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 “it's 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.

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 Juicebox.  Click here to schedule a 30 minute demonstration to see how we help you communicate with secondary audiences.

9 Reasons We Resist Making Data-Driven Decisions

If the goal is more informed decisions, better tools to analyze and present data just scratch the surface of the solution. There are many cultural and personal reasons why people struggle to rely on data to improve their work. Here are nine common barriers to data-driven decisions -- as illustrated by my 6 year-old daughter:

1. Head in the Sand

The truth can be painful, especially if knowing that truth means letting go of long-held assumptions. Analyzing data holds the risk of revealing new insights that are contrary to someone’s experience about how the world works. One symptom of this type of data resistance is described in a Harvard Business Review article about big data and management:

"Too often, we saw executives who spiced up their reports with lots of data that supported decisions they had already made using the traditional HiPPO approach. Only afterward were underlings dispatched to find the numbers that would justify the decision."

2. Aversion to Math

"Twelve years of compulsory education in mathematics leaves us with a populace that is proud to announce they cannot balance their checkbook, when they would never share that they were illiterate. What we are doing—and the way we are doing it—results in an enormous sector of the population that hates mathematics. The current system disenfranchises so many students." -- Teaching Math to People Who Think They Hate It (The Atlantic)

This subsegment of our society is immediately resistant when presented with numbers. Their reaction may have very little to do with the message and everything to do with the medium.

3. Analysis Paralysis

Some people may embrace data-based decisions…a little too much. Because data is often incomplete or insufficient to draw firm conclusions, it can be easy to keep searching and analyzing in hopes of more clarity. 

When is good enough good enough? RJMetrics suggests that “data driven thinkers avoid analysis paralysis by sorting out when it’s worth taking action now, and when it’s better to pause and collect more data.” 

4. Fear

If the decisions are based on data, why am I necessary?

The fear of displacement can animate some people who resist using data. In their mind, they were hired for their experience, expertise, and gut instinct. These people may not appreciate the important synthesis of data and business understanding that is required to make analytics useful. 

In an American Banker entitled Bank CEOs Fear the Data-Driven Decision, an experienced banker explains: “…most bankers got where they were using their ability to 'read' the situation, a relationship, a deal or a market opportunity based on their gut and their personal skills and experience."

5. Uncertainty and Doubt

Inexperienced users of data will often question their own ability to understand what the data means. They wonder if their interpretation is right and how exactly to read data visualizations.

Sometimes these questions are turned outwards. Can I trust what this data is telling me? Do I feel comfortable with the sources of the data? Or most cynically, do I trust the motivations of the person who provided the data?

6. Preference for Stories

Narratives are easily digestible. The lessons are often clear, as are the heros and villians. Audiences love them. In an effort to commandeer a bit of stories’ attraction, the data analytics industry has focused on the concept of data storytelling. Even so, for many executives, telling a story unencumbered by the facts is a more compelling approach than being tied to the data.

7. Unable to Connect the Dots

Data decision-makers need to make the link between the data they see and the actions they can take. Sometimes this is an organizational problem: the data insights are being generated in a data science team while the people at the front-lines are some distance away. Another disconnect may be between the presention of data and the audience’s ability to absorb the message.

8. Impatience

Relying on data can mean taking the time to find the right data, test hypotheses, and evaluate results. In our fast-moving world, who’s got the time to do the analysis before making every decision?

In response to a Quora question 'What are executives' biggest unanswered questions about data in decision making?’, one respondent noted: "Someone once told me they'd rather rely on heuristics because data analysis is laborious, time consuming, expensive, noisy."

Clearly, data doesn’t need to drive every decision, and making smarter decisions will always save time and resources in the long run.

9. Lost in the Weeds

Pablo Picasso said “Computers are useless. They can only give you answers.”

It isn’t hard to find yourself surrounded by numbers from reports and dashboards, and in the process lose a sense of what it all means. The numbers often don’t help you understand what are the right questions, and what you should do with the answers. People can become fixated on the details and lose the ability pull themselves up to a level to appreciate the implications of those details. 

