Our new book Data Fluency may not be for you

...but you probably know someone who should read it.

If you've been following Juice for a while, you know what it is to effectively communicate with data. You know that sweating over a finely-honed analysis is of little use if your audience misses your message. You understand the frustration of presenting a beautifully-designed dashboard only to have the discussion derailed by a debate about what a metric means or how to read a chart. You might agree that the challenges in analytics are less about technology, and more about people, culture, and shared understanding. You've seen that healthy discussion about data is as important as "Big Data" or creative visualizations. 

Sorry, we didn't write Data Fluency for you. We didn't need to.

We wrote the book for those around you to expand appreciation of these principles to others. We believe that data fluency (which we define as the ability to use the language of data to fluidly exchange and explore ideas within your organization) needs to be pervasive to be truly effective. Being the only one in your organization who is great at communicating with data is like being the first fax machine.

How do you expand these pockets of data fluency to entire organizations? How do you create a culture that encourages effective data communication? What capabilities and skills are necessary to put data to be put at the center of decision-making and discussions?

We wrote Data Fluency for the people who can be part of this change. Perhaps it is your boss who needs to better understand the untapped potential of data. Or a colleague responsible for reporting, but who is still learning how to communicate with data. Or your sales team who may be both data-starved and a little data-phobic. 

When Nathan Yau and Wiley approached us about writing a book, we knew that the world didn't need another guide to dashboard design (we'd already written a white paper that did a pretty good job), more lessons in visualization fundamentals (Stephen Few has that covered), or a practical guide for visualization practitioners (Nathan's done that with Visualize This and Data Points).

We wanted to provide a fresh perspective that answered a different question: How can organizations more effectively incorporate data into their decision-making?

Data Fluency is intended as:

  • A roadmap for transforming an organization with a lot of data to one that uses that data to share ideas and knowledge.
  • Practical advice for both consumers and producers of data products (reports, dashboards, analyses). It takes both an effectives presenter and a willing audience for the data to flow freely.
  • A guide for executives who are energized by the opportunities to make a smarter organization, but puzzled by their organization's struggle to be more data-driven.
  • An inventory of the skills and capabilities needed to be data fluent, and an opportunity to see where you stand.

At the core of the book, we've provided a framework for thinking about all the parts that need to come together to build a data fluent organization. We have identified the four elements you need to build a data fluent organization. They are:

  • Engaged and educated data consumers;
  • Skilled authors of data products;
  • A culture that encourages communication with data;
  • An ecosystem of people, processes, and tools that supports the production of quality data products.
Our Data Fluency Framework

Our Data Fluency Framework

Our hope is that this book starts a new kind of conversation in the analytics field -- one that incorporates the people side as much as the tools, techniques, and technologies. We hope it spurs individuals and organizations to start on a journey toward making data a more useful tool for sharing ideas.

Nathan Yau is making a chapter from the book available on his site Flowing Data. Or skip straight to Amazon to buy the book Data Fluency.

Analytics 3.0 and Data Monetization

When it was published in 2007, Competing Analytics: The New Science of Winning sparked the imagination of many business leaders. It opened eyes to the concept of analytics as a strategic capability, beyond basic reporting, financial analysis, and basic marketing optimization. The authors Thomas Davenport and Jeanne Harris established themselves as leading thinkers in an emerging field. However, the book found its critics among some analytics professionals (like us) who felt it delivered a superficial understanding of the real challenges “on the ground” and offered guidance that was abstract, academic, and anecdotal.

At the time, we said his advice for becoming an analytics competitor was "a good example of condensed misperceptions about what analytics can and should do."

Others were less circumspect:

  • The top review on Amazon states: “This is the glib, anecdotal book built around a basic, almost stereotypic Harvard Business Review five-level model, this one focusing on various levels of use of analytical methods, systems and processes.” 
  • From Neil Raden: "what Davenport is implying is not only centralized control, but also centralized design. This is another naive assumption, because many organizations are not only decentralized—they’re dysfunctional."

