Guide Users to Understanding With Common Structures

When was the last time you vacationed in a new city? Remember that feeling of being lost until you had a couple landmarks under your belt or had to pull out your smartphone for guidance? Well it’s also important to provide that same guidance for users of your data products so they aren’t lost when trying to utilize it. For successful navigation, use structure to guide users on a path through your data product.

Laying out information is often undervalued, so we end up seeing a lot of visuals that are haphazardly placed on the screen. Sure, all the information is there for you - but can you understand it? Not every user will be an expert in the data, so that’s where guidance is important. Can people understand how the data points relate? If the way the data is presented doesn’t help the user’s understanding move forward, then the product has failed.

When deciding how to structure your information, consider the general structure of the underlying data. Related items should be near each other, there should be a clear entry point to reading the information, and important items should be more prominent. All these things can help move someone through the information and affect the way they approach the business problem.

Here are 3 Common Structures Used in Data Products:

The first structure is flow. This emphasizes your business’s sequence of events or actions across time. Generally, a flow structure will be based on an underlying process with a beginning and an end. Think about that vacation, you decide what new cities to visit, dates to stay at each location and create an itinerary or flow. All this data informs where your vacation will take you and when.

Relationships are another common structure for data. With information design, you  can emphasize relationships by using connective lines and descriptive labels so the user can understand how things are connected.  A common illustration of relationships are found in things like the metro or subway maps that you rely on as a traveler to get around the city.

Finally, grouping as a last resort for structure. Grouping data categorizes information and creates hierarchy. By grouping similar things you can help bring order and logic to otherwise haphazard information. While traveling, figuring out where to get your next meal may help you understand grouping. You can use your smartphone to check out venues by categories like type of food, ethnicity, neighborhood, price or reviewer ratings.

By keeping these structures in mind, you ensure that your users are guided down a path. This leads to better understanding and ultimately, action.

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.


Metrics that really mater - 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.

Goals and Metrics, like Peanut Butter and Chocolate

Goals are defined by metrics. Metrics are given meaning through goals. They go together like peanut butter and chocolate.

That’s why we decided to integrate goals into our customer reporting and data visualization platform, Fruition. To help people make better use of data, we needed to think about how people set, track, and update goals. In the process, we tried to answer the why, who, what, how, when, and where of using data-driven goals.

Why do we set goals?

Goals help focus an organization and connect everyday activities to a larger purpose. Goals are a communication mechanism to emphasize what’s important. They define the gap between the way things are now and where the organization wants to be. Goals establish priorities.  

Who sets the goals?

Commonly, goals are set by the highest level of management and passed down to the rest of the organization. However, its not unusual for management to be disconnected from the constraints and realities of the metric and goal. The best people to set goals are those who know the context, can be accountable, and have access to the resources to impact change. It is also important that there is transparency in who set the goals and why it was defined as it was.

"The early models (some still common today) focus on management. Goals are established by top executives and then communicated down into the organization. Therefore, goals are not always meaningful to individual contributors and employees doing the actual job." - Goal Science Best Practices, BetterWorks

What is a well-defined goal?

A common framework for setting goals is the SMART criteria. Goals should be: Specific, Measurable, Achievable, Relevant, Time-bound.

How should we set goals?

A metric-defined goal needs to find a balance between realistic improvements and previously-unattainable ambition. Finding this range requires analysis. Consider industry averages, historical performance, and expert opinions. Top performers can lend guidance as to what’s possible but extreme outliers will set an unrealistic bar.

When should goals be evaluated and re-evaluated?

Choosing when to update your goals depends on the pace of your business and how quickly you can track progress. You want to update goals as conditions change and as you bring in more information to know whether you’re expecting too much or too little. At the same time, goals without some sense of permanence are easy to ignore. Practically speaking, many organizations are evolving from annual goals to quarterly reviews to ensure better responsiveness to changes.

Where should goals show up?

Our Fruition platform enables the kind of interactive reporting that people can actually understand, use, and act on. To make data more useful, we knew it was important to allow our customers to build goal-setting right into their apps. We paired our visualizations for showing key metrics with an expandable drawer to let users set their own goals.

Here’s what we did to implement a feature that would make goals part of everyday data usage:

  1. Enable permissions to allow specific user types to have the ability to set goals. The rest of the users see the goals but can’t make changes.
  2. Goals are paired with information about the key metrics (e.g. comparison to industry average, trends) to guide SMART goal-making.
  3. The user-defined goals become an integral part of the report — not only are key metrics compared to the goals, but other results throughout the interactive report are keyed off the goal.
  4. Enable collaborative discussions about the goals right inside the Fruition application.

To get a better glimpse at Fruition and the goal setting features, schedule a demonstration via this link.

When good enough reporting is OK

OK is a bad word in our house.  Its like “fine” or “satisfactory”.   Nothing troubles me more  than hearing my wife say “its fine”.  OK or good enough always means there’s room for improvement.  

Good enough is OK in reporting and dashboards when you and your audience know:

  • there is an improvement plan to move beyond good enough
  • you’re testing the waters and actively engaged in getting feedback
  • there’s a bunch of iterations planned
  • you’re available for Q&A

More specifically for reporting or data presentations good enough usually applies when:

  • there are new metrics or measurement is evolving; e.g corporate sustainability metrics
  • its a 1.0 report with a 2.0 planned and funded in the near future

The worst aspect of good enough is that it rarely triggers the desired response. Think about the last time you saw a restaurant health certificate that was a “B”.  It's good enough to still be in business and selling food, but what was your response to seeing it?  Do you think it was the response the restaurant wanted?

When displaying data either in a report or presentation you should consider if “OK” is a desired response.  What if after sharing a presentation that you put hours of effort into, your audience’s reaction was, “It was OK”? How would that feel? Consider the last mile of your efforts to ensure they're received as more than good enough.

When is good enough NOT OK?

Here are a few situations when sharing data where good enough is never OK:

  • customer annual or quarterly performance reviews
  • supplier/franchise performance reporting
  • launching a new product or report offering

In these cases you have a limited window for success.  There aren’t chances for a do over with your audience. You want the intended emotional response and not the indifference associated with good enough.

How do you avoid OK?

You avoid OK by being tuned into your audience.   See Cole’s recent post on audience for some tips. The better you “get” your audience the more likely you’ll exceed good enough.  For some specific tips on audiences also check out the Audience-centric design principles section listed here about midway down the page.   

Let us know if we've missed some instances of where good enough is OK.  We'd love to hear from you.

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 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.