Gift Ideas for Data and Visualization Lovers

It's that time of year again. Thanksgiving is just a few days away and soon you'll have to answer that question you dread every year - what to get for your data-loving friends for Christmas? Clever gift giving is not easy so we're here to help with some great suggestions. 

Books

A great option for someone who loves data and loves to read. Also a great choice if there's a book you'd really like to read. Get it for them and when they're done, you can borrow it. Win-win. Here are a few books we love:

Storytelling with Data

Data Visualizaton with JavaScript

Data Fluency

Visualize This

Coolness Graphed

Posters/Prints

You can find lots of neat visual gifts on Etsy, from infographics on Zombies, to periodic tables of Game of Thrones, the perfect cup of coffee art, to world map canvas art.

These prints from FlowingPrints.com would also make a great gift.

Visual Family Tree

My Tree and Me - a fun and unique way to visualize your family tree. Just in case you need to answer that timeless question, “how are we related to him again?"

Do you have any great gift suggestions we missed? Leave them in the comments below! Happy shopping!

Building Customer Loyalty through Reporting

Over the past few months we’ve heard numerous stories, like the SEO reporting example highlighted here, where organizations are losing customers or renewals are in jeopardy because of poor reporting.   It hasn’t been specific to one industry either.  We’ve heard this story in healthcare services, software maintenance renewals, ad agencies, etc.

What we’ve noticed as a common thread across these cases is that reporting is viewed as a compliance activity or requirement, not an opportunity to connect with customers.  Reporting or the sharing of insights, is rarely thought of as a means of educating customers, sharing expertise or part of the overall customer experience.   

While there are many opportunities to build customer loyalty using data, i.e. using predictive analytics, to personalize offerings, we’ve created a short, no registration, e-book, Building Customer Loyalty Through Great Reporting, to articulate how a valuable Information Experience TM can enhance the overall customer experience you deliver and round out all your touch points.

The e-book is an easy late night Kindle read or lunch time scan.  Please check it out and let us know what you think about the relationship between reporting and your customer experience.  You can download it by clicking here.  

For a demo of our product, Juicebox, schedule an appointment.

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Emotional Dashboards - Moving from confused to happy customers

Just like hearing that popular song from high school can elicit certain emotions, so too can a dashboard. Intuitively, the words “emotional” and “dashboards” don’t appear to go together. However, it's not difficult to imagine some four letter words being thrown around because “the numbers don’t make sense” or because your audience is frustrated that they can’t understand what you’re trying to say.

Sound familiar? When dashboards do not connect for your audience this is the sad intersection of lost clients, wasted time and dollars.

Today’s dashboards have the power to do more and be more than their predecessors. Just as modern web/application design marry utility, usability, mobility and beauty, so the same can be said for our information displays. Your organization’s data, when presented correctly, should command attention, start a conversation, compel action AND strike the right emotion.

How do you trigger the right emotions with a dashboard?

Frustration #1: Which information is most important!

Unfortunately, more often than not the heart of the designer’s message is lost among all the metrics and chart. In this flurry of enthusiasm to get something done, little attention is paid to guiding the user on how to consume the information, so they get what they need. Take a second look at your dashboard and ask what should be the first thing they see? Will they know it’s the most important.

Frustration #2: There’s too much detail!

Don't get caught in the myth that more is better. Your users probably have other responsibilities other than looking at your work all day. Give them the high-level path to follow, and let those users who need more info have the option to drill down into the details which can be collapsed under the high-level data point, or linked to an appendix, or included in separate report.

Frustration #3: The dashboard looks like Gaudi made it!

Overzealous graphics, too much color, images, etc can cause more harm then good.  Don’t get us wrong, Gaudi was an amazing artist, but, when displaying valuable analyses save your modernism impressions for some other endeavor. Be purposeful in how you use these elements.

There is no need to let those emotions go unchecked when it comes to displaying your dashboard results. To ensure you are triggering the right emotions and ensuring your dashboards are delicious, Download the emotion-filled Juice white paper “Designing Dashboards People Love”. Just updated to reflect the latest in Data Fluency.

Metrics that really matter - common pitfalls to avoid

No matter what business you are in, keeping a competitive edge is essential. Being able to evaluate your performance and extrapolate the next steps is essential to a successful  business model. Just like a judge at the Westminster Dog Show, you will need a host of metrics to scrutinize a good performance.