Even with mixed feedback, it was clear that Competing on Analytics hit a nerve. Davenport and Harris continued their research and evaluation of the analytics world, and in my opinion, have made progress in reflecting the realities and challenges of analytics practitioners. In 2010, they released a book entitled Analytics at Work that focused more on the front-line realities of information workers. In December 2013, Davenport and Harris published an article in the Harvard Business Review entitled Analytics 3.0.

With this evolution, I feel they have begun to capture the essence of the analytics opportunity ahead. In particular, they have begun to focus on how data can be used to enhance product offerings — shifting the focus from smarter internal decisions to smarter, higher-value product offerings. In their conversations with data savvy companies, they describe seeing "a new resolve to apply powerful data-gathering and analysis methods not just to a company’s operations but also to its offerings—to embed data smartness into the products and services customers buy."

They go on to say:

Today it’s not just information firms and online companies that can create products and services from analyses of data. It’s every firm in every industry. If your company makes things, moves things, consumes things, or works with customers, you have increasing amounts of data on those activities. Every device, shipment, and consumer leaves a trail. You have the ability to analyze those sets of data for the benefit of customers and markets. You also have the ability to embed analytics and optimization into every business decision made at the front lines of your operations.

The Analytics 1.0, 2.0, and 3.0 framework from the International Institute for Analytics

The Analytics 1.0, 2.0, and 3.0 framework from the International Institute for Analytics

In the HBR article, Davenport and Harris describe what it takes for companies to engage in Analytics 3.0, and include some of the standard messages that we’ve become so accustomed to from the era of Big Data: more data, more “data management options" (i.e. Hadoop, NoSQL, in-memory, etc.), faster technologies and faster analysis. Perhaps least compelling for me is a concept of creating "analytics on an industrial scale.” Apparently IBM has created a data model factory and assembly line to make and maintain 5,000 models a year. It isn’t clear whether this kind of approach would be appropriate or useful for most other companies.

However, when they delve into the human and organization challenges, their message is more resonant with our experience. For example, they highlight the need for:

  • Focus on delivering analytics to the front-line decision makers. These are the people who are making everyday decisions that impact customers. The best analytical solutions do a good job of presenting data that helps people within their current workflow in ways that are easy to understand and act upon.
  • Time and resources need to be invested into data discovery to understand data before delivering analytical products. Too often we see analytics and reporting rushed out to an audience without a clear sense of what metrics matter and what information will actually help the recipient.
  • Collaboration must occur between the business, analysts, and IT. This is certainly one of those concepts that is easier said than done. Nevertheless, it is better to recognize this challenge up front than believe a cohort of elite data scientists will be able to bring analytics to the masses.
  • Top-level leadership needs to support the deep embedding of analytics into products and services. While this is true, we have also found that achieving successes at a grassroots level can help convince leadership of the opportunity in analytical products.
  • Prescriptive analytics (the effort to use data to specify optimal behaviors and actions) will be more valuable than descriptive analytics and more common than predictive analytics. We’ve found that the analytics needs to have a clear and strong point of view to guide users to insights and actions.
  • Organizations need to focus on transforming how decisions are made. Davenport and Harris state: "Managers need to become comfortable with data-driven experimentation. They should demand that any important initiative be preceded by small-scale but systematic experimentation.” The challenge is in building a culture of data fluency — something we tackle in our upcoming book.

Every day we talk with companies that view their data as an asset that can help them either 1) augment and enhance their existing solutions; or 2) generate new revenue streams. Like Davenport and Harris, we feel these are the early days. Some companies still view customer reporting as an unfortunate requirement rather than an opportunity to build loyalty. Other companies haven’t had the time or resources to find ways to make their products more powerful with data. That understanding will come. Gartner predicts that 30% of businesses will be monetizing their data by 2016 and McKinsey Consulting is seeing similar evidence that companies are finding ways to turn data into dollars. We are excited to have a front-row seat.

