Jennifer >> I'm going to go ahead and get our recording started and welcome our presenter today.

Dr. Kate McDowell is the Associate Professor at the School of Information Sciences at the University of Illinois Urbana-Champaign.

And we are so thrilled to have her here today to talk about data storytelling for libraries.

We're so glad to have you, Kate.

Thank you so much for being here.

>> KATE MCDOWELL: Thank you so much for having me.

And I'll just get started right away.

This work, the image that you're seeing here is an image from our grant project where we found that a lot of people were interested in learning how to tell data stories about libraries.

So we've been building this toolkit that I'll introduce a bit later supporting libraries, using data libraries are already collecting.

So this is the webinar for you if you're part of an organization that is already collecting data.

Many of us are, and we collect our data more than we connect our data to different purposes.

Storytelling is one of those purposes that can really make a difference.

Of course that involves creating good visualizations and strong story structures.

I work as part of a team.

My teammate, Matt Turk, is the astronomy professor who has the big data expertise who is our visualization expert.

I won't be going deep into visualization today, although I do have some ideas about that from my own experience.

But fundamentally, we're talking about data storytelling, advocating for libraries.

How can we tell the story of what libraries really do and really accomplish based on the data that we have so that we can make a difference in the impact that our institutions can make?

Here is how I define storytelling.

It's deceptively simple.

It's the dynamic process through which the story emerges in the exchangeteller and the audience.

It includes your responses, how you are here, what you bring here, the questions you ask, the notes you take for yourself.

That's part of how this story emerges.

You as the audience take the story away with you.

So let's see.

I think I'm going to minimize the chat so that if there are audio issues, I'm not necessarily looking at that right now.

But I'm sorry to hear some folks are having that.

So there's this dynamic process of exchange between the teller and the audience, and the story emerges.

One other piece I'll point out, because this is not as simple as it looks, the audience also has a vantage point on not just the story but also the teller.

And thirdly, the relationship between the teller and their story.

This is perhaps why personal storytelling is so popular and comes to mind when people say "storytelling."

But I'm here to talk about institutional storytelling, and storytelling that we do with our data for library support.

So this is my basic definition of storytelling.

And here's our agenda for today.

The agenda is organized in questions that you could take notes about.

So at each section where we touch on one of these questions, I'll pause and just mention to you again, you've got this lovely graphic from Pixabay to indicate where we are that touch point in the agenda process.

We'll talk about -- I'll ask you to think about what kinds of data do you have as evidence.

I know our field, I know the kinds of data we collect.

 I don't know your library and I don't know what audiences you need to reach.

And what's the best narrative structure for your story?

I hope you'll leave today with some idea for some data story you haven't told yet but might want to tell.

So I know what I know because I'm part of a long history of --

so folks, please let me know if I need to change something as presenter.

I'm seeing some -- I saw an interesting comment in the chat, quite distracting, about their being no audio.

So if I need to change anything, please let me know.

As far as I can tell, we did all our testing.

>> JENNIFER PETERSON: You're all good, Kate.

>> KATE MCDOWELL: I'll close the chat, then.

I know what I know because I'm part of this tradition.

I come to you having been in a children's library, admittedly in what we call the 1900s.

We come from this tradition and need to reclaim this tradition of storytelling in our work.

We have more than 130 years, more than anything else in the information field of any kind, of storytelling.

One person who is a real anchor for this is Augusta Baker, first to hold the position of storytelling specialist at the New York public library, who emphasized the story rather than the storyteller.

She talked about us, the storytellers, as being simply a vehicle for which the beauty and wisdom and humor of the story comes to the listeners.

I'll come back to the amazing woman in the hat in a moment.

My mentor was longtime director of the Center for Children's Books.

I also know about data storytelling because of my consulting work.

I've worked with these organizations and more.

It's an embarrassment of riches.

I do different things for different organizations.

Sometimes I speak with -- last January, for example, with ILNS, the first keynote speech for their group, so I talk with people who are doing the data collecting at the Federal levels and state levels.

