line lengths to represent the number of people, sticking with just eastward flows. 

Star Wars blasters

Star Wars blasters

Here we get a better sense of the bigger sources, especially at the beginning of the animation, however, for some paths, like California to Nevada, we end up with a solid line for most of the loop. The short geographic distance obscures the large migration of people. We wondered if using dashed lines would fix this—particularly in links like California to Nevada.

Machine gun bursts

Machine gun bursts

This gives us a machine gun burst at the beginning with everything draining into 50 little holes at the end. We get that sense of motion for geographically close states, but the visual doesn’t match our mental model of migration. Migrants don’t line up in a queue at the beginning of the year, leaving and arriving at the same time. Their migration is spread over the year.

What if instead we turn the migration numbers into a rate of flow. We can move dots along our edge bundled paths, have each dot represent 1000 people and watch as they migrate. The density of the dots along a path will represent the volume.  This also has the convenience of being much simpler to explain.

Radar signals

Radar signals

We still have a burst of activity (like radar signals) at the beginning of the loop, so we’ll stagger the start times to remove this pulsing effect.

Staggered starts

Staggered starts

Voilà. This finally gives us a visual that matches our mental model: people moving over the period from one state to another. Let’s add back westward movement.



Very cool, but with so much movement it’s difficult to tell who’s coming and who’s going. We added a gradient to the paths to make dots appear blue as they leave a state and orange as they arrive.

Coloured ants

Coloured ants

Let’s be honest, this looks like a moderately organized swarm of ants. But it is a captivating swarm that people can identify with. Does it give us any insight? Well not any of the sort we were originally working for. No simple way to compare years, no clear statements about the inflows and outflows. If we want to make sense of the data and draw specific conclusions… well other tools might be more effective.

But it is an enchanting overview of migration. It shows the continuous and overwhelming amount of movement across the country and highlights some of the higher volume flows in either direction. It draws you in and provides you with a perspective not readily available in a set of bar charts. So we made an interactive with both.

Each dot represents 1,000 people and the year’s migration happens in 10 seconds. Or if you’d prefer, each dot can represent 1 person, and you can watch the year play out in just over 2 hours and 45 minutes. If you’re on a desktop you can interact with it to view a single state’s flow. And of course for mobile and social media, we made the obligatory animated gif.

And just when we thought we’d finished, new data was released and were were obliged to update things for 2015.

Glowing ants

Glowing ants

Building a visualization that is both clear and engaging is hard work. Indeed, sometimes it doesn’t work at all. In this post we’ve only highlighted a fraction of the steps we took.  We also fiddled with algorithm settings, color, transparency and interactivity.  We tested out versions with net migration. We tried overlaying choropleths and comparing the migration to other variables like unemployment and birth rate. None of these iterations even made the cut for this blog post.

An intuitive, engaging, and insightful visualization is rare precisely because of how much effort it takes. We continue to believe that the effort is worthwhile.

Originally Posted at: Visualization’s Twisted Path by analyticsweek