Juice Analytics Bridges the Gap between Data Analytics and Data Fluency with Fruition

Newly launched data presentation platform helps organizations harness the power of data through consumer-friendly, revenue-generating applications

NASHVILLE, Tenn. & ATLANTA--(BUSINESS WIRE)--Juice Analytics today announced the launch of Fruition, its data presentation platform designed to help organizations unlock value from data. Fruition empowers business leaders to leverage or monetize data assets through interactive, insightful web applications that compel customers to act on data. The culmination of nearly ten years of industry-leading information design experience, Fruition tells a visually compelling story with data, delivering actionable intelligence and revenue-generating products.

“The Juice team expertly integrates the art of visual communication with the science of data, leading users down a guided path and telling stories with data”

“We have a deep understanding of how and why organizations struggle to capitalize on valuable data,” said Zach Gemignani, chief executive officer at Juice Analytics. “Fruition incorporates our knowledge and best practices into a clean, simple, cloud-based platform that allows organizations to communicate fluently with data. With Fruition, we’ve addressed the next-generation data challenge, moving beyond dashboards and reporting to what we call the ‘last mile of data’—where insights are revealed, decisions are influenced, and action is taken.”

Having worked with other leading business intelligence and data visualization solutions, Gemignani and team found them to be lacking in ability to deliver great products—and most took months to implement. Fruition integrates data sources quickly without burdening IT staff and presents data in a user-friendly interface. Information is layered in visual building blocks to allow users to drill down and slice data based on interests, with each selection leading to greater focus. From enterprise-wide views to individual or product-level performance, insights are easily captured, shared, and saved with a unique Snapshot feature that helps generate dialogue around discovery.

“The Juice team expertly integrates the art of visual communication with the science of data, leading users down a guided path and telling stories with data,” said Robert A. Frist, chief executive officer at HealthStream (NASDAQ: HSTM), a leading provider of workforce development and research / patient experience solutions for the healthcare industry. “Fruition helps users get comfortable with data by building intuitive, easy-to-use products that make information immediately actionable. At HealthStream, we have a tremendous amount of healthcare workforce data flowing through our network and, with Fruition, are exploring ways to leverage it to help healthcare providers achieve better outcomes, save time, and reduce costs.”

HealthStream—which made a minority equity investment in Juice last year—was instrumental in a Juice headquarters relocation to Nashville one year ago, with both companies recognizing the potential of Fruition to support HealthStream’s network, including Nashville’s data-rich healthcare industry. Juice notes, however, that the platform is well-suited to help all industries leverage data. An example—and early adopter of Fruition—is Predikto, which helps manufacturing organizations with high-valued assets anticipate future asset failures and reduce potential for significant downtime costs.

“Fruition delivers our predictive analytic results to customers in an easy to consume, visually compelling way,” said Mario Montag, chief executive officer at Predikto. “Working with the Juice team has been great. Their expertise is evident, and their commitment to creating a truly valuable product is realized in Fruition. Our customers are ecstatic with the results.”

As thought leaders in Analytics 3.0 and the data-driven consumer experience, Juice Analytics’ founders recently authored a book to address the widespread disconnect between collection and meaningful use of data. Entitled Data Fluency: Empowering Your Organization with Effective Data Communication, the book will help leaders responsible for analytics and business intelligence turn data into a valuable tool for decision-making. Data Fluency is scheduled for publication by Wiley on November 3rd.

Turning Data into Product

With today’s technology not only can nearly everything be gathered, counted and measured, but the information can be stored and then processed at record speeds. The result is analysis that goes beyond sums, averages and basic statistics to aggregates, benchmarks, recommendations and predictions. So what does one do with all of this game-changing data and analysis? Create a data product. They’re all around us and they’re changing the way we as consumers interact with companies and the way businesses interact with each other. Tom Davenport, of Competing on Analytics fame says, “It is a new resolve to apply powerful data-gathering and analysis methods not just to a company’s operations but also to its offerings—to embed data smartness into the products and services customers buy.” Analytics 3.0 by Tom Davenport.