So what is the key to winning in the dog-eat-dog world of business? Tracking and analyzing of metrics, of course. Your metrics can create focus and alignment in your company by providing clarity to what improvement looks like. Although be warned, they can also lead a company astray if not carefully selected.

5 Common pitfalls to avoid when choosing metrics:

Historical conventions translate into blindly following conventional wisdom or history without giving thought to the implications. In an ever changing business climate, you stay on top by being adaptive and responsive.  A Westminster judge is not going to vote for the posh poodle just because the previous two winners were poodles.

Simplistic metrics means taking only at face value what data gives you. Just because the data is easy to track does not mean it will lead your business to the front of the pack. For example, in a two day dog competition with many different breeds of dogs, easy to obtain metrics like weight and height, would not be enough to help you discern a deserving winner.

Complex metrics are contrived metrics that combine data from many sources. If your goal is to shape company behaviour to increase success, then it is imperative for people in the company to understand how the metric was created and trust the data source. Otherwise they may be skeptical of the metric. The metrics for Best in Show are transparent for all involved. This is imperative when you are dealing with dogs of all shapes and sizes. The dogs are first judged within by metrics within their breed. As the competition continues grouping is based on the jobs the dogs were bred to do.

Too many metrics, also known as data overload. Typically, this occurs when you are working with dozens of key metrics because they all mean something, but they may not all deserve to be called “key”. This is why grouping and filtering down is important.  If you a had to choose the winner of Westminster on day one with all 2,500 contestants present, that would be overwhelming!

Vanity metrics are just what you think they are. These are the metrics that make your organization look good, but don’t necessarily tie to important or relevant outcomes. The dashing dachshunds might look dapper on the runway but how well did they perform in the other areas of competition?

By avoiding these pitfalls, you can create data products that will lead your team to meaningful decisions and actions.  Accurate tracking of data and analysis is the key to your company unleashing its earning potential and staying ahead of the competition.

Find out more about effective data visualization from our book, Data Fluency.

Get a free excerpt from the book! (enter code: FLUENCY-EXCERPT)

Excerpted with permission from the publisher, Wiley, from Data Fluency: Empowering Your Organization with Effective Data Communication by Zach Gemignani, Chris Gemignani, Richard Galentino, Patrick Schuermann.  Copyright © 2014.

4 game changing strategies for information discrimination

We’re pretty excited about the upcoming Women’s World Cup as well as all the soccer (football) games we’ll get in Atlanta and Nashville this Summer.

All these matches made us think how much authoring data for an audience can be like a preparing for a game or a PK (penalty kick). Distractions and extra information are your enemy. As a data author intent on having your audience understand (get) what you’re doing, you need to prioritize what information really matters. Here are some thoughts around keeping focused and having the biggest impact possible on your audience:

1. Find the heart of the  issue - your data product should have a core theme which is based on the essence of the issue. For the sales team the big question might be “How can we generate more leads into our pipeline?” Honing in on that core question can help you eliminate information that isn’t helpful.

2. Ask a better question - “What would you like to know?” might generate a long list of responses. To help narrow down the list, follow that question with “What would you do if you knew this information?” This second step will help you decided what data is actually needed.

3. Push to the appendix - Of course there will still be times when you are required to include all the data people might want to see. Utilizing an appendix can ensure the information is available but doesn’t detract from the data product’s main purpose.

4. Separate reporting from exploration - Reporting and exploration are two separate processes. Know which purpose you are designing for. Just remember, tools designed for reporting should address specific questions and stay on topic. On the other hand, tools designed for exploration or analysis will provide a broader palette for users explore a variety of data.

Staying focused and incorporating these strategies will help you create data solutions that are useful, productive and interesting. After all, isn’t that the goal :-) ?  Enjoy the matches this summer!

Find out more on effective data presentation strategies from our book, Data Fluency.

Get a free excerpt from the book!

• Visit www.bookshout.com/datafluency

• Enter the following code: FLUENCY-EXCERPT

• Follow the sign up steps

• Download your eBook excerpt

Excerpted with permission from the publisher, Wiley, from Data Fluency: Empowering Your Organization with Effective Data Communication by Zach Gemignani, Chris Gemignani, Richard Galentino, Patrick Schuermann.  Copyright © 2014.

Data doesn't speak for itself

Data is important, but by itself it’s nothing special. Much like words in a dictionary, data needs a voice to give it meaning.

Data can’t speak for itself and just turning data into charts won’t do it either. You need the fundamentals; a design-first approach, knowledge of the business, and data communication skills.

Instead of racing to the finish, focus on what it takes to make your data useful.

