Events

Let's Meet Up at the Nashville Analytics Summit

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The Nashville Analytics Summit will be on us before we know it. This special gathering of data and analytics professionals is scheduled for August 20th and 21st, and should be bigger and better than ever. From my first experience with the Summit in 2014, it has consistently been a highlight of my year. My first Summit took place at the Lipscomb Spark Center meeting space with about a hundred attendees. Just a few years later, we'd grown to more than 450 attendees and moved into the Omni Hotel.

Mark it on your calendar. I'll give you five reasons why it is a can't-miss event if you work with data:

  1. We've invited world-renowned keynote speakers like Stephen Few and Thomas Davenport. You won't believe who we are planning to bring in this year.
  2. There isn't a better networking event for analytics professionals in our region. Whether you're looking for talent or looking for the next step in your career, you'll meet kindred spirits, data lovers, and innovative businesses. For two years in a row, we have hired Juice interns directly from conversations at the Summit. 
  3. It's for everyone who works with data. Analyst, Chief Data Officer, or Data Scientist... we've got you covered. There are technical workshops and presentations for the hands-on practitioner and case studies and management strategies for the executive. We're committed to bringing you quality and diverse content.
  4. It's a "Goldilocks" conference. Some conferences go on for days. Some conferences are a sea of people, or too small to expand your horizons. The Analytics Summit is two days, 500-something people, and conveniently located in the cosy confines of the Omni Hotel. It is easy to meet new people and connect with people you know.
  5. See what's happening. Nashville has a core of companies committed to building a special and innovative analytics community. We have innovators like Digital Reasoning, Stratasan, and Juice Analytics. We have larger companies making a deep commitment to analytics like Asurion, HCA, and Nissan. The Summit is the best chance to see the state of our thriving analytics community.

Now that you're convinced you can't miss out, you're may wonder what to do next. First, block out your calendar (August 20 and 21). Next, find a colleague who you'd like to go with. Want to be even more involved? We invited dozens of local professionals to speak at the Summit. You can submit a proposal to present

Finally, if you don't want your company to miss out on the opportunity to reach our entire analytics community, there are still slots for sponsors.

I hope to see you there.

Data Monetization Workshop 2018: Key Themes & Takeaways

“Data Monetization is a hot topic because it has two words that everyone loves. We all love data, and who doesn’t want to monetize something?”

These were the words that kicked off the 2018 Data Monetization Workshop to a roomful of attendees and industry experts who had gathered to discuss the question that followed this observation: what does Data Monetization actually mean?

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This question was discussed at length over the course of the half-day event and was the impetus for speaker topics related to using data for social good, how to account for data on a balance sheet, how AI will affect the future of Data Monetization, and more. Here are some of the most important themes and takeaways from the discussions of the day.

What Is Data Monetization?

  • Data monetization is about data value, not data dollars. It’s not about selling customer lists, but about deriving value.

  • Data Monetization encompasses business intelligence and takes a much broader perspective on what can be done with data. Analyzing what options exist outside the enterprise, what products and services can be created using data, and trying to get data into the hands of decision-makers are all components of Data Monetization.

Data for Good

  • Most organizations aren’t trying to sell your personal data; they’re focused on using information to improve city performance, prevent mass shootings, and rescue people from sex trafficking.

  • Nobody owns data. Companies and organizations have rights to data, but in order for progress to be made data must be shared and communicated.

The Dark Side of Data

  • While data offers many beneficial opportunities, there also exists a dark side of data. What complications does something like what happened with Cambridge Analytica have on future opportunities for Data Monetization?

  • Using certain data is not always a question of “Is this legal?” but rather “Is this ethical?” Sometimes data is available but not right to use, which can feel like a restraint at times but leads to being an organization being perceived as trustworthy. It is important to have a solid core philosophy on what data you do and don’t use before it becomes necessary to bring in lawyers and PR teams.