 

Defining a Data Product

In the simplest terms, data products turn the data assets a company already owns or can collect into a product designed to help a user solve a specific problem. Still unsure? Here’s an example. Think of car shopping fifteen years ago. You relied on write-ups from industry magazines or ratings in publications like Consumer Reports to help gauge performance and reliability, then drove to the handful of dealerships in your area to price shop and hoped for the best. Today, you use a data product. You access reports and dashboards that tell you precisely how much people across the country paid for the exact same car, how often that make and model goes in for repairs, estimates for how much you’ll spend on gas and tune-ups, what you can expect the trade-in value to be in five years and nearly any other piece of information you will need to make an informed decision. You’ve also probably shopped for hotels using data products, harnessed sites like zillow.com to research current and future estimated housing values and allowed devices like your DVR to suggest other shows you may like based on your previous history.

Finding Value

Companies benefit from data products in two ways:

Direct revenue:  Charging consumers for access to the data and analysis.

Indirect revenue:  Augmenting existing products or services, driving customer loyalty, generating cost savings or creating revenue through alternate channels. For example, you may be able to use the data product to research your car purchase for free, but the company that provides the analysis sells advertising on its site. Or, an organization may stay loyal to a particular staffing and training firm because of the data products they offer that generate analysis on retention, hiring costs and benchmarking information about other similar companies in their industry.

Both online and traditional companies are getting in on the game. In healthcare alone there is an estimated $300B to $450B in reduced healthcare spending achievable through data applications. For example,  Blue Shield of California is parterning with NantHealth to develop an integrated technology system that will allow doctors, hospitals, and health plans to deliver evidence-based care that is more coordinated and personalized. This will help improve performance in a number of areas, including prevention and care coordination.1 Data products like these are beginning to revolutionize the information we collect, moving it from a state of rest to a state of interaction.

Rules of Engagement

So what makes a good data product? While it may be tempting to think that any organization of a large set of data can be productized, that really isn’t the case. Here are some guidelines. Data products:

  • Go beyond simply putting data in pretty packaging. They contain unique connections to data such as benchmarking results; mashups of disparate, possibly public, data sources; calculated and/or composite metrics.

  • Generate insights and guide users to decisions rather than just supply data points.

  • Are designed with the audience in mind – not just how they will view the information, but how they will interact with it

  • Use data exhaust – consumer generated data that is “left behind” during their product interactions.  You can use this to improve interactions and product usage.

  • Are part of an evolution of how we want our audiences to consume information.

Follow-Up

Intrigued? We’ve barely scratched the surface on the data products and what they can do. Here’s a couple of items to add to your back to school reading list to really get your wheels turning.

  1. Data Jujitsu by DJ Patil.  An e-book, so its not a quick lunchtime read, but easy enough to digest in an evening.  Provides a really good roadmap to organize your thoughts and priorities on data products. You may find yourself re-reading it often like we do.  It’s on Amazon too and his SlideShare is pretty awesome too.

  2. The evolution of data products by Mike Loukides.  Some great examples and validation of how the data itself isn’t the product, but a means of “enabling their users to do whatever they want, which most often has little to do with data.”

After you’ve added these articles to your Flipboard or Zite, register at a local ProductCamp. There are a few coming up this fall, and they’re a great place to do some mingling with Product Managers, get tips on product thinking or brainstorm your data product ideas. Juice will be at the one in Atlanta, registration here,  in October –so if you’re in the area, drop by and say "Hi". Otherwise check out some other ProductCamps coming up this Fall all over North America.

 

1 The big-data revolution in US health care: Accelerating value and innovation, April 2013, McKinsey & Company

Photo credit: Dave Bleasdale

Customer reports: The Forgotten Touchpoint

Customer loyalty. It’s no secret that it’s a make or break aspect of business. Loyalty converts a one-time sale into an ongoing stream of revenue from the same customer. But what keeps a customer loyal? Much of the current literature suggests loyalty stems from touchpoints – those contact points between a customer and a brand that shape perception. These varied events occur throughout the customer journey cycle.

https://www.surveymonkey.com/blog/en/identify-customer-touchpoints/

https://www.surveymonkey.com/blog/en/identify-customer-touchpoints/

But you’ll notice something missing from the list above. Customer reporting. It’s virtually never listed as a touchpoint and yet so often a crucial component in shaping a customer’s view of your value - which ultimately determines their loyalty to you.