These nine problems -- and many more that you may have seen -- are more emotion than technical and depend more on mind-set than skill-set. Overcoming them requires executive leadership, clarity of message in data communications, explicitly linking data to actions, and a collaborative, pro-data environment. These are a few of the topics we explore in our book Data Fluency.

Three-and-a-half lessons learned from network diagrams

Once a month here in Atlanta, we invite a few folks from the data community together to discuss the "data value chain" and sharpen each other's thinking in the area of using data better. In a recent gathering we were discussing the merits and challenges of network diagrams. The stake I firmly planted in the middle of the table was this: for the vast majority of problems that folks have to deal with, network diagrams don’t help. Ever.

Ok, so maybe that was a little harsh. And as we discussed it, I had to soften my position. We concluded that there are most definitely situations where network diagrams can be successfully used. Here’s what we uncovered.

When most people think about network diagrams, this is what first pops into their heads:

It’s great for showing the hierarchy that would otherwise only be represented in some sort of over-bloated frankenstein of a table. And I think it works pretty well for a situation with a finite number of nodes that represent physical elements that can readily be counted such as “number of routers.” This is our first lesson:

Lesson Learned #1: If you can reasonably count the nodes, a network diagram can reasonably add clarity about relationships.

So, the concept of a network diagram feels like it makes tiered data more accessible. But let’s look at more complex relationships. Take your LinkedIn network. There are lots of layers of relationships that it seems a network diagram would seem to make sense of. In case you missed it, a couple of years ago, LinkedIn Labs made network maps available to LinkedIn members in their InMaps. Here’s mine:

It is beautiful. They’re using a Gephi-inspired in-house development to lay out the nodes, chose the colors and stuff (if you’re interested in more on this topic, check out the Quora post - oh yeah, that guy Sal Uryasev who worked on creating inMaps is a former Juicer. Nicely done, Sal!).

I love, love, love the groupings. In my opinion, this is the most useful part of the layout. At this number of nodes, it’s not the individuals that are meaningful, but rather how those nodes group together. The approach Sal et al. used nicely summarizes a good portion of my career in about 5 large chunks such as “7 years on the roller coaster” at a dot com, and “Todo: attend a reunion” for connections I made while at Georgia Tech (the labels are mine - wouldn’t that be cool if InMaps could do that?).

But, as far as network diagramming goes, you’ll see that they’re just plotting the first-generation relationships (of which I have 500-ish) and it’s still fairly dense. Imagine what would happen if second-generation+ relationships were added (there are supposedly 11 million “in my network”). Yuck. So here is our next lesson:

Lesson Learned #2: Network diagrams with many nodes are most useful when showing aggregated groupings and relationships.

And the corollary this quickly brings us to:

Lesson Learned #2.5: When many nodes are aggregated into a few relationships, network diagrams can be used as a presentation medium. Otherwise, stick to exploration.

Ok, we have time for one more lesson. Here’s another example offered by a small company you might have heard of:

If you think about it, this is nearly the perfect problem for a network diagram to solve: making it easy for a person to find images similar to the one they’re looking at. But, this offering, inspite of it’s well crafted-ness, went nowhere.

Why? Well, one reason might be because those of us who are visual pundits would love to see these complex relationships simplified by just the right visual representation. But the fact remains that for the vast majority of people out there, advanced visualizations are just not enticing enough -- and too complex feeling -- to incite broad use. There, I said it. So, finally:

Lesson Learned #3: Even for relationships that “normal people” can easily understand, network diagrams aren’t easily traversable by “normal people.”

So, there you have it. Three-and-a-half lessons we’ve learned with network diagrams. Apply them to your next network display challenge and see how they work for you. If you need some technology to help you, check out the wikipedia article on network diagramming tools. Let us know if you find any that reveal other lessons to you.

Guest Post: "All Data is Local"

We are excited to offer you this “Guest Post” by Sam Zamarripa of The Essential Economy Council. In this thoughtful post, Sam reminds us that data is everywhere - including politics. He also reminds us through a real-life example where our focus should be before we start to unload all of the knowledge, information, and data we possess.