Education, Train, Explain - Data Literacy

  • Poor data literacy is seen across the board. If you don’t read the fine print, you can sign your data rights away. Many problems with the use of personal data are often due to mismatched expectations.

  • People don’t always understand how valuable data is and what an asset they hold. You have to teach people to think in technicolor. Some companies try to exclude information, but more information changes the landscape and provides more context.

  • Creating data products with different derivations is one way to communicate data to different roles (e.g., an analyst versus a CEO). You have to meet people where they are.

  • Being transparent with a product roadmap is a great way to demonstrate to people that data products will look different as time goes on. Users can know what features they can expect and when.

Doing Things Differently and Looking to the Future

  • There are emerging technologies that can help make processes easier. Right now you just have to ask yourself, “How can I do things a little bit differently today?”

Doug Laney Is One Cool Dude

  • Doug Laney was kind enough to join us remotely from his vacation to answer audience questions about his book Infonomics -- of which every audience member got a free copy!

Special thanks to all of the speakers, to MapR for sponsoring the post-workshop networking reception, and to everyone that attended! If you have questions or comments about the Data Monetization Workshop, feel free to reach out to info@juiceanalytics.com.

Related Reading:

You're Invited: Data Monetization Workshop 2018

Juice is proud to announce that it will host the third annual Data Monetization Workshop on Thursday, March 29, 2018 at the Nashville Technology Council’s Tech Hill Commons. Created by local data expert Lydia Jones in 2016, the Data Monetization Workshop brings together some of the top data and analytics practitioners in the country to discuss how to deploy data monetization in a business setting.

This year’s workshop will feature speakers and panelists from companies such as BuildingFootprintUSA, Crystal Project Inc., Dawex, Digital Reasoning, and Uber. The topics covered will be: 

  • The Now: What are the opportunities and business models you should consider to monetize your data? E.g. enhancing existing products, new data products, data marketplaces.

  • The Future: How will emergent technologies such as IoT and AI unlock new opportunities and challenges for data monetization?

Prior to the workshop, attendees will have the option to attend a data storytelling seminar led by Juice Analytics employees. The seminar will showcase Juice’s unique method for quickly and easily creating data stories from a given data set. The workshop will conclude with an open bar networking event.

Attendees in the past have come from Florida, Texas, New York, Georgia, California, Canada, and Australia, and across industries such as healthcare, finance, retail, technology, and government. They are typically members of the C-Suite (including CEOs, CMOS, CFOs, CXOs, and CAOs) as well as data and analytics leaders, data scientists, investors, and data product development leaders, among others.

To learn more about this year's Data Monetization Workshop, visit the link below. Please be sure to register in advance as seating is limited. We hope to see you there!

 

Gift Ideas for Data and Visualization Lovers: 2017

It's that time of year again. Thanksgiving is over, and the mad dash to find the perfect gift for everyone on your holiday shopping list is on. If you're anything like us, you've got a number of data visualization enthusiasts on that list that you just know are going to be particularly difficult to buy for. Thankfully, we're back with our annual gift guide created specifically for people who love data and visualization. Read on to find out exactly what to buy for your data-loving friends and family.*

Books

Just like last year, we're kicking this gift guide off with a selection of books that we think any data lover would enjoy. While there are so many excellent books on data visualization to choose from, these are a few of our favorites that were released (or re-released) this past year, with a few old classics thrown in as well. 

The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios by Steve Wexler, Jeffrey Shaffer, and Andy Cotgreave

Semiology of Graphics: Diagrams, Networks, Maps by Jacques Bertin

Visual Journalism: Infographics from the World's Best Newsrooms and Designers by Javier Errea

Infographics: Designing and Visualizing Data by Wang Shaoqiang

Presenting Data Effectively: Communicating Your Findings for Maximum Impact by Stephanie Evergreen

Storytelling with Data by Cole Nussbaumer Knaflic 

Beautiful Evidence by Edward Tufte

Data Fluency by Zach and Chris Gemignani

Art

A few years back, Juice gave each of its employees a piece of sound wave art and it was a huge hit. One employee actually loved his painting so much that it now hangs permanently in Juice's Atlanta office. These pieces are not only custom and unique, they're absolutely beautiful visualizations of something that everyone loves: music.