When Reporting misses the mark

Reviewing the images in the last post reminded me of an incident from a prior job.  Our Marketing Department spent significant time and money with an internet company to revitalize online adwords and boost search engine optimization (SEO). It was a detailed endeavor and I know they logged a lot of hours with the internet company prior to the go-live date to make sure everything was just right. Several weeks after the launch, curious if all the stress was worth it, I asked Marketing about the campaigns’ performance.

 

The answer I received was surprising. Unfortunately, they weren’t really sure. While their partner company wrote good copy, created great landing pages for the ads and put in place solid practices for boosting SEO, they didn’t provide useful reporting to help their customers measure success. The reports they delivered each week contained so little information that Marketing couldn’t determine which ads were performing well or why, so it was impossible to make decisions to know where to adjust. Logging into the portal provided by the adword platform had the opposite effect. The information there was overwhelming, and they could spend hours trying to decipher the countless pages of charts, drop down choices, metrics, etc. and still not really understand what they were seeing.

 

Reporting, whether obtained via self-service, delivered by a services team, an automated notification or through a customer portal is an interaction that a customer has with your company. And as such, every presentation of data should be treated as the valuable touchpoint that it is and seen as an opportunity to build loyalty and positively influence customer perception. Truly, this interaction should be considered as valuable as any other touchpoint in the overall customer experience. In the story above, if Marketing had been happy with the reporting they were receiving, the internet company would have had been able to convert them to a loyal customer. Marketing would have become a customer who went from receiving a one-time service to one who not only received on-going reporting services but also a customer who had the potential to drive additional revenue for the internet company by creating ads for other areas of the business, using additional services such as A/B testing or buying more products such as drip campaign creation.

What are some ways customer reports can be a positive touchpoint and increase loyalty?

1.     Make sure the report answers more questions than it raises and allows viewers to quickly consume the information that’s most important to them. This ensures customers are excited to receive the reports because they know they’ll quickly be guided to the answers they seek.

2.     Ensure the data presentation initiates conversations with customers.  Design reports such that customers think about the data and and can envision how they intend to solve problems with it.

3.     Think of it as a product versus a report.  You're not just sharing data, but giving customers something you want them to actively use.   Thinking like a product gives you a construct of thinking about how and when it will be used.

4.     Keep stickiness in mind. Provide information in such a way that it will become ingrained into the customer’s routine and way of conducting business. If done correctly, reporting becomes a driver behind renewal discussions.

Creating these types of data presentations isn’t easy.  They require the right level of data readiness and the knowledge of how to maximize their value.  Join us for our upcoming webinar on Turning Data into Dollars where we’ll walk through a series of examples on how to deliver more valuable information to customers and impact loyalty through reporting.

Common Myths Tied to Data Monetization

Bigfoot, Loch Ness, Chupacabra. We’ve all heard them: these stories gain traction because of their folklore element. Someone, somewhere, saw something that they couldn’t explain, and in the course of investigating, a fantastical tale emerged that captured the minds of many.   The same can be said of the business world. All too often you hear of “industry standards” and “best practices”.  It’s hard to pin down where they started, and often even harder to figure out why they’re perpetuated. Most frightening is that many of these standards or practices have actually morphed into myths. What may have once originated from someone, somewhere trying to help a specific customer in a specific situation has now seen so many iterations that it simply no longer holds true. When it comes to what works best when monetizing data, we’re finding many a myth that needs busting.

As a product manager, you’re probably facing growing pressure to package or “productize” your data. Your organization may be in search of greater return on their Big Data investment or could be looking to add incremental value to an existing product. No matter the situation, let’s go through some of the most common myths concerning the creation of data products.

Myth: No One’s Asked for It

Fact: While members of your organization may not be asking you for a data product explicitly, they might be saying it indirectly.  Their requests could be hidden in questions posed to you, your sales or support teams. Questions such as:

  • How do I compare to others?

