Data Storytelling: The Last Mile (A Curration)

1. 8/27/2017 Data Storytelling: The Last Mile A Curation Brittne Kakulla, PhD AARP Research 1
2. Big Takeaways ✤ The Four Components of Data Storytelling INTERACTIVE SESSION: Tell a story with data ✤ Data Visualization Best Practices INTERACTIVE SESSION: Tell an insightful story with data 2
3. Quiz Who is your home internet service provider? By Yummifruitbat - Own work, CC BY-SA 2.5, https://commons.wikimedia.org/w/index.php?curid=1688551 Who installed the cables in your community in order to provide your home internet? 3
4. ✤ What is Data Storytelling? 4
5. Data Story Flow Visual Insight Data Storytelling 5
6. Data Storytelling: Data 6
7. “The ability to take data-to be able to understand it, process it, extract value from it, to visualize it, and to communicate it- that’s going to be a hugely important skill.” –Hal Varian, Chief Economist at Google (2009) 7
8. Data Storytelling: Data 8 Source: https://www.slideshare.net/GarethKnight/data-management-for-librarians-an-introduction
9. Data Storytelling: Data 9
10. Data Storytelling: Story Flow 10
11. 11 Data Storytelling: Story Flow https://mariahkkaplan.wordpress.com/2015/09/22/my-ranking-of-cinderella-movies/
12. 12 Data Storytelling: Story Flow The Shape of Stories • A good story grabs your attention, incites emotion, is memorable, and teaches, entertains, or persuades Source: http://www.mayaeilam.com/2012/01/01/the-shapes-of-stories-a-kurt-vonnegut-infographic/ Link: https://www.youtube.com/watch?v=oP3c1h8v2ZQ&rel=0
13. 13 • Your story should follow a sequence of events and have a start (context), a middle (what could be), and an end (call to action) • The story is for your audience, so communicate for them, not for you • Use data (facts, observations, and experiences) to support the story Data Storytelling: Story Flow Source: https://www.thinglink.com/scene/491691498954293249
14. Identifying a start (context) for your story ✤ What background information is relevant or essential? ✤ Who is the audience? What do I know about them? ✤ What biases does the audience have that would make them support or resist our message? ✤ What data is available to strengthen our case? Is the audience familiar with this data? ✤ What are the risks or factors here that need to be proactively addressed? ✤ If I only had a single sentence to tell my audience what they need to know, what would I say? 14 PRINT THIS!
15. Here you are communicating your actual data/ content. ✤ Are you comparing your data to something else? ✤ Are there changes in your data over time? ✤ What is your headline or key point you want your audience to know so they can remember it, share it, and use it? ✤ What is the most important metrics or information you need to communicate? This should go first. ✤ What aspects of your data fit the formula “because of this, something happens, until finally something else happens” 15 Identifying a middle (plot twist/ what could be) for your story PRINT THIS!
16. ✤ Challenge a cultural norm- What is expected here? What if it wasn’t true? What can you say to challenge a cultural norm? ✤ Advance a societal debate- think in terms of initiative or big picture not just one issue. What is important? How can you talk about it? How does your information tie into the debate? ✤ Surprise influencers-What would surprise influencers or people who care about your issue? What would make influencers or people who care about the issue interested in what you are saying or want to share what you have created? ✤ Make a recommendation- What are you recommending based on the information you have presented? Are you asking people to make a decision to take an action? ✤ End with an ACTION WORD for your audience such as “establish, change, collaborate, persuade, pursue, understand, support, secure, encourage, form, implement, etc.” 16 Identifying an end (call to action) for your story PRINT THIS!