Sometimes I work with organizations that are struggling to tell their story like Prairie Rivers Network is a statewide organization in the state of Illinois where I live and I helped them at their 50th anniversary to revitalize some of their stories along with many other people.

Sometimes I'm called to work with, and it's quite daunting, to be honest, PAHO, a Pan American Health Organization.

A team reached out to me to talk about how to deal with the pandemic, information stories around health.

I was very proud to be part of that team and not a little bit scared because when we're down to the former children's librarians, the world is in trouble.

Anyhow, I've been very honored to work with all of these groups and I've learned a lot in this process.

The third way I know what I know, third and last, is by teaching data storytelling.

Back in 2017, along with Dr. 

Matt Turk and other folks listed here and a few folks who are not listed here now because our team has grown to be nine people this summer, we teach data storytelling at the at the University of Illinois.

We teach it to all of our students in all degree programs.

It's not required but it's an option across every degree we teach.

These are courses in ethical and accurate information communication.

It's a difficult world that we live in in terms of data challenges.

So we look at flexibility of expression and honesty as important dimensions.

So that's a little bit about how I know what I know.

And now I would like to know a little bit about what you know.

We have a poll for you.

And there are a few options in this poll.

Your comfort level, I would love to know, tell me what's your highest comfort level, so pick one that you're most comfortable with.

Is it data, is it story, is it both, or is it neither?

I used to just force people into a binary choice of data or story but I don't think binaries work very well for us.

Please go ahead, if you can see that poll, and if you would take a vote, I'm seeing something that says voting closed but hopefully -- 

>> JENNIFER PETERSON: Oh, shoot.

Oh, dear.

Here we go.

So sorry.

It's like, why am I not seeing -- now folks can answer.

>> KATE MCDOWELL: I'm seeing lots of folks responding.

I don't see the responses quite yet.

But I see the participation.

Thank you for doing that.

It helps me to get a little bit of a read on who is here today.

And that's important to me.

I like to understand as much as I can about who is in the virtual room, so to speak.

There's much more we could know about each other.

There should be a Slido, S-L-I-D-O, window that's an option for you.

I see about 212 people have voted, which is a good large percentage of who all is here, which is great.

So why don't we end that and let me see the results.

Fantastic.

Lots of folks familiar with data as your highest comfort level, storytelling next, and some with both, some with neither.

Thank you so much.

That's great.

So let me tell you how I define data storytelling.

Data storytelling for my purposes means any presentation of data using narrative strategies in story form.

And this way of thinking is a way of acknowledging that we can probably make stories out of many, many things that we already have.

But this is a good moment too to pause and say, I would like you to be thinking about what data do you have as evidence of library value.

We have had a long history of gathering evidence of library value.

I promised you I would come back to the fabulous Carolyn Huyens, at a time when women were only 20% of the profession, which was an exclusively white profession, I should say, it was not diverse racially.

White men were 80%, white women were 20% of the profession.

They created the first systematic, empirical evidence-gathering in librarianship, a series of nationally based qualitative survey-based reports.

They developed this research model.

It was adopted by others.

And most importantly, if you look at the time period right after 1898, it became the key research model in the field for quite some time.

So, many years before the development of the social sciences at the University of Chicago, about 50 years before social science took off as a field, as an idea, there were people in our field who were gathering data.

And so I like to say that our roots in data storytelling go very, very deep.

They go back to the beginning of what we do.

And I teach this, and I also learn from my students.

So one of the students, Ashley Hedrick, in the first class we taught on data storytelling in 2017, as far as I know we're the first people to teach this historically.

Ashley pointed out her training in statistics had led her to dehumanize people through the use of the Titanic data set that one could argue normalizes the correlation of poverty and death, because if you had the cheaper seats on the Titanic, on the ship the Titanic, then you were much more likely to die in that ship going down.

And this data set is very commonly used for training people on statistical analysis.

And so we ended up with a lot of questions about what does that do.