  • How do I compare to the industry average?

  • Can I get more frequent access to my data?

  • Can others in my organization get access?

  • Does a summary version of this data exist for my boss?

Don’t wait for a specific product request, but listen to what they are asking the data to do. Don’t be surprised if more than one request is identified.

Myth: You Can’t Monetize Data That’s Already Owned

Fact: The value isn’t in the ownership of the data itself, it’s in the value add of the industry-specific metrics, customer benchmarks, and recommendations. The data itself is not what’s being sold, but the insights, metrics, algorithms, display, etc. that’s baked into the analysis. Remember all those questions in the previous myth? Providing thoughtful, easy to navigate visualizations that guide others to those answers is the key to monetizing data. Don’t position a data product as easy access to raw data, but rather as a solution that solves a problem.

Myth: More is Better

Fact: Not really.  This is probably the most common myth encountered and can often be one of the hardest to overcome. Those asking for data products often think that more is better – more data fields, more ways to “slice and dice” results, more metrics, more dimensions, more chart views. In their minds, they’re asking for flexibility to manipulate the data. The reality is that these requests almost always stem from uncertainty. They’re unsure what exactly to do with the data, so they figure they might as well as ask for all of it.

Our experience suggests that most users want to be guided to their answers. They want the data presented to remove uncertainty -- not just raise more questions. Users, particularly non-analytical ones, don’t invest more than a few minutes using data trying to answer their questions. Sure, creating the uber - report with lots of filters, a date range selector and the ability to download the report is pretty easy to implement.  However, if the member of your organization can’t easily derive value, then they won’t use what you’ve given them and even minimal efforts on a simple report download interface are wasted.   

For example, compare the two reports below. In the first, the user is confronted with so many filters, columns and data points that making an informed decision from this information would be extremely time consuming. In the second, the user is immediately drawn to the key pieces on data that are needed to quickly understand the important details of what is happening and what’s driving the information.  Investing time on designing for the data consumer and providing them a clear path of guided exploration is the way to go.

Assembla filtered view
Slice campaign example

So tell us, what’s the most outrageous request you got for a “more is better” data product or visualization report? Send in your stories to info@juiceanalytics.com with “more is better” in the subject line and we’ll pick our favorite. The winner will get to spend two hour with us and together we’ll turn that unwieldy request into something functional, informative and cool. Bonus points if there’s a snapshot of the current report attached to your story!  

Think you’ve run into a data product myth that is more mysterious or pervasive? Drop us a note at twitter.com/juiceanalytics.

 

 

A Fantasy Visualization for Fantasy Football

At the height of my fantasy football obsession, I probably checked the score on my match-ups more than 50 times a week. As NFL football fan, you have lots of time to do such things -- if you have fantasy players in all four game times (Thursday night, two Sunday games, and Monday night), you have around 13 hours of televised games a week.

This year I quit my fantasy football league. I'm not saying it is because the fantasy football site we used didn't present the data in an interesting way, but an awesome visualization might have made a difference for me. With such a dedicate audience, Yahoo!, ESPN and the rest would be wise to create an great way to track the performance of your team versus an opponent. Here is a blueprint:

fantasy_football_visualization.png

This visualization would answer the important questions as I obsessively dissect the scoring:

  • How am I trending in my match-up? That is, am I on pace to win? Most fantasy football systems have built prediction engines to project out results, but these results aren't shown in a chart.
  • How are individual players contributing to the scores? The trend lines show when and how a player is scoring. Rolling over the points in the line would reveal the big plays that are helping or hurting your cause.
  • What confidence can I have in the projected outcome? The dark parts of the chart are actual points earned whereas the lighter blue is projections. As your column chart "hardens" into dark blue, you can have confidence in the final tally.

As I pointed out recently, Fantasy Football has done an amazing job of making more people data literate. Why not finish the job with a great interface for team owners to spend their weekends cursing over?

