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!

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.

Are you asking the right questions?

How was the game last night?  Depending on who you ask, the responses will vary. The person in box seating may say they had the time of their life. The person in the nosebleed section may say they couldn’t see anything.  The person behind the tall guy with the big hat may say it was awful. Everyone is talking about the same event with a different perspective.  So how does your data product tell the story?

As a data producer, your product needs to start with your audience in mind. Who are the data consumers and what do they already know? Often times there will be multiple audiences with varying roles and positions utilizing the same data product. Take for instance the marketing analyst who needs to dig around in the nitty-gritty details and find the underlying reasons, details and insights. Another user, like the CMO, is extremely busy and needs to see top-level metrics that are simple and clear.

So what’s the secret for designing a data product that speaks to multiple audiences? Ask the right questions! Good questions and knowing your audience will inform the structure and design of your data products. Picture your audience. Think about the role they play in the organization. Envision their workflow at the office. Get a feel for the competency your audience will have with data, numbers and your industry lingo. By using these characteristics you can derive the right questions.

The chart illustrates the logic behind well designed data products for multiple audiences.

So does that get put into practice?

Every day we see data products that consider the audience - or not. Do you ever look at the forecast to see if you need an umbrella that day? When you check a weather source, you may be hit with a 5 day or 10 day outlook, with several data points on sunrise, sunset, dew point, humidity level.  You get the picture. But you really just need to know if there is a chance of rain! So checking a site like DoINeedanUmbrella.com may do the best job for its audience.

The authors of this data product knew exactly what their audience was after and they get right to the point.

Now you are on your way to designing the kind of data products that effectively communicate company data and generate meaningful dialogue all the way through the company!

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

Get a free excerpt from the book!

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.

6 Principles For A Guided Conversation With Data

You may have heard the Native American proverb about stories. "Tell me a fact and I'll learn. Tell me a truth and I'll believe. But tell me a story and it will live in my heart forever." Storytelling is a huge part of the human experience. But there are often multiple stories hidden within one data set. It’s important to remember that data is less often about telling a specific story and more like starting a guided conversation and letting the user find the story that fits their need. Think dialogue instead of monologue!

When communicating with data, your product should pose a problem and deliver insight to lead the audience to take action. Do you remember the Choose Your Own Adventure Series? These children’s books were very popular in the 80’s and 90’s, allowing the reader to become part of the story. They are a great example of how to turn a linear story or monologue into flexible dialogue or a guided conversation. As the main character, you were able to take in the information provided and take action by deciding what to do from two or three options. Each of which led to more options, and then to one of many endings.

Now, that you’re geared up to put those critical thinking skills to use, let’s make sure you have the principles to help you design for guiding the conversation.

1. Find the purpose and message of your data products and know your audience. Know what information is most critical for your audience's decision-making, and what questions they need answered to be more successful. Ask:

  • What outcome are you looking for?

  • What do you hope to change in your organization by creating this report, dashboard or analysis tool? 

As you design the data product, understanding the audiences can help you craft a product that fits their needs and is something they love to use.

2. Be discriminating with what data you present. The most common mistake in data products is the inability to make decisions on what information is most important. This lack of focus often results in a directionless and sprawling document -- drowning your audience in data. Remember, you know the data and you know your audience. Distinguish between what is simply interesting and what is really relevant.

3. Define metrics that are meaningful and can lead to action. Metrics are the values that you use to judge performance. They are the numerical reflection of the real-world behavior that your organization wants to improve, avoid, or shape. Metrics create focus and alignment in an organization by providing clarity on what improvement looks like. Metrics can also create behaviors that are counter-intuitive or contrary to organizational goals. Only the right metrics and most actionable data should be featured in a data product to make the most of your audience's attention.

4. Create a logical structure and narrative flow for your data product.

How you choose to lay out the information shapes how your audience understands the big picture and how the smaller pieces fit together. Ask:

  • What is the general structure of the content you want to communicate? 

  • How does the content connect? 

  • How does one data or visualization element flow into the next? 

Whether it is a dashboard or a data-rich presentation, the structure of your data product is an opportunity to define the logical way to look at a problem or the business.