Certainly from my perspective, from the library field, what does it do for our field if the ways that we practice learning about data tend to dehumanize people or at minimum, devalue, you know, normalize the loss of life as being correlated with lack of economic resources.

So my students have gone on to do many projects.

I'll show you just a couple of them.

Here's one, Justin Martin, also at the University of Illinois as a staff member, just like Ashley is, they still work here, fabulous master's students, Justin did a comparison in 2020 of what it takes to get online for a family.

This is the kind of comparison you might want to do for your region.

And a key difference that he found with the yellow bars is that if you can't afford enough to pay the first year owning equipment fee, then forevermore, you are locked into a rental fee that makes your long term costs, year two plus, the bottom two bars, that yellow at the bottom, that's your equipment rental fee.

It makes your cost of being online much, much higher.

Again, possibly data you could use in your region to think about the value of library online access for real families and real people.

Finally, I had a student who wished to remain nameless because this data to some degree might be considered controversial.

It calls out the specific incomes, this was from 2021, of different service areas in the Chicago region, in the north Chicago region, and looks at what does this matter for people.

In print materials, you get three times more print materials, e-books, three times more e-books, and two times more programs in a richer library in this region versus a poorer library in this region.

And if these all seem too much like school projects to you, then let me show you one from Emory University that is a classic story.

I'll come back to this again later.

The achievement of serving real needs.

How do we know we have real needs?

Usually we do a survey.

Here is a wonderful news story that came out.

The link will be in the slides, about a user survey that led to new technology investments.

And they took the time to analyze the results from this survey, change their investments in technology, and I really like how they say, we're ready to showcase the new computer workstations, laptops, and other equipment ready for checkout.

So we respond all the time to user surveys with technology improvements.

This isn't something innovative on its own, it may be very important for this library, but the innovation is telling a story about it.

And let me suggest that if you really think about that storytelling triangle in a dynamic relationship between the teller and the audience where you could switch places, then asking people after you've made those improvements how did it go, hey, we did a survey, we changed things, how's that working out for you, that could be a really profoundly important piece of keeping a sense of story-like dynamic exchange going with your audiences.

What I've found in my years of doing this work so far is that library data in general needs story before storage.

I have had the great pleasure and privilege of being on sabbatical this year.

I was affiliated with the Center for Digital Narrative in Norway.

I did a day-long workshop for the public library in Norway.

We talked about this slide a bit because when we looked at their data, they were so kind, they had some data they had collected, they had some new Power BI, they had done some great visualization.

When we looked at their data together, the first thing they said, we don't write the questions.

I thought, that's right, we usually don't write the questions, we don't make the jars, we don't necessarily determine what goes into the jars, but somehow we have to find a way to get beyond collection and into connection anyhow, we have to own the answers that we have, we have to take the data that we've got.

And here's the point of this visual metaphor.

It's great to store things.

We are storage experts.

We are preservation storage collection.

Our strength in that is so massive that we sometimes overstore things.

We keep too much and we forget that the point of data is not just to store it.

The point is to use it.

It's more like a kitchen than like a library.

Our data should be consumed.

It should be out there.

It should be something we bring out the point of food is not to keep it, the point of food is to eat it.

So I'm going to briefly go into a little side detour on the theory of this, because I've found most people are interested when they need a storytelling researcher to understand how do I think about storytelling as information.

I'll tell you a little bit about that and we'll get into very practical stuff right afterwards.

It's just about, let's see, we've got four slides on this.

I base my thinking on the DIKW pyramid.

This pyramid is long-standing.

It goes back to the '80s in computer science with Ackoff who described this concept that data is the basis of how we know things.

We then come to understand information from that data, from information we develop knowledge of what to do to solve problems, and at the top we hope for wisdom, we aim at that.

But this pyramid is not only based on computer science.

This pyramid is also based on poetry.

To me this is the fundamental duality of our field.

I've written in other places but the way storytelling crosses our very own epistemology divides.