For Kids

It’s never too early to start introducing the children in your life to the wonderful world of data, visualization, and technology. Instead of wandering through toy stores frantically searching for Fingerlings, consider instead one of the cuter, cuddlier, and less noisy distribution plushies from Etsy seller NausicaaDistribution. These visual guides to Star Wars and comic books make for great introductions for kids and teens to the wonderful world of visualization. And if you want to start them really young, check out the Code-A-Pillar from Fisher Price. It's a seriously cool toy that involves planning a path for the robotic caterpillar and getting it to follow that path using coding.

For the Data Lover Who Has Everything

What do you get for your data loving friends that already have everything on this list? How about the most customized visualization possible - one of their DNA! Give someone the ultimate information with either a 23andMe or AncestryDNA report that details his or her ancestry, food intolerances, and so much more! It will definitely be unlike any other gift they've ever received before.

These are just a few ideas for gifts for your data-loving friends. For more ideas and inspiration, check out our gift guides from previous years. And of course, have a very happy holiday season!

Related reading:

*Or for yourself. We don't judge here.

 

 

Data Storytelling Workshops, Part 2: Data Story Showcase

This is part two in a series on sharing Juice’s data storytelling method at various workshops around the United States. In part one, we talked about the highlights of teaching business professionals how to build insightful data stories in under an hour. Here we’ll showcase a data story that was built by one of our own Juicers who attended a workshop.

When creating data stories at Juice’s data storytelling workshops, we always start with a set of data from Nashville’s Open Data project. There are an infinite amount of data stories that can be created from a set of data like this that contains information on construction permits, location, cost, and type of building permit. For our data story prototype, we decided we wanted to know where to find construction projects for multi-family housing so that we could determine where to best build a cool new coffee shop.

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When creating our data story, it was important to us that we first give it a title. There are many different strategies that you can utilize when coming up with a title, but we decided to start with a simple yet profound question that would ultimately be answered by the end of the data story.

We find that in order to ensure that the data story being created is coherent and focused, it’s crucial to determine how each visualization contributes to the overall goal of the story. In this example, we wanted to have a high volume of people who enjoy a good cup of coffee and would be likely to visit our coffee shop. To display this in our data story, we would zoom into the map to browse areas around Nashville that are sized by the number of multifamily home projects currently underway in a given zip code.

Once we had selected a zip code that had a business-sustainable number of multifamily projects underway, we also wanted to check to see which way the number of projects in that zip code has been trending over the past 5 years. After seeing that the volume of projects in our zip code of interest had been positively trending over the past few years, we would search through the table at the bottom to find the largest one of these projects to build our coffee shop near so that we can maximize our chances for success.

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Want to know more about our data storytelling process? Send us a message! We're always happy to share our methodology or to answer any questions.

 

Data Storytelling Workshops, Part 1

The Juice team has been traveling around from conference to conference showcase our method of quickly and easily creating data stories from a data set. We got the opportunity to utilize Nashville’s Open Data project to source the data we used for the workshops. Attendees were divided into several groups and given the option to choose between several personas for whom they would build their data story. By focusing on a particular type of user’s goals, attendees were easily able to create questions that should be answered by the data. These questions or “goals” for their data story were written out on sticky notes by each group member and were shared with the entire group.

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Once the goals for their stories were distilled into just 3 questions total, attendees chose metrics and dimensions that would function to best answer the questions that would achieve the goals that their personas wanted from the data story. Making decisions about building effective data stories typically take hours if not days. We were able to accomplish this in less than an hour and saw attendees leave with a full understanding of how a great data story is built for a particular audience or user.