1.    Design First - Don’t just ask what’s possible, ask what’s useful. It’s really tempting to use the latest chart or Minion Yellow color. Figure out what purpose you are trying to solve for.  Think about what questions your users actually have. You might think you know, but asking them is a sure way to know you’re on the right path.

2.    Knowledge of the Business – If you’re not the expert go find one. No matter the amount of data or your chart sophistication, you’ll need to convey the importance and validity of what you’re trying to communicate.  Getting users to buy in requires expertise.

3.    Data Communication Skills  – Figuring out the right charts or visualization that communicate to your audience is an important part of comprehension. Since you’re telling a story, layout and/or sequence is important too. Remember you’re guiding the user through the information, kind of like an instructional manual.

Ultimately if there is no understanding, then you haven’t given your data a voice and it's just hot air. Be objective about you and your team’s capabilities. Not all teams possess these fundamentals. Once you’ve decided where your team might be lacking, you are better equipped to find the right help and ultimately turn your data into action.

3 Important Questions to Glean Insight From Data

Never take anything at face value. We know that Sherlock Holmes was known for solving mysteries using his keen sense of observation and ability to ask the right questions.

Much like becoming a successful sleuth, developing data fluency means learning how to ask some simple questions from the information in front of you. Training your eyes to see the patterns and anomalies as well as asking these critical questions will help you use the clues to get the most out of a data product.

With the components of data and structure under your belt, we are ready to ask the important questions. Let’s see which insights can be revealed from your college search data (from last week’s post) by digging a little deeper.  

1. Where does the data product come from? Knowing the origin of data is just as important as seeing data. Understanding where data comes from means knowing how data was collected and how it was processed before you received it.  It also means considering what the goals and biases of the author of the data product. Following our college search example, we know the national college board develops the rankings, thus providing a neutral perspective.  

During the data origin process, it is also important for you to figure out what the scope of the data is. If the universities in the top 10 studies are only from the Northeast -- then you know that you may want to collect some more information from other geographic regions to compare. Sometimes hidden gems of information lie in the individual relationships or the outliers. Meaning if you are only seeing the averages, you may miss a significant fact in the information specific to your search. Perhaps, Northwestern University was further down the rankings due to a specific dimension classification in the data. Being able to go back and question outliers may reveal their admission rate was 1% but in all other categories the school would be a best match for you.

2. What can you learn from it? Your ah-ha moment! This is when you move from comprehension of what is it to what it means. Data visualization helps you see data results easily and determine if they compare to your expectations - thus encouraging you to do more of the same. Or alter your course, if results were unexpected, and take new action. So you were able to filter down your search results to 15 schools that best match the criteria you are looking for - tuition, retention rate, class size and academic discipline emphasis.

A skilled data author will create data products that emphasize the message to be conveyed. Many different tables, charts and graphs exist and it is an art to be able to choose the most effective visualization. Whether you are looking at a pie chart, bar graph or dashboard, always begin your analysis of each data product with a focal point of a small area. You can build from there. By breaking down complex data into its smallest pieces and finding something comprehensible, you can start to understand both what the author is trying to show and how to read the content.

3. What can you do with it? Now that your eye for discerning data is more discriminate, you can tackle the last question: what now? You are ready for action. The data draws a direct and obvious line between the implications of data and specific actions and decision making. Now you and your child are making your own top 10 list of schools, prioritizing the universities and then applying. This will save you money in the long run and ensure you made the best decision concerning higher education for your child.

Best Practices to Strengthen Your Data Language Foundation

• Keep an eye out for unexpected distributions, patterns or relationships, and unexpected trends. Like in our example, when Duke wins the NCAA basketball championship, students seeking admission always increase and influence ranking results.

• Look at comparisons to give context. Reviewing performance results from one year to another provides a historic perspective for further investigation. 

• Find a starting point and filter down based on findings at the individual level. For example, if it is most important for your child to go to a college in the top rankings and close to home. Start there and build your list.

• Find actions you can take and do something about. Apply to those universities that are the best fit! 

Just remember, knowledge is having the right answer. Intelligence is asking the right questions!

Excerpted with permission from the publisher, Wiley, from Data Fluency: Empowering Your Organization with Effective Data Communication by Zach Gemignani, Chris Gemignani, Richard Galentino, Patrick Schuermann.  Copyright © 2014.

3 Building Blocks of the Language of Data

Hiring managers say that one of the hardest skillsets to find in an employee or candidate is the ability to understand and communicate with data. Discussions around data are becoming an essential part of our personal and professional lives - yet most of us struggle with this.