17. INTERACTIVE ✤ In teams of two, each team take 20 minutes to review your assigned tab. ✤ Create a list of questions about your tab. What do you want to know about caregivers? For example, on Tab1 you many want to know if more caregivers are employed or retired. Are male caregivers older than female caregivers? ✤ Identify an audience for your list of questions. Think about why this is the most important thing they should care about right now? ✤ On Post-Its write one headline per post-it that can answer your questions. Arrange those post-its in a fairy tale story format with a start, middle, and end. 17 Tableau Caregiving Visual https://public.tableau.com/profile/saipriyadarshan.sundaramurthy# !/vizhome/CaregivingTableauProject/Story1
18. EXAMPLE 18 62 is the median age of a male caregiver 61 is the median age of a female caregiver 48% of current caregivers are employed 61% of current caregivers are female 20% of current caregivers make under $30k Most caregivers are married Audience: members of a caregivers support group
19. EXAMPLE 19 Tableau Caregiving Visual Caregivers are important to our society. We will all age and will need care. About half of current caregivers are currently employed full time. The median age of male caregiver is 61 and female is 62. This is peak income- earning age for adults before retirement. Unfortunately, many caregivers have to choose between work and loved one. Some don’t have the luxury of making that choice and limiting work as 2 in 5 HH income is less than $30k. Every caregiver should have the option to balance work and life. Caregivers should proactively work with employers to identify co-work opportunities. https://public.tableau.com/profile/saipriyadarshan.sundaramurthy#!/viz home/CaregivingTableauProject/Story1
20. 20 Data Storytelling: Visual http://www.storytellingwithdata.com/book/
21. 21 Data Storytelling: Visual Why do we visualize? Aid in comprehension Reveal hidden trends Display for presentation Identify opportunities for future analysis Synthesize results Categorize information Summarize information Compress data Helps our audience retain, recall and retell our data-driven stories http://www.storytellingwithdata.com/book/
22. 22 Data Storytelling: Visual Learn how to make all these best practice visuals in the book “Storytelling with Data” by Cole Nussbaumer Knaflic! http://www.storytellingwithdata.com/book/
25. 25 Visualization Best Practices 1. Make the title descriptive 2. Label axes only when necessary 3. Get rid of extra whitespace 4. Get rid of redundancies, clutter, and chart junk 5. Keep the colors distinct 6. Everything has a purpose! Data Storytelling: Visual Make sure you are using the right chart for your data, and the question you are trying to answer Start with a low-tech pen and paper graphic before you graph anything PRINT THIS! http://www.storytellingwithdata.com/book/
26. Bar Chart Best Practices Limit number of stacked categories to five or six. 26 Limit number of bars to 10-12. Too many categories make a chart hard to read. Consider what you want your audience to compare. Bar charts are easy for people to compare relative end points.
27. Line Chart Best Practices Avoid having too many lines 27 Use color and contrast to draw the eye Label your axes with consistent intervals
28. Pie and Donut Chart Best Practices Pie and donut charts are hard to read because the human eye isn’t good at quantitatively comparing angles and areas 28 If you must use, limit your categories to 3 and never use 3D *Cole doesn’t like pie/donut charts but AARP does so keep it simple!
29. 29 Data Storytelling: Insight
30. “The purpose of computing is insight, not numbers.” Richard Wesley Hamming 30
31. Insight is all about audience! ✤ Who is your audience? An internal client, a policy maker, the general public? ✤ What are their needs and why does your audience care about this? ✤ What motivates your audience or will resonate with them? ✤ What is the appropriate mode of communication for your audience? 31 https://commons.wikimedia.org/wiki/File:Michael_Marshall_leads_the_audience_in_a_homeopathic_overdose_at_the_QED_conference_in_Manchester..jpg
32. Use text to write a good headline that grabs the reader’s attention and graphics to enhance communication 32 Data Storytelling: Insight
33. ✤ What information is most valuable to my audience? 33 A B C Data Storytelling: Insight Source: http://annkemery.com/sketching/ One data point visualized nine ways!
34. 34 ✤ Go back to your caregiving story. Create a headline and identify/sketch three (3) ways to graph one data point for your story. ✤ Now change your audience! What aspect of your data story now needs to change too? INTERACTIVE
35. 35 CAREGIVER SUPPORT GROUP Caregivers are important to our society. We will all age and will need care. About half of current caregivers are currently employed full time. The median age of male caregiver is 61 and female is 62. This is peak income- earning age for adults before retirement. Unfortunately, many caregivers have to choose between work and loved one. Some don’t have the luxury of making that choice and limiting work as 2 in 5 HH income is less than $30k. Every caregiver should have the option to balance work and life. Caregivers should proactively work with employers to identify co-work opportunities. EMPLOYEE RESOURCE GROUP About half of current caregivers are currently employed full time. The median age of male caregiver is 61 and female is 62. This is peak income- earning age for adults before retirement. Older workers have consistently been reported as valuable assets to employers due to institutional knowledge, reliability, and high productivity. Twenty-seven percent of caregivers are high income earners (over $75k) When caregivers have to choose between work and loved ones often the workplace suffers. Every caregiver should have the option to balance work and life in order to continue to contribute to their employer. Employers should proactively work with caregivers to identify co-work opportunities. EXAMPLE
36. 36 Questions?

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so here is how we will spend our 2 hours together.
first, we need a working definition of data storytelling so we’ll go over that.