This is from T.S. Eliot, where is the wisdom that we have lost in knowledge, where is the knowledge that we have lost in information?

The idea that these things have layers and could be understood was fundamental to an argument that I made in an article in 2021 that storytelling wisdom is possible.

The title was "Storytelling Wisdom."

It's a fundamental information form.

The process of storytelling is central to understanding social, collective, and community meaning-making.

This is why information science and sometimes even we librarians get confused when we see a lot of information floating around, not all of it good information.

But storytelling is key to that in positive and negative ways.

I want us to use it ethically and positively, of course.

But in story, wisdom often means discovery in ways beyond the obvious.

I always tell this story, if you're from Haiti you would probably be able to tell this story better than I can.

A girl's father had died and she lived with her stepmother and one day she overheard her stepmother having a conversation with a man who came to visit the village and the man was asking what it would cost to buy her as a servant.

Her stepmother told him, I'll dress her in red tomorrow and you bring me that money, you can pick her up.

She was shocked but she went to her friends.

When her friends heard what had happened, they all decided to do the same thing.

The next day when the man came to the village, they were all dressed in red and every one of them said I'm her.

Of course he couldn't take everyone.

This is one of those ways beyond the ways that seem obvious that wisdom can be enacted.

So we have a real challenge in our field right now.

And that is -- let's see, that's sort of the pyramid as it works as story, I should say this.

So if the basis of information is our story and data, we need interpretation of context as story.

That's where our responsibility comes in.

We have to interpret our data for people.

We can't just show our spreadsheets.

I know, I would love to show mine too, I have them for all the research I do, but it doesn't work, it doesn't stick with people, they can't retell it, they can't remember it.

We need this data interpretation process.

We also need to build an understanding that leads toed  leads to action able story.

My main goal as a researcher and librarian is to bring wisdom into the conversation so that we must wrestle with that all the way down to our data and up through our interpretation and India what to do, we should be wrestling with wisdom.

Now I was going to say, libraries have some challenges these days.

We know what this is, what this means, what this looks like.

In fact later I'll mention we know that this may in fact be the indicator that have person's academic success but other people don't know about our work and so we really have a storytelling challenge at our doorsteps right now, for all different types of libraries, whether you're funded with an institution, funded directly by taxpayer dollars, whatever kind of political arrangement, overseas, your structure, we're facing some real challenges.

So back in -- I started thinking about this in 2014, the research started in 2014.

In 2020 we really got going with the seed grant and since then we've had a USA nationally funded IMLS grant, Matt and myself, to build a DIY guide to data storytelling for common data uses in libraries.

If you're a big multitasker, feel free to sort of page through this as I go.

But let me quickly explain what this is.

We base it on a lot of research methods.

I'm not going to go through them right here.

Suffice it to say we use a lot of different qualitative ways of trying to assess our people needed to build and tell stories.

We have a goal in mind.

It's that we get beyond this first step, which is hard, of taking data and turning it into a memorable story.

We want to get to the second step, which is in some ways easier, of making sure that our data becomes a story that someone else can retell.

How do you know if someone else can retell your data story?

Well, you need a practice audience.

You need some people who can sit with you and try out your story and learn what you know and find out what you have to say from that story.

How do you know they've gotten it?

How do you know your rehearsal audience has the story?

Have them tell you back what they heard.

We don't have to guess, we can know if our story can travel if we remember that we don't practice storytelling by ourselves.

The great story coach Doug Litman once said we never practice communication alone.

The same here, we must practice with other people in order to know that our work is going in the directions that it needs to go, that it's repeatable.

I want to pause for questions at this point.

We'll have time at the end as well.

The next thing I'm going to go into is the process for toolkit.

I'm going to go into some of the important features for takeaways today for you to start thinking about, if you've got that data in mind, you've got some evidence in mind, you're thinking of things you might want to tell a story about, what are the first steps you would take in order to think about what that data could mean.

>> JENNIFER PETERSON: There's a question asking if this is a series of webinars.