Some workshop moments that were captured can be found in the 30-second video below:

Stay tuned for part two, in which we will showcase a data story that was created by one of our attendees. You won’t want to miss what he created in just under an hour with Juice’s guidance!

If you can't wait and want to see how you can start making your own data stories with Juice, send us a message using the button below.

Q&A with Treasure Data: Everything You Ever Wanted to Know about Data Viz and Juice

This post originally appeared on the Treasure Data blog. 

Tell us at the story behind Juice Analytics. What’s your mission?

My brother and I started Juice Analytics over a decade ago. From the beginning, our mission has been to help people communicate more effectively with data. We saw the same problem then that still exists today: organizations can’t bridge the “last mile” of data. They have valuable data at their fingertips but struggle to package and present that data in ways that everyday decision makers can act on it. Even with the emergence of visual analytics tools, data still remains the domain of a small group of specialized analysts leaving a lot of untapped value.

Our company has worked with dozens of companies, from media (Cablevision, U.S. News & World Report) to healthcare (Aetna, United Healthcare), to help them build analytical tools that make it easy and intuitive to explore data. We published a popular book in 2014 titled Data Fluency: Empowering Your Organization with Effective Data Communication (Wiley) with a framework and guidance to enable better data communication. To bring our best practices and technology to a broad audience, we built a SaaS platform called Juicebox that enables any organization with data to create an interactive and visual data storytelling application.

Why is data visualization so important to an organization’s ability to understand its data?

Data visualization is one of the most useful tools in bridging the gap between an organization’s valuable data and the minds of decision makers. For most people, it is difficult to extract insights or find patterns from raw data. When we tap into the power of visuals to help us recognize patterns, data becomes more accessible to a broader audience.

For many of the organizations we work with, data visualization has the added value of uncovering issues with the data. Once you start visualizing trends and outliers, the weaknesses or mistaken assumptions about your data come to the surface.

What is data storytelling? How can it be useful to marketing professionals?

The term data storytelling has become increasingly popular over the last few years. We know that data is important to reflect reality — but absorbing data, even in the form of dashboards or data visualizations, can still feel like eating your vegetables. We all recognize the power of storytelling to engage an audience and help them remember important messages. People who focus on communicating data — like our team at Juice — feel that there is an opportunity to use some of the elements of storytelling to carry the message. Stories have a narrative flow and cohesiveness that distinguishes them from most data presentations.

However, data storytelling is different from standard storytelling in some important ways. For one thing, in a data story the reader is encouraged to discover insights that matter to them. One analogy I like to use is a “guided safari.” Data storytelling should take the audience to the views of data where new insights are likely to occur, but it is up to the audience to “take a picture” of what is more relevant to them.

In our experience, data storytelling is particularly valuable to marketing professionals. For internal audiences, data storytelling techniques can help you explain the impact of your marketing efforts to your stakeholders. For customer or prospects, data stories can be used to lend credibility to your marketing messages and enable deeper insights of your product.

What are essential tools for data storytelling?

The tools for data storytelling fall into a couple of categories: human skills and technology solutions.

The most critical skill you can have for data storytelling is empathy for your audience. You want to know where they are coming from, what they care about, how data can influence their decisions, and what actions they would take based on the right data. Knowing your audience allows you to shape a story that emphasizes the most important data and leads them to conclusions that will help them. Data storytellers must remember that an audience has a scarcity of attention and a need for the most relevant information.