Then we’ll review aspects of data storytelling particularly story flow and practice this hands on in teams of 2.
Visualization is a key component of data storytelling (the data distinction makes this different than just storytelling) so I will quickly review some best practices about visualization with the guidelines that some things are based in science while some are art, which means they are subjective.
Then we will again work in teams to revamp our data story.
Lastly, you will leave with a resource guide I have pulled together of cool books, tools, and things to help you along you way.
So before we get started, I’d like to do a mini poll. How many people know their internet service provider? How many know who laid the cables? Right. so the term “the last mile” in telecoms refers to the final leg of telecoms that connects the end user to the utility- this is the portion that people care about when it comes to their utility- the service provider. The term has started to be applied to lots of different fields. Particularly research because if you think about it, it’s the same concept- There is a lot of work that goes into doing research- laying the ground work and all the grunt work of analysis but what people care about, the utility to the end- user is the insight. What does this research mean to me- how does this connect and impact me? So the first mini tip in our data storytelling curation is to connect users to the last mile first, then provide the rest of the pipeline later or on-demand.
That was a mini-tip but let’s start from the beginning. We have all been hearing the terms data storytelling. Now it’s in the building and in our departments, and coming from our clients. So what is a good working definition of data storytelling?
Well there are lots of ways to put the words together but at it’s essence and across multiple resources- data storytelling is based on 4 components- 1. first data (uh duh). Next a story flow- this is part of what makes DS different from a reporting of the findings a narrative. 3rd component is the incorporation of a visual aid to help engage the audience, engage the audience, provide additional information, and reinforce key points. A text paragraph of findings isn’t a data story. Lastly, data storytelling incorporates an insight. It influences change. The author has considered the audience and their specific needs and interests and provides some actionable information or direction rather than reporting findings and letting audiences cherry pick data points of us to them. So that was the 20k foot view, now let’s dig a little deeper.
What makes data storytelling different that just storytelling is the data. It’s the facts and numbers behind your anecdotes and stories and narratives. Here’s an interesting quote about the importance of data in 2009 that has become quite prophetic.
The first component you need in data storytelling is obviously data. A formal definition from a research library defines data as facts, observations, or experiences on which an argument, theory or test is based. Data can be numerical, descriptive or visual. Data can come from various sources- primary data- you collect it yourself. Or Secondary- you get data someone else already collected. The context of the data can vary as well. It could involve numbers, words, images, sounds, anything we can experience and use to base an idea, theory or argument on.
But there is good news and bad news for us is when it comes to data. Given new technology developments, we are now swimming in data. There is open source data that anyone can download and analyze, companies that are sole data providers that allow you to scrap existing data from websites, there’s gov survey data, personal data about ourselves we can quantify like never before.The economist did a special issue on all the data that is available and data deluge. Another term you may start to hear often is a Data Lake. A data lake is a storage repository that holds a vast amount of raw data. So science is already giving us the visual of a flood of data and information that is so broad it is being stored or captured in a lake.
Now that you have all this data, how you communicate is important. That’s the next component of datat storytelling.
Question- what is your favorite Disney or Pixar story? ASK people.
Why is it memorable? Is it easy enough for a 5 year old to understand? Here’s an image from the live action Cinderella. Who can give me the plot of Cinderella?
I wasn’t too fond of the movie, but we all know the story. Cinderella leads a happy life until her father remarries an evil woman with 2 daughters. When the father dies, the woman makes Cinderella a servant to them. A handsome eligible prince has a ball. She can’t go but a fairy godmother makes her a princes for one night, long enough the win the heart of the prince who uses a glass slipper to find her. When he does, they marry and live happily ever after. BOOM.
Since the discovery of the story in 1899, there have been over 100 movie versions of Cinderella that retell the story, or tailor it to fit new audiences, or generations but the essence of the story never changes. Why?
Whether it’s a Disney story or something on Youtube, a good story grabs your attention, takes you on an emotional journey, teach or persuade people, and is memorable. Giving your data a story flow is a powerful way to leverage something everyone is familiar with and a basic human instinct as a powerful communication tool to garner impact. There are lots of ways to tell a story. Some people might be familiar with Kurt Vonnegut, a famous author. His “rejected” thesis as a graduate student was that all stories have a shape
Again, what makes data storytelling different from simply reporting the findings is data storytelling follows a sequence of events. It’s like a fairy tale story. It has a plot, middle or twist, and an end. A call to action- recap of the problem and the need for action. So the writing assignments in 5th grade are full circle in your life.
If your information presents a “well, duh” then you need to dig deeper and be more specific. TIP- Start with a question.