These data-focused conversations move a company forward by going beyond the basic “what is it” and getting to “what does this mean and what can we do”. Ultimately, the goal of data is to foster better business decisions. To be able to speak the language of data, you’ll need to begin by knowing the structure - the essential building blocks - of data and how they work together.

Understand the components of data

Let’s take an everyday example: you are helping your son or daughter look for the right college. You want to help them make the best choice possible, yet there are so many factors to consider. Good thing publishers like US News & World Report have put together data products to help you wade through the pool of information.  

Using our college search scenario, we’ll illustrate the basic building blocks of charts, tables and other data visuals.

1. Elements: The person, place or object doing something. When you are checking out one of the college lists like National University Rankings, the name of the university represents an element. You can also think of it when looking at a table of data (see below), where each row of information represents an element -- in this case, Princeton or Harvard.

2. Dimensions: These describe characteristics of the elements involved. Dimensions are details which you can use to understand the story about the element. Dimensions can help you narrow down your list with filtering and can be found in the columns of a table. A dimension you might use to filter by when looking for colleges would be location - which state a college is in.

3. Metrics: Metrics answer questions like how much or how many? Metrics are easy to see because they are objects and/or actions analyzed in a mathematical expression such as the sum or average. So in your college search you may want to know the graduation or freshmen retention rate. The metrics can also be found in the columns of the data table.

Most of the time when we interact with data it’s already summarized, such as the Top 10 Colleges. The analyst has grouped certain dimensions together and highlighted metrics to help the us find the right schools.

Use the data building blocks to review tables and charts

Take a minute to visualize these building blocks coming together, each serving a role in the explanation of the data story. Each row in the data table tells a story about the element. The dimensions and metrics are characteristics of the element. You can filter by dimension, summarize by adding up or taking an average of the metrics, or break elements out by dimensions for comparisons.

Now when you look at the data table above you see the story told about each school. This helps frame what the data is about and how to compare it. The element gives you the individual perspective and the columns give the bigger picture. As you take control of the data table, remember you can remove the columns that aren’t helpful to allow you to focus on those that are useful. For example, you might be looking at schools under 45k tuition with less than 10k students enrolled. This narrows your results to a single element, or school, Princeton it is!

Congratulations, you now have the data building blocks ready to construct a new pathway of interpreting data. As a next step, we will dive into gaining insight from the data and visualizations as well as the different questions to keep in mind when analyzing information.

Find out more on effective data visualization from our book, Data Fluency. …. and make data more delicious for everyone.

Excerpted with permission from the publisher, Wiley, from Data Fluency: Empowering Your Organization with Effective Data Communication by Zach Gemignani, Chris Gemignani, Richard Galentino, Patrick Schuermann.  Copyright © 2014.

Creating Tools for Data-Informed Decisions

Data-based decisions. It is a phrase that has become dull with overuse. It even suggests that choices are made obvious if you have the right data.

Data-based decisions seems to ignore the fact that decisions, for the most part, are still made by people — people who have colleagues and bosses and customers and stakeholders to balance. Results shown in data often run counter to long-held assumptions or exclude important, unmeasured factors. Data-based decisions aren’t make in a vacuum; they are made in our social, political workplaces.

Perhaps we’d be better of focusing on a similar phrase: data-informed discussions. Before a decision can be made, there is a discussion. Data can, and should, influence these discussion. It is left to humans to sort out options, weight outside factors, and evaluate risks that are not evident in the data.

Consider any discussion you’ve had recently that should be informed by data…a political discussion about climate change; optimization of your marketing tactics; education options for your children. Why is it so hard to bring data into these discussions in ways that enlighten and encourage smart decisions?

A few of the common problems include:

• Misalignment about the nature of the subject, the decision to be made, or the meaning of concepts;
• Failing to make assumptions explicit;
• Different conceptual models of how the world works;
• Different sources of information, leading to conflicting results.

Ideally, these issues would be addressed ahead of a discussion. What if every difficult discussion could be framed by data so the dialogue could focus on the most important things?

A couple years ago we tried to answer that question for one specific discussion that happens all the time. The tool we created — inelegantly called the “Valuation Analyzer” — facilitates discussions between start-up founders and investors. The debate centers around financial projects, value of the company, and the ultimate payback for equity holders. Our tool is intended to get these two parties on the same page.