Not at this point, but Kate might be able to talk about some of the other things that are happening with her work.

And then I just wanted to share a lovely piece that someone mentioned, remembering Cora Belpree, the New York public librarian who was told she couldn't tell folk tales and she went on to publish these stories and read them to her neighborhood kids.

>> KATE MCDOWELL: Yes, I'm so glad that came up, a great example of librarians reshaping the world bit by bit.

The link is in the chat, as I understand it.

Let me get into the process here.

I've been doing a series of webinars that started in fall --

let me think, about 2022, when we launched the first webinar for this project.

It's open, free to the public.

I always advertise them.

I'm on LinkedIn, if you want to follow me there, I will advertise the next one.

Should be in July.

I'm not making hard promises right now, because we have a lot of moving parts and pieces for the project.

But yes, I'm doing 15 talks, seven of them key notes, this year.

So yes, I am out there, and I would be more than happy to connect with you as well.

So here is the process for the toolkit user.

We have an introduction with navigation.

There's a little video of me again talking you through it.

And then we have these four parts that emerged as really important to the research.

And I'm going to walk through parts of three of them today.

You have the whole toolkit so you can read it yourself.

But reaching audiences, choosing motivations and goals, and structuring narratives are three of the four pieces that we found to be critical to the process of developing a data story.

I know for sure that your work is going to be very different in each place where you are located.

So you may choose to use part, some, all, none of this toolkit, right?

It's freely available, it's funded by the ILMS.

We're up for a second grant, if we get that in July, we'll be working with the association.

This one is meant for any librarians.

Reach audiences, I always like to start there.

People will tell you you should know your audience.

I always ask, did anybody ever tell you how to know your audience?

We spent some time thinking about how to know your audience, which is I think an important thing.

We can know audiences, we can think about their knowledge, we can think about -- let's see, I'm just trying to close that and see if -- yeah, and so -- 

yeah, I think I am going to wait on questions in the chat right now, just because some of them are really detailed.

You can think about people's knowledge, their demographics, their attitudes.

Knowledge is variable, it depends on how much people know about your data.

Demographics do not tell you that much about knowledge or attitudes but they can tell you things like what languages are spoken in your community, what kinds of collection development might that imply.

Demographics can be helpful for getting a broad picture of how communities have changed over time as well, because sometimes libraries are in a position of really needing to make new investments.

In my community, Urbana-Champaign, I've recently learned that there are about 6,000 immigrants from the Congo who have found a way here through a particular State of Illinois program.

And that's really exciting.

That means our libraries will be changing in interesting ways.

And third, and the one I'm going to just dip into today, is about attitudes.

Attitudes have a lot to do with how our audiences receive the stories that we tell.

So this is the quick guide to audience attitudes that appears at the end of our tutorial.

Half the toolkit is a static tutorial and half the toolkit is dynamic interactive pieces.

In this tutorial we talk about five kinds of attitudes that have emerged through our research, positive, negative, mixed, indifferent, and polarized.

What the first four have in common is they have a shared reality, meaning they are in the conversation in order to figure out how libraries can go forward.

They might disagree about a particular proposal, a particular amount of funding.

But they don't disagree at the level of do they think libraries should exist or not.

Polarized audiences are those that may be so far out in terms of not sharing a reality that they do not think that libraries should exist.

So positive, negative, mixed, and indifferent, the tone and tactics for each audience, we walk through this, this is available in the toolkit, so I'll walk through pieces of this.

For positive audiences, always remember to build trust.

We can't take sympathy for granted.

Make sure they can retell that story.

For negative audiences, we stay calm.

You want to focus on persuasion, thinking about how to reflect back that you understand a negative audience's concern.

Finding compromises is an important piece here.

And slowing down to de-escalate conflict as well.

Mixed audiences, this is people who are both positive and negative.

This is very, very common.

So in that case, if you can persuade some people, what is the story they need to tell about why they were persuaded?

That's an interesting reframe of story to