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 [] 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.

4 Components of the Data Fluency Framework

Data alone isn’t valuable—it’s costly. Gathering, storing, and managing data all costs money. Data becomes valuable when you start to get insights from it and apply those insights to actions. But how do you empower your organization to do that?

You might be thinking that the answer is that you need another dashboard, or perhaps better visualizations. Sure those things can helpful, but without a data fluent culture within your organization, you’ll still be scratching your heads and wondering why it isn’t working. There are so many benefits of having a data fluent culture, but what path or framework do you need to actually get there?

The key to the data fluency is actually a web of connected elements. It requires (1) people who speak the language of data, (2) skilled producers of data products, (3) an organizational culture with the conditions to support data discussions, (4) and the systems, tools and ecosystem to create and share data products. The Data Fluency framework explains the roles of individuals, the organization and the systems necessary to achieve it.

Individual - The most fundamental element of your Data Fluency Framework is the individual or data consumer. The organization is made up of numerous individuals and enabling these individuals to understand and draw deeper meaning from data is the fundamental condition for a data fluent organization. It takes more than a solitary listener to give meaning to data. When individuals are informed, they can participate in comprehensive dialogue around that data.

Data Producer - Your organization’s data producers must work with your raw data and deliver the content in ways that are easy to absorb.  Each individual comes to the information with different priorities, needs, and perspectives. As a producer of data, your successful translation of data builds on this variety as an asset - everyone in the discussion adds to the overall understanding of the group and finds their own insights.

Data Product Ecosystem - To enable the flow of information and the creation and sharing of data products, you need standards, tools, and process in place. A good example is what Apple did with the App Store in creating a platform and standards by which apps are created, tested, distributed, and reviewed. Your data product ecosystem is no different, you must come up with those same standards, tools and processes to facilitate the data environment:

Standards are the design patterns and style guidelines that make it easier for data producers to effectively communicate with the data.

Tools enable you need to design and build data products and ensure they are discoverable for your the target audience.

Processes encourage the sharing of insights and collaboration between producers and audiences, as well as ensure data hygiene and quality throughout.

Data Fluent Culture - As your company develops more data consumers and producers, the data fluent culture will develop and  flourish.  Your company will develop your own unique dialect of data fluency through defining key terms, data collection and interpretation. This leads your company to actions based on results and goals —and that is a culture everyone wants to have!

Pick up our book, Data Fluency, to learn more about how to create a data-fluent culture within your organization and contact us to get a demo of our product, Fruition, and start turning your data into dollars.

Get a free excerpt from the book!

Enter the following 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.


Give them cake - and a great user experience

My favorite marketing story from a class in college was about cake mixes. It always stuck with me because of the way consumers drove the development of the product. For a product that is so prevalent these days, I was amazed to hear that when it first came out, it was kind of a flop. Why? The end user.

When first created, cake mixes were made for convenience - making it easy to have a cake at home. In trying to make it as painless as possible, cake mixes initially touted that you only had to add water. That’s it! Could it be any more convenient? And yet, sales didn’t take off like expected. 

It was only when market research was done that they discovered that there was a psychological element to cake mixes that had been overlooked. Women wanted to feel more a part of the cake-making process. As a result, they changed box mixes to add water and eggs. Sales went up and now cake mixes are widely used and loved by all.

Of course, I’m simplifying the story a bit, but the point is this - the end user. The consumer. You have to know who they are and what they want in order to meet that need and for your product to be successful. Whether it be a cake mix for consumption of cake, or a data product for consumption of information. If it doesn’t meet their need, it won’t be used. 

One of the most important things we can do when designing is to consider the end user. It’s all about them. Even in this era of Big Data, the data is just the beginning. The ultimate goal is to be able to do something with that data, likely using some kind of data application. The person using that data to make decisions, take action and be productive needs to be able to glean valuable insight from it. If it doesn’t enable them to do that, they won’t use it and the benefit of all that data will be lost.

When using our product, Fruition, to build data applications, we always start a design by talking about the end user and what their goal is. Then we work backwards to figure out how to create a design that helps them achieve that.

Here are some things to keep in mind about the end user when working through your own design:

1. Reduce new features and improve current ones to improve experience.

"You have to deeply understand the essence of a product in order to be able to get rid of the parts that are not essential." - Jonathan Ive, Senior Vice President of Design at Apple Inc.

2. It’s not about you. Consider your user’s needs. How can you help them complete tasks in the most intuitive way?

3. Workflow integration. How can your design fit into it?

4. Offer guidance and instruction for your users where needed. 

5. Consider a user’s comfort level with data and visuals and design something appropriate. 

6. Make it conversational

7. Keep it simple.

8. There’s a difference between interesting and useful. Lead the user to take action.

In addition to those things, have conversations with clients and figure out what they actually need to accomplish. You know the saying, "If you build it, they will come"? Well, they lied. If you build it with the ability to help them solve a problem, they will come.

4 Benefits of a Data-Fluent Culture

Businesses are aggregators of data, yours being no exception. And with technology advancing at exponential speed, we’ve been able to collect massive amounts of data—some would argue that we’re drowning in it. Much to the dismay of your college statistics professor, most data is underutilized and  rarely makes it off the spreadsheet. And so, we must ask, does your data matter without someone to read, understand and use it? 

One of the ways you can compete today is to learn how to transform that wealth of information into valuable actions. We don’t mean that it falls solely on you or even that small team of number-crunching analysts in the basement. We are doing away with the antiquated model where data nerds are the gatekeepers of all that big data and information flow. In essence, the entire company needs to become fluent in the language of data. Everyone from the people curating the data to the executives who are making decisions needs to know and understand the metrics being measured, how to talk about them, and how they impact your business.

Here are four benefits of creating a data-fluent organizational culture and how it will impact your business:

1. Make Informed Decisions

Your company’s clear understanding of data and the ability to transform it into meaningful analysis will move your organization away from historical biases, decision by anecdote, pure gut instinct, and urban legends. Data fluency creates a more systematic and objective approach across the board so that everyone can make smarter decisions and react with better judgement. “Because I said so” no longer flies far these days.

2. Save Time and Get to the Point

Staring at 80-page slide decks full of charts and graphs rarely inspires productive discussion. More time and energy is focused on deciphering the busy graphs and tables than having meaningful discussions. One of the barriers to progress lies in miscommunication and misinterpretation of data itself. But, in a data-fluent culture, it’s easier for everyone to understand and discuss the key takeaways—getting right to the point.

3. Be Transparent and Accountable

It’s tough to measure progress and responsibility when data isn’t available or is confusing. Clear visualization and communication about key metrics give everyone an understanding of priorities and how their actions influence those priorities. In data-fluent cultures, it’s easier for team members to hold themselves and each other accountable.

4. Cultivate a Learning Culture

Discussions about data often raise more questions than they answer. In an organization where data communication works effectively, new and better questions are asked. This inspires creative friction and changes the conversation from explaining to questioning. These questions lead to deeper knowledge, more insights, and, hopefully, change.

When understanding and speaking data becomes part of your company’s culture, the dialogue changes from gut instincts and misinterpretations to bigger thinking and informed decisions.

Pick up our book, Data Fluency, to learn more about how to create a data-fluent culture within your organization and start turning your data into dollars.


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.