At Juice, we’ve thought a lot about the capabilities that make data storytelling most effective — after all, we’ve created a technology solution that lets people build interactive data stories. Here are six features that we consider most crucial:

  1. Human-friendly visualizations. Your audience should be able to understand your data presentation the first time they see it.
  2. Combine text and visuals. There are lots of tools for creating graphs and charts. But data stories are a combination of data visuals flowing together with thoughtful prose and carefully-constructed explanations.
  3. Narrative flow. The text and visuals should carry your audience from a starting point (often the big picture of a situation) to the insights or outcomes that will influence decisions.
  4. Connected stories. In many cases, it takes more than one data story to paint the whole picture. Think of presenting your data as a, “Choose Your Own Adventure” book, in which the audience can pick a path at the end of each section to follow their interests.
  5. Saving your place. The bigger and more flexible a data story becomes, the more important it is to let the audience save the point they’ve arrived at in their exploration journey.
  6. Sharing and collaboration. Data stories are often a social exercise with many people in an organization trying to find the source of a problem and decide what they should do about it. Therefore, it is critical to let people share their insights, discuss what they’ve found, and decide on actions together.

Where do you see organizations struggling the most with managing and understanding the data they collect? What should they be doing differently?

A common problem is that organizations don’t truly understand the data they are collecting. Ideally, data is truth— it should allow us to capture and save the reality of historical events, such as customer interactions and transactions. However, more often than not, what the data is capturing isn’t exactly what people imagine. We find it useful when we can get a data expert in the same room as the business folks who will be using the data. A deep dive discussion about the meaning of individual data fields will often reveal mistaken assumptions or gaps in understanding. Working together to build a data dictionary can be invaluable as you continue to use data.

Data exploration is an iterative process. Answering one question will raise a few more. In this way, organizations will eventually identify where they lack understanding of their data. The faster you can iterate on analyzing and presenting data, the sooner you will resolve the issues.

Is all data visualization created equal? What do organizations need to know about finding the right type of visualization to help better understand their story?

Not all data visualization is created equal. There are visualization approaches — charts and graphs — that could be a good fit for your data and message and there are poor data visualization choices that will obscure your data. One mistake that we see is an ambivalence toward finding the right chart for the job. You may have seen dashboards that default to show data as a bar chart, but also give users the ability to pick a variety of other charts types. Why not choose the best chart to convey your data and unburden users from making any more decisions?

There are also well executed and poorly executed data visualizations. Good data visualization emphasizes the data over unnecessary styling, clearly labels the content and directs attention to the most important parts of the data.

From where you sit, how should organizations approach their data management – from collection to storing to analyzing?

We start from the end, then work our way backward. One of the biggest mistakes we see is organizations trying to collect and consolidate all the data they may possibly need in one place. These types of data warehouse projects quickly spin out of control with endless requirements and increasing complexity. It doesn’t have to be that way. Instead, we’d encourage people to start with three simple questions:

  1. What important action do we take today that could be better informed by data? Only include high impact actions where you have the data to answer the question.
  2. How would we present that data to the people who make take those actions? Most of the time it isn’t a data analyst who is going to be acting on the data on a day-to-day basis. Consider the simplest possible view of the data that would enable the end users.
  3. What data is necessary to deliver that view? Now you’ve narrowed down to just the critical data that is going to make an impact.

Once you’ve answered these questions for one specific action, you can go back and do it again for another.

What trends or innovations in Big Data are you following today?

Here are a few of the areas that are interesting to us:

  • Data narratives. Companies like Narrative Science are turning data into textual summaries. Like us, they are looking for ways to transform complex data into a form that is readable to humans.
  • The intersection of enterprise collaboration (e.g. Slack), data communication (e.g. Juice), and business workflows (e.g. Salesforce). Our goal isn’t just to help visualize data more effectively. We want people to act on that data. To do so, data visualization needs to connect to places where people are having conversations and into systems where people make business decisions.
  • Specialized analytical tools. The pendulum appears to be swinging away from do-it-all business intelligence platforms and toward best-of-class, modular solutions. Companies like Looker, Alteryx and Juice aren’t trying to be everything to everyone — but rather serve a specific portion of the data analysis value chain. We’ve found more and more companies that are looking for the best tools for the job, but require mobility of the data between these tools.

Do you have a question about data viz, data storytelling, or Juice that we didn't answer? Send us a message at info@juiceanalytics.com or fill out the form below.