Here what it looks like:

The Valuation Analyzer provides common and explicit ground rules for estimating the future value of a company. Users can define a small set of assumptions and instantly see how those inputs impact valuation and return on investment.

Each of the items underlined in blue can be adjusted by clicking and dragging left or right (thanks to Bret Victor’s Tangle library for this input mechanism). Most of the important assumptions are in the lower half of the screen: Do you want to calculate valuation based on revenue or EBITDA? What multiple will you use? How will revenue/EBITDA change over the coming years?

As you make adjustments, the values and visualization updates instantly. As a special treat, we added a “founder ownership” option (see the button under the title) to answer the most urgent question for any start-up founder: what’s my potential financial outcome?

Finally, a scenario can be saved and shared. The save button will keep all the assumptions you’ve entered and create a unique URL for that scenario.

This tool sets the parameters for a productive data conversation. The dynamic sentence across the top explains the meaning of the content. The assumptions are flexible, obvious, and explicitly stated. Two people sitting across a table can focus on the important values that impact equity owners’ outcomes. The discussion can happen in real time and the results can be saved.

It would be impossible to have a data discussion tool for every situation — there are too many unique circumstance. Nevertheless, this tool provides a model for those opportunities when a discussion happens time and again.

At Juice, we’ve found that organizations are so caught up in the race to capture and analyze data that they’re rushing past the most critical component – the end user. The best data in the world is useless if the everyday decision maker can’t understand it and interact with it. We’ve created a solution. Juicebox [www.juiceanalytics.com] delivers a more thoughtful approach to data visualization. We think about data conversations, not just presentations. Instead of just presenting data, we create more ways for people to interact with, socialize, and act on the data.

End data-phobia: 3 visualization pitfalls to avoid

Here’s the situation: you are at your department’s quarterly meeting and your boss’s boss is presenting a number of charts and tables from the reporting dashboard. The data visuals are not telling a clear story and most are struggling to join the discussion around what impact this data has on the department, which is the entire point of the meeting. In that moment, you really appreciate the kind of graphics that make it easy to “get it” with a glance.

The confusion created from unclear visuals is a common scenario, but it doesn’t have to be that way. Just think of all the visual data we encounter in our everyday lives -- we constantly consume and interact with it. And when done right, the data can help us understand our world and engage curiosity.

Take for instance the weather. Most of us check it everyday to know whether to grab a jacket or an umbrella. There’s usually a nice graphic to quickly get that info across.

And on your way to work, you might check the GPS for traffic and route alternatives. Heading home, you stop by the grocery store and grab a bottle of wine and pick up ingredients for dinner. The wine label even pairs the food for you!

We are looking at data and making decisions all the time. We habitually checkout these data products on our smart devices which are sophisticated yet digestible, so we continue to use them. But what happens when we can’t digest and just don’t get it? Data phobia!

Sadly, most of us suffer from this illness because the data products we depend on don’t tell a clear story. While it often takes more than just ordinary visuals to communicate a complicated message, some cues can be taken from everyday examples to help make complex data more accessible.

Here are 3 pitfalls we’ve found that hinder a user’s understanding of data:

1. Expecting end users to understand your jargon. It may make you look smart, but it makes others feel dumb. Ever see the finance manager presenting to a room full of HR and marketing execs about EBITDA and CAPEX? Consider how you can make the message meaningful to your audience.

2. Dropping end users in the weeds. Give end users a clear and simple starting point, help them out with high level summaries and allow them to progress logically through the data, gradually intruding more complex details. Eliminate those weeds and provide a path from simple to complex with clear visual hierarchy.

3. Telling conflicting stories with different data sources. Make sure not to provide data from different sources that tell conflicting stories. Don't make your end users have to guess which story belongs with which case. Data should be good enough to help a user make a decision and move the discussion forward.

Now take a look at some of the data products you’ve created. Do they pass the pitfall test? It’s always a good idea to run some of the visualizations by a sample of your end users.  Get feedback on whether they can glean insights, and if it spurs discussion and decision-making. If not, find out what is confusing to them. Drill in and think about how to simplify the message, construct a consistent story, and guide the end user down a logical path.

Find out more on effective data visualization from our book, Data Fluency. Do your part to help end data phobia and make data more delicious for everyone.

Get a free excerpt from the book!  Enter the code: FLUENCY-EXCERPT

Excerpted with permission from the publisher, Wiley, from Data Fluency: Empowering Your Organization with Effective Data Communication by Zach Gemignani, Chris Gemignani, Richard Galentino, Patrick Schuermann.  Copyright © 2014.