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If you’ve paid close attention to the politics of the last 15 or 20 years, you may have heard the expression, “all politics is local”. This expression was originally coined by Tip O'Neill, former Speaker of the House in the U.S. Congress. This phrase refers to the specific kitchen table topics that are most relevant in each district. It is about addressing what each person in the district truly cares about instead of harping about big, global, and intangible ideas. This phrase is so pertinent at the Essential Economy Council, we are now starting to say that “all data is local” too!

At The Essential Economy, we realized that all of our printed materials and discussions needed to be grounded very solidly to the local district and their specific areas of responsibility — to their local politics. Sure, an overall average or total might be considered an interesting factoid, but we’ve proven that they’re much more engaged when our content is specific to them, or better yet, to their constituents, resulting in a much higher likelihood that they will take action.

“The Essential Economy” in its simplest form refers to that portion of our economy that includes restaurant kitchen staff, janitors, landscape crews, farm workers, nursing aides, stock clerks and other non-managerial positions. The cluster spans six major economic sectors from agriculture and construction to hospitality and personal care. Workers in The Essential Economy have traditionally been described as low wage and unskilled, but without whom, core and necessary components of our economy would collapse (anyone out there like to have their trash collected on a regular basis?). In Georgia, one in four workers belong to this part of our economy.

In 2012, we were asked by these industry leaders to understand the impact their workforce had on the overall economy. As a result of these initial discussions, the Essential Economy Council was created. With the help of Alfie Meeks, PhD Economist, of Georgia Tech. We compiled data from the Georgia Dept of Labor on 86 job classifications.

Essential Economy jobs in Georgia

Summary of key findings:

• 12% of Geogia’s GDP
• Generates $114M in sales taxes
• 25% of all jobs in Georgia
• Average wage: $21,718
• Consistently present in all Georgia counties, from wealthiest (Fulton, 22% of workers) to poorest (Quitman, 24%)

This overall data is great to have in our hip pocket and it continually surprises folks. However, when we presented this same state-wide data to Georgia Speaker of the House David Ralston his first response was “so what?” That was the response we needed to hear. When we modified our approach and proceeded to show him the data for the counties in his district, his reaction changed. He immediately asked pertinent follow-up questions such as "How have the Gilmer County numbers changed over the years?" and "How does Gilmer compare to the counties around it?"

Now that we understand this, we offer a more customized approach to each audience we address. We have developed anecdotes about the data. For example, “did you know that Forsyth County has over 1,400 cashier positions”. We are now able to share this information with policymakers, industry and economic development leaders all over Georgia. They seem to appreciate the fact that we realize “all data is local”. We’ve learned it’s OK not to do a full data dump during every meeting or presentation; not only “OK”, but “better.” To accomplish this, we’ve worked with Juice to build several interactive tools to help us communicate our findings in targeted and contextually relevant ways.

Interactive Georgia County Map

As we consider future datasets, growing the Essential Economy beyond Georgia, and contributing more to the national discussions on immigration reform, we continue to believe strongly in the idea that all data is local. As you consider sharing information with your audience and you are looking for more action than "so what", "that's interesting" or "thanks for sharing" responses, think of this post and remember to "localize" the data for your target.

The Essential Economy Council is a bipartisan, nonprofit 501(c)(3) organization that originates research and communications that are used to educate elected officials and business leaders on the value of Georgia’s Essential Economy. The Council is managed by a board of industry specialists and professionals, and it partners with leading businesses, economic development organizations and academic institutions to design and execute its research and communications. If you'd like to know more about the Essential Economy and the work we're doing you can visit our website or follow us on twitter @EssentialEcon.

Google Reader: Looking for options?

Dang! When Google announced in March that they were going to sunset Google Reader, we wanted to believe that if we ignored the announcement, it wouldn't happen. But alas, our strategy didn't work. In case you haven't been paying attention, Google Reader won't be available after July 1st... this coming Monday. So, if you're a Reader user, the blogs you follow (like Juice's) won't be brought to you with the reliable regularity you've come to know and love since 2005 (yep, we started our blog right about the same time Reader made its debut).

But never fear! Here is a link to a list of Google Reader alternatives that will let you import your Google selections directly into a new reader.  But (and here's the part where you need to pay attention), you've got to sign up with one of them before June 30th to ensure this happens.

Afraid of commitment? Too soon to start a new relationship with a new reader you just picked up on the internet? Or maybe you're not sure because you have almost a whole week left. Well, if any of those describe you, you can follow us on Twitter, LinkedIn or Google+.  We post links to our updates in those places as well.

Here's to many more years of happy reading!

Think Like a Designer

I’m a big fan of the work they’re doing over at Duarte Design. Great, practical, motivating presentation design practices. Rarely do I come away from their site un-inspired about something.

Recently, Nancy Duarte participated in an interview with Jimmy Guterman of the MIT Sloan Management Review, which resulted in the article "How to Become a Better Manager By Thinking Like a Designer" (sign up is required). The quote that summarizes the article is:

“Often managers… rely heavily on data and information to tell the story and miss the opportunity to create context and meaning…leaving lots of room for interpretation, which can spawn multiple cycles and limit advancement."

It’s the same with information presentation. A focus on design at the beginning expands the ability to deliver context and meaning. But before you discount design as a concept for well, you know, "those artsy types", keep in mind, as Nancy puts it:

"Design is... crafting communications to answer audience needs in the most effective way."

What this means is that the more you focus on design, the more you’ll "speak" to your audience - which means you’ll be more effective with your data presentation. It’s about the audience, not you.

Here are some dashboard design principles that we use (with a few enhancements from Nancy’s interview) to make sure we become better information presenters by thinking like designers:

• Unity/Harmony - a sense that everything in the application belongs together, resulting in a "whole" that is greater than the sum of the parts. All the elements complement, augment, and enhance, as opposed to distract and detract from each other.

  Takeaway: Identify the problem you’re solving and make sure every element you place moves you closer to answering that question.

• Proximity/Hierarchy - Things that are near each other are related. Hierarchy demonstrates relationships between items where appropriate. Proximity and Hierarchy both provide tremendous contextual cues leading to better understanding.

  Takeaway: Place related things near each other and separate unrelated things. Remember, dogs and cats don’t play well together.

• Clear Space - White space in information display is very important and too often overlooked. Maximizing dashboard real estate means creating places for the eye to "rest" so that the non-white space is more effective.

  Takeaway: Use white space in conjunction with proximity to help your viewers follow the story the information is telling.

• Balance - Dominant focal points either give the viewer a sense of comfort (balanced) or spur them to action (unbalanced). Nancy points out "that does not mean all things must be in balance all the time. It is often effective to jar people and thereby effect a change in behavior or thought. Be aware, though, that once something has been thrown out of balance, it is the nature of the universe to find a new state of equilibrium."

  Takeaway: Make sure the primary focal points in your information presentation tell the viewer either "it’s ok, move on" or "you need to do something."

• Contrast - Contrast creates interest to focus attention or highlight differences. Again quoting from the article "The value of contrast lies neither in the black nor the white, but in the tension between them."

  Takeaway: Use Contrast to shift Balance so the viewer focusses and acts more quickly.

• Proportion - More important elements deserve more real estate. It’s tempting to want to present an unbiased view of the data. However, as Amanda Cox of the NYT graphics department stated at the OEDC "Seminar on Innovative Approaches to Turn Statistics into Knowledge" "data isn’t like your kids, you don’t have to pretend to love them equally."

  Takeaway: Increase the size and emphasis of the values and decrease the size of labels and you’ll find dramatically better impact and speed of understanding.

• Simplicity - Stay focused on the specific fact on which you’re trying to shine light. This sometimes means showing less data and a simpler display. I think Garr Reynolds sums it up best: "Don’t confuse ’simplicity’, which is hard to achieve, with ’simplistic’, which is easy and usually lacking value."

  Takeaway: Help your viewers focus on what’s really important by pointing them to the kernels and not the chaff.