Lessons from More Than Insights: Beyond Exploratory Data Viz

Last month a group of Juicers attended a lecture at Georgia Tech entitled “More Than Insights: Beyond Exploratory Data Visualization” given by Hanspeter Pfister, Professor of Computer Science and Director of the Institute for Applied Computational Science at Harvard University.

Pfister cited the rise of the infographic, as well as an increased general interest in subjects like data storytelling and data journalism as evidence that more and more people are becoming interested in using visualization to communicate and explore information. But what comes after information is shared?

“After insight comes the message,” Pfister explained. “The information is the ‘what’, the message is the ‘so what’ - the ‘why should I care?’”

Being able to address the “so what” brings a whole new set of challenges to data communication, Pfister told the audience. He explained that we’ve only just begun to scratch the surface of what is possible, that we actually don’t know as much as we think we do about these subjects, and that much more research is needed to even begin to understand these intricacies. To illustrate his point, he used examples from three different subject areas: data visualization, data storytelling, and data tools.

Data Visualization

Pfister cited a study that he had participated in along with Michelle Borkin on what makes a visualization memorable. In the study, participants were shown a string of various visualizations and told to respond if they remembered having seen it previously.

So what did the researchers find made a visualization memorable? The charts were found to be more memorable if they contained human recognizable objects (such as dinosaurs or faces), if it was colorful, visually dense, or had a title, labels, and/or paragraphs.

Are these descriptions setting off alarm bells and making you scream internally? It’s probably because these characteristics are the exact design elements we’re taught to avoid. To further prove this point, Pfister shared that the least memorable visualizations were what we’d think of as more “Tufte-compliant.”

So the question on everyone’s minds: do we toss out the old guidelines in favor of brighter, busier visualizations? Not necessarily. Pfister shared that he believes the answer may lie in “something beyond [Tufte] that we haven’t explored that much.”

Data Storytelling

Pfister then brought up the ultra-new method of using comics to communicate data. Ultra-new because, as Pfister pointed out, there are few actually using comics to communicate data, there is no real definition of what a data comic actually is, and there are no real tools to create data comics.

A data comic, he explained, is communicating data in a way that comic books typically communicate stories. He explained that the four essentials for data comics were visualization flow, narration, words, and pictures, and demonstrated how all of these work together by displaying a data comic that showed the various power struggles that contributed to World War I.

It’s hard to do the comic justice by just talking about it, but to give you some idea of the effect it had on the audience, I would like to use one audience member’s own words: “It’s like a punch to the brain.”

Viewing the information in the form of a data comic was a faster and clearer way to communicate the information than any textbook could have done. It was evident from this example that data comics are more likely to play a larger role in the future, but, Pfister questioned, how will it fit into data storytelling overall?

Data Tools

The last subject Pfister hit on was data tools. He explained how the majority of popular data tools are relatively easy to use, but lack ability to customize visualizations easily. On the other side of the spectrum, however, are tools that are more expressive but lack ability to add insight. He argued that data scientists not only want but deserve better tools, and because of this there should be a product that falls somewhere in between Excel and InDesign.

The answer that Pfister and a team of individuals, in collaboration with Adobe, came up with was a program in which the user puts data into a spreadsheet, then uses guides that constrain the data to create a visualization. It was an interesting way of displaying data, but will it satisfy data scientists’ quest for the perfect tool? Only time will tell.

 

It was clear from Pfister’s lecture that more research needs to be done in all of these areas before we can truly say for sure what the best methods of communicating data are. It’s an exciting time to be in visualization, and we’re excited to see what the future brings. In the meantime though, check out our design principles for what we’ve found to be some pretty effective strategies for communicating data.

A New Juice Tool for You: Buyer's Guide to Analytics Solutions

At Juice, we like to create useful tools for our readers. Here favorites seem to come out every two to three years:

The data and analytics space is a confusing place, densely populated with dozens and dozens of vendors, each one claiming they alone can solve your problems. But who’s really offering the right tool for your situation?