Fantasy Football is Teaching Data Fluency

 

Fantasy football season is here again (along with the actual NFL season). I thought it a good time to share a section from our upcoming book Data Fluency, scheduled to be published in October through Wiley and with Nathan Yau of FlowingData as editor. In this excerpt, we suggest that Fantasy Football has taught an enormous audience to understand the language of data:

It may not be a stretch to say more Americans have learned about data and statistics through fantasy football than every college statistics course in the country. Each week, some 19 million NFL football fans spend their Sundays meticulously setting team line-ups based on statistical projections, historical patterns, and analysis of week-to-week variance. The couch potatoes who once relished on-field hits and in-game strategies now spend an average of more than eight hours a week diving into the data of the sport.

For the uninitiated, fantasy sports let fans play the role of team owners and managers by picking players for their own fantasy team and making weekly roster decisions. As the action plays out each week on the field, fantasy owners collect points against other competitors within their fantasy leagues. To win, fantasy owners quickly realize that success often depends on studying player and team performance data closely.

Here are a few ways that NFL fantasy players incorporate data into their thinking:

Variation in Player Performance

The best fantasy owners understand the nature of week-to-week variance and its relationship to earning points. For example, touchdowns generally earn a fantasy owner six points; but touchdowns occur rarely and can fluctuate wildly. In contrast, the number of touches players receive may be a better indicator of how much the team is using them and their opportunity to provide the owner with points. Because consistent performance matters, successful owners often focus on players with more stable predictors of success (for example, touches) versus more sporadic events (for example, touchdowns).

Rankings Can Be Misleading

Fantasy football cheat-sheets offer rankings of players in every position. These ranking mask the differences and dispersion of expected performance. For instance, the top running back may be expected to perform 20 percent better than the second rated running back, who in turn is only expected to score 5 percent more points than the third through sixth rated running back. The data shows that players often cluster into tiers of performance. This statistical understanding was publicly explained by Boris Chen who stated that “players within a tier are largely equals. The amount of noise between the ranks within a tier and actual results is high enough that it is basically a dice roll in most situations.” This concept has been widely adopted by fantasy owners as a player drafting strategy. 

The Only Constant Is Change

The worst fantasy football owners are stuck in the past and pick players and teams that they have relied on in the past to generate points. That is, they fail to update their assumptions about the best teams, players, and trends. Following the data closely reveals when certain players have gone past their prime and when teams that once had high-scoring offenses can no longer put up big points. Clinging to past success may be a formula for disaster because the only constant in fantasy football is change.

Context Fills Out the Picture

Data viewed in isolation can be deceiving. Say, for example, that your top wide receiver scored only one-half the number of points that he scored on average in a season. Is this a new and troubling trend? Should you trade? A little research might reveal that he matched up against one of the league’s top cornerbacks, or his quarterback was knocked out of the game, or perhaps he tends to perform poorly in cold weather, away games. These environmental factors make a difference with respect to outcomes. Performance data cannot be understood in isolation—context matters.

So how did fantasy football create legions of fans who have developed a specialized dialect of data fluency? It has been a combination of education, effective data presentation, common data conventions, and incentives. Fantasy football owners have been taught how to use data to their advantage through the efforts of the NFL, ESPN, Yahoo!, and a cloud of other websites dedicated to football analyses. Organizations like Football Outsiders built new media businesses around data modeling and projections of player performance. 

Leading online fantasy football sites like ESPN and Yahoo! have been aggressive in pushing data and data visualizations to their users. These sites include trend charts for every player, drive charts, player comparison graphics, and predictive models for estimating game outcomes.

The educated fantasy football community is also highly engaged with the sport. The community loves football! The fantasy league has provided a whole new (and rewarding) dimension to its fandom. No longer is it tied down to rooting for a single team—instead, the whole league becomes fodder for its attention as it picks and chooses players from each of the 32 NFL teams. In addition, the fantasy football industry has coalesced around consistent formats for leagues, points, and key metrics. Terms like PPR, running back by committee, waiver wire, and flex are well understood, facilitating conversations among league owners. And with $1.18 billion bet in fantasy football leagues annually and a passionate fan base, fantasy owners have huge incentives to make informed decisions. When money or bragging rights are on the line, individuals invest time and energy into developing the skills and abilities to become data fluent.