5. Master basic design skills for making your data presentation attractive and easy to understand, including choosing the right form and language to present the data. Your next challenge is to consider how the data looks, in what form it is delivered, and how words are best incorporated to facilitate understanding. You can start this process by considering factors that will influence the format in which you present the data:

  • Timeliness - How frequently is that data updated? 

  • Aesthetic value - How important is if that the data presentation looks attractive, or can it be purely utilitarian? 

  • Mobility - Does the audience need to access the information through mobile devices? 

  • Connectivity - Does the dashboard need to connect to live data sources or can it be updated on a less frequent basis? 

  • Data detail - Will the data product offer a capability to drill down to see more context?

  • Data density - How information-rich will views of the data be? 

  • Interactivity - Will the user benefit from interacting with the data? 

  • Collaboration - Is it important that your audience can easily share and collaborate with others about the data? 

6. Create data products that serve a broader audience and start a dialogue.

These products do not simply facilitate the flow of information between people. They also add tremendous value to the data they are communicating by analyzing, summarizing, structuring, storytelling, visualizing, and contextualizing. It takes many diverse skills to be good at designing data products.

Begin with these principles and start a dialogue around your data in a logical and structured way! Find out more on guiding the conversation around your data from our book, Data Fluency.

Get a free excerpt from the book!

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.

5 Key Qualities of a Data Product Producer

To build an effective data product, the data product producer has a big job. He must bridge the gap between the data and the intended audience with visuals that resonate and spark discussion. The data product should inform, instruct and lead people to smart discussions, decisions and actions.  To be able to do that effectively, a data product producer needs to have 5 key qualities:


1. Data authors know their audience. Much like the savvy salesperson, they consider how to best capture their attention, how they perceive data, and how to gain their buy-in. Ultimately leading the audience to take action. You probably wouldn’t buy a car from a sales associate who didn’t understand your specific needs and taste.

2. They have empathy. Like a therapist, a data author has the desire and ability to understand and share the feelings of others. They ask thought provoking questions to get in the hearts and minds of their audience. What will motivate them to take action? What are they afraid of? How can the data address these concerns?

3. The best data authors are willing to make tough distinctions between data that is interesting and data that is important and actionable. A connoisseur of data, they understand the audience’s limited attention span, and makes sure the data supports a concise point.

4. Data authors must be comfortable working with data. Having a deep understanding of the data and what it has to offer is critical in conveying its message to the audience. You can’t paint a beautiful picture if you don’t like working with paint!

5. Data authors have perspective on how the data product fits into the way people work. They ask the right questions. How is the data product incorporated into the audience’s workflow?  How does information travel through the organization? What do people care about and what do they ignore?

Now that you know the key qualities, how does your resume compare? Sharpen and utilize these skills altogether to help produce delicious data products.

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

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 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. Fruition [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.


Help your audience spread the word

One of the biggest things we’ve learned in recent years when it comes to presenting information is the importance of the secondary audience, your audience’s audience.  The information shared, (e.g. report, dashboard, application), if valuable, rarely stops with the initial or primary audience.  It isn’t just “liked”, but shared and "re"-presented to peers, bosses, etc.  In many cases the secondary audiences are more significant than the primary ones.  In fact, often it is with these secondary audiences and conversations where decisions are made and the data turns into actions.

If these secondary audiences are so important, what can be done to minimize the risk of the telephone game effect (see image above) and help these secondary audiences understand and value what has been shared with them?

Garr Reynolds in presentationzen, a favorite book of ours, offers some great guidance on presentations that is very relevant to sharing data as well.  He says on Page 67 relating to not sharing slides with your audience, but providing handouts, “Since you aren’t there to supply the verbal content and answer questions,  you must write in a way that provides as much depth as your live presentation”.

Here are a few tips and an image below to help you be successful with secondary audiences, give them the required depth and solve for not being there. 

  • Offer Context - Use descriptions and provide access to the details. 
  • Anticipate Questions - Describe what questions you’re able to answer.
  • Explain Usage - Provide guidance and instructions on how to explore the data.
  • Share as Modules -  Give bite sized pieces of information that are easy to digest.

Our epiphany on the importance of these secondary audiences is driving much of the new functionality in Fruition.  Click here to schedule a 30 minute demonstration to see how we help you communicate with secondary audiences.