 Big Data Landscape, Matt Turck 2016

Big Data Landscape, Matt Turck 2016

Our Buyer’s Guide is designed for technology decision-makers who are trying to make the most of their data. Whether you are looking to analyze large data sets, map location data, or build visualization tools for your customers, we’ve done the dirty work of scanning the landscape and categorizing what we found. We’ve categorized the more than 100 analytics solutions into 19 categories of tools.

 We start The Buyer's Guide with a question about your end-user.

We start The Buyer's Guide with a question about your end-user.

The Guide is a decision tree where you answer questions about your needs, and each answer leads you down a path toward the right type of analytics solution. Think of it as a "Choose your Own Adventure" book where your happy ending is the best tool for the job. For each category of analytics tool, we’ve tried to compile a comprehensive list of providers. 

After navigating the choices available to you, you will have the option to submit your results. If you’d rather keep your results private, no problem. For those who submit their results and email, we’ll send you our three most popular white papers and include you in our monthly newsletter. 

If you find that we’ve missed analytics vendors that you are familiar with, send us a note at info@juiceanalytics with the subject line "Buyer’s Guide". 

Book Review: "Bringing Numbers to Life"

"The effort was none other than to do the hard work of bridging numbers and people, and building that work into products that entice, even seduce, skeptical users to invest in an unfamiliar activity."

In his new book "Bringing Numbers to Life",  John Armitage sets out to create a design framework for analytical apps. It is a worthy subject and one that is close to home for our Juice team.

Mr. Armitage’s book has been published online and free-of-charge by the Interaction Design Foundation. The Foundation’s founder, Mads Soegaard, shared their vision: "We offer complete, unrestricted and free access to our chapters/books/textbooks in online versions. Everything we do is a labour of love and not a business model. We walk the talk of altruism.” The IDF hosts a wealth of free materials and textbooks, with contributions from top academics and professionals.

The book is a worthy subject delivered by a worthy organization. I was happy to share my thoughts.

"Bringing Numbers to Life" describes the how, what, and why of a “design-led innovation in visual analytics. The author is a lead designer at SAP (update: John Armitage is now head of UX design for Host Analytics), the software behemoth and vendor for such business intelligence products as BusinessObjects, Lumira, Roambi, and Crystal Reports. He was charged with creating a unified design model across these products to make them more effective in the communication of data. In his words:

“The result of this effort was a number of prototype projects that led to LAVA, a design language for visual analytic environments intended for broad application across the SAP product suite. The key driver behind LAVA was simplicity and low cost, which translated into some fundamental innovations that, with the backing of a large company like SAP, stand to improve the clarity and reach of visual analytic consumption in the workplace and beyond."

The analyst’s office is filled with books by Edward Tufte, Stephen Few, and Alberto Cairo providing guidance on how to visualize data. But what about the designer or developer of analytical solutions? Their challenge is in many ways more complex.

Armitage makes an important distinction between “Artisanal" and “Production" solutions. It is one thing to craft a one-off visualization solution for a specific purpose. In these cases, the author knows the data, the audience, and the specific message they want to convey. His challenge is to develop a system that can repeatedly deliver high quality data visualization and analytical tools. It is the difference between the carpenter who can craft a single table and the engineer who can create a factory that delivers a thousand tables. The requirements, skills, and frameworks are very different.

Armitage has researched this topic thoroughly. Over many years, he worked with internal SAP teams and consultants to define and refine his LAVA (Lightweight Applied Visual Analytics) framework. His framework describes important pieces for any analytical solution, and how these pieces should fit together. While he defines a specific nomenclature, the elements are universal building blocks. A few examples:

  • Charts can exist at different levels of detail and fidelity. When we want to represent a concept, it may appear as a single number or sparkling (“micro chart”) or a fully-labeled trend chart (“chart”) or as part of a multi-component, interactive visualization (“meta chart”).
  • Analytical tools need a hierarchy of components that allow a user to consider a broad concept, drill into more detailed topics, and explore specific data.
  • Modern analytical applications need to offer much more than the visualization of data. Features for sharing, collecting insights, and personalization are necessary to deliver a complete analytical tool.
  • The traditional single-page dashboard design is antiquated. "Scrolling effectively increases the virtual size of your display outside the borders of your window or device, and is a basic convention for digital content consumption that has been ignored in traditional dashboard design."

Armitage is also a critic of the increasingly complex and bloated analytical solutions — something I hear more and more from companies frustrated with visual analytics tools like Tableau.

"Currently, however, most visual analytic solutions reflect previous efforts to serve large high-paying enterprise customers, and are thus bloated with features designed for highly trained – and high-paying – specialists. As Clayton Christenson’s principle, and associated book titled The Innovator’s Dilemma tells us, companies in such high-margin businesses are beholden to serving their large customers, and thus leave the low-end of the business exposed to inroads by newcomers to the market. Visual analytic market leaders are facing such a dilemma today."

Despite my fondness for the topic and appreciation of Armitage’s evident research, the book has issues that I found hard to overlook.

Armitage indulges in lengthy tangents, personal biography, and an obsession with the specifics of the SAP landscape and politics — providing us with sentences like this:

“I even produced a farcical off-site video on the theme of multinational collaboration, and a comedy routine – based on the famous “Who’s on First?” from Abbot and Costello – to poke fun at working in multinational teams. I performed the latter live with Jay Xiong at our 2012 office holiday party in Shanghai."

He also appears to be fighting a public battle with the leadership of SAP to adopt his LAVA model in the pages of his book:

"Although LAVA, in particular the Lattice, pointed to similar effects for quantitative data, it was difficult for some people who were particularly close to BI to acknowledge its potential. The definitive objection from this faction was that LAVA “does not match Our metaphor”, which was of course precisely the point. LAVA is a new metaphor, and one that’s necessary for achieving SAP’s product aspirations."

I have a deeper concern with some of the recommendations for visual solutions. One of the features concepts is a "Lattice chart”. I found the example provided to be confusing and complex. Designing visualizations is an exercise in finding simplicity and accessibility for your audience. The image below is crammed full of information, but lacks the legend or space for a typical user to know what it all means.

Perhaps my biggest concern is with Armitage’s long-term vision. He expresses a desire to create an analytics world where human intervention isn’t necessary to communicate data effectively.

With this scalable framework in place, we can start to dream of efficiencies on a large scale, with entire Board Sets generated automatically from adequately-provisioned data warehouses. Filters, Panels, and Lattices can be determined with a rules engine automatically from combinations of Measures and Dimensions. Galleries can be populated by algorithm-generated charts derived from the most relevant Lattice Layers. Points can be created from data set indexing and data mining, and populated with major category representations and outliers. Multiple data sets can be organized into groups and presented as Board Sets, or one giant data set can be subdivided into Boards according to individual or sets of Measures or Dimensions, with these Boards further subdivided into Categories and Lattice stacks according to the depth and complexity of the data. These efficiencies will allow more people to use data to make decisions, and require fewer people to support them.

In my experience, data communication needs to start with an intimate knowledge of the business context, an appreciation for your audience, and an understanding of what it takes to make them more effective in their jobs. These aren’t things that can be scraped from a database.

Philosophical issues aside, this is a book about Armitage’s journey in trying to change how an organization delivers analytics, his process and research, and the quite-useful framework that came from his journey. As someone who has tackled the same design challenges in creating a new kind of tool for visualizing data, I appreciated being able to get an up-close view of how another professional wrestled with common challenges.

In describing his conclusions, John Armitage notes, "During LAVA’s development, I found myself surprised that nobody had before arrived at our fairly simple and basic conclusions."

Don’t worry, John, you’re not alone.