In short, these factors have brought data fluency to the masses. Millions of fans have learned how to read charts, grasp basic data concepts, and allow deeply embedded data to inform how they make decisions—all critical skills associated with quadrant one in our framework. 

Visualization techniques we all knew at 4 years old

A few years ago my niece sat down at the table with me and drew a picture. Here it is:

An afternoon drawing by my niece. Marker and Paper.

Whether you play with $100k dashboarding tools or the latest and greatest open source reporting solution, they have no secret sauce in the visual thinking department that wasn’t already exhibited when you were 4 years old and drew something for your uncle. 

Let’s walk through 6 principles of visual comprehension I observed after she drew it. The 6 aren’t meant to be all encompassing nor the only way to interpret these visual principles, but they are fundamental aspects of what makes data visualization so special. I like to think of them as parts of the grammar for speaking visually.

Let's see what each principle would say...

Things that are enclosed by a shape will be seen as a group.
— Enclosure

First, she drew me. Yes, that’s me to the right. She started with my eyes, nose, and mouth. Then she grouped those items through the principle of enclosure to say, “Here’s James’ big head and all those facial features belong inside it.” 

 

Young children learn to read the face first, so this becomes much of your identity to them at an early age. In fact, the rest of my entire body is represented by two lines. Somehow my big head does need to move around after all. 

Young children learn to read the face first, so this becomes much of your identity to them at an early age. In fact, the rest of my entire body is represented by two lines. Somehow my big head does need to move around after all. 

Things that are connected are part of the same group.
— Connection
 

Thankfully, she also acknowledged my hair. It’s not floating in mid-air, but touches the enclosure of my head to say, “While not inside, these things in the group of things that make up James." 

Things that are near each other belong to a group.
— Proximity

Next she drew her face and body next to me — her way of saying, “We have a relationship. I like hanging out with him.” Perhaps, if I was that weird uncle in the family she would have been on a corner of the page, but instead this proximity indicated that we are in the group friends and family. 

Things that are aligned are perceived as a group.
— Continuity

Not only did she draw herself close to me but also on the same vertical plane. She's a rather grounded girl, so, instead of drawing her floating about, she emphasizes that we’re both in the group of things that obey the laws of gravity and stand on the floor. 

Some other objects around us have the freedom to fly about. They’re just principles after all; not laws.

Some other objects around us have the freedom to fly about. They’re just principles after all; not laws.

We strive to perceive shapes as complete.
— Closure

When drawing her ears (with earrings, of course), you can see how the circles were sure to be complete through the overlapping beginning and ending of those lines. This assurance of closure says, “My little ears can definitely support big girl earrings.”  

Things that share color, size, or shape belong to a group.
— Similarity

I imagine she drew the cactus floating above us because it has a visual similarity to her hair (which she drew after my hair). This is her saying, “What else could I draw that would belong on this page? I’d like to draw a cactus. That feels right.” 

Lest you doubt it is a cactus, I asked her at the time and, no doubt about it, it is obviously a cactus. The swirling thing to the right is also obviously a snail.

Lest you doubt it is a cactus, I asked her at the time and, no doubt about it, it is obviously a cactus. The swirling thing to the right is also obviously a snail.


One thing I’ve learned is that design or visual comprehension principles make more practical sense looking backwards. We all have various beliefs or observations of the world that have been internalized and are unique to us. We probably don’t even know what many of them are — just as I’m fairly confident my niece hadn’t studied the laws of gestalt grouping from the early 30’s when she sat down to do this drawing. When you want guiding principles to guide a new product, look back at what is most natural and pervasive. 

We all were born with this visual grammar, and they have been incorporated in all sorts of data visualizations and products in recent years. One hope of mine is that we’ll start seeing data products that allow us to not just see the data, but see through it to those “aha” moments, where people are seen and lives are truly impacted, where insights are revealed as effortlessly and confidently as drawing a picture on a blank page.