Ok, so before we tell a data story, we need data. Today we are going to use an interactive on caregiving our Intern has been working on in Tableau. This visual is a rollup of all the caregiving surveys conducted in state research from 2014- 2017 across 36 states. Right now, this is just the data reporting, it’s not a data story. So let’s play.
Caregivers support members might want to know more about people like them. They are struggling with work. They would want to know about half caregivers are employed.
caregivers are important to our society. We will all age. Many caregivers are currently employed full time. The average male caregiver is 61 and female is 62. This is peak income age for income. Climax- many caregivers have to choose between work and loved one. Some have reduced pay. Some don’t have the luxury of limiting work as 2 in 5 HH income is less. Every caregiver should have the option to balance workand life. Creating a schedule or wo
Ok, lets move to the 3rd component of data storytelling- visual.
The 3rd component of data storytelling is visual. Well actually visualized data. Visualized data refers to a graph or visual display of information. There is a lot of science that supports visualization. Seeing something, particularly data helps people understand, compress, synthesis, compare, and categorize quickly.
There are lots of ways to visualize your data. My bible- storytelling with data has streamlined it down to 12 types of visual that are the most common. I love this book. We’re not going to go through each of these types but I highly recommend the book. It provides use cases and best practices for each type of chart as well as how to create them.
Contrast is the arrangement of elements. Important in art. Important to anything visual. How we achieve this is by
How we position things, the colors we use, and any added marks in our visual.
More specifically when we talk about position, we are activating one of several innate Preattentive cues are hardwired in the human brain. They signal where to look. Prehistorically, they helped with survival. Now they do that and help process information. The attributes are orientation, shapek, line length, width, size, curvature, marks, enclosure, color or hue, intensity, spatial position, and motion.

With your data storytelling, it is up to you to draw the readers eye to where you want them to see and focus them on information you want them to have. Again, different from just reporting the findings. Using our story, we have decided what information we want to convey, and we use visual cues to reinforce that choice.
So I’m not going to go through each type. Cole has some video great examples on the blog and in the book. So he’s the 40k foot tip of best practices as your visualize your data. Really could do a workshop just on visual.
So we don’t have time to go over each type of chart. I am going to spend some time on the top charts I see around here and tips for best practices. First Bar charts.
Line graphs imply a connection between the data points . You use to plot continuous data.
A lot of data viz people are online. Pies are evil is a thing many say. It’s a bad graph. Cole doesn’t like them and doesn’t list in her dirty 12 but AARP does. I also like a pie when it is 2 categories, a yes and a no. and you use visual best practice of contrast and color to really highlight the Yes or No in your data.
The 4th and key component of data storytelling is insight. A lot of the work we do it’s academic or data for data sake. It is to influence change. We want people to see the data we have examined and we want them to get something from it and do something with it! That is insight.
In order to get people to get something from our analysis and do something with it we need to know our audience. Insight is about audience. WE had a formal director who loved one word slides. Great when your audience is in person and you want to communicate a mood or feeling, not great for detail oriented people who want to. I once gave a retirement presentation about preparing for retirement that was insightful and I had all the tips. Someone came to me later and said I can’t do any of that..I didn’t know my audience so everything I said, was discarded because it wasn’t what she needed. It’ not insightful to tell a bunch of school teachers that women will outlive men in retirement. Duh. What is insightful is to to tell them why they should care women like themselves will outlive men in retirement and what they should do about it.
When you are working on your data story, ask yourself what is valuable to my audience. If you don’t know, you shouldn’t be trying to influence them until you do because context is everything. Here we see one data set told 9 different ways. The data is the same, what changes is what the author chooses to focus on in the visualization. The way to help decide what to focus on starts with the audience. Ask yourself what info is most valuable to my audience? In row A1 in the red this is the actual data. In a table of the data. It is number of tickets sold for a theater by year and type. This is a reporting of the findings but no insight. What does it mean? What is valuable and what do we want to convey. The information that is most valuable is what you visualize.
A2- Just the trend for online because it’s a new offering.
A3- the trend for all 3 types of tickets. Here we can compare each type across the years. But what if it’s valuable to see each trend individually? B1. B2 adds a reference line to provide context. B3 is a topline summary. Maybe all the audience cares about it is when we started and where we are now.
C1- what if total tix sold is more important than time? Well actually time in important so now we have C2. But maybe they just care about total tickets. So in C3 we see a pie.
For me, if my audience changes, I will change my focus on income as a way for work life balance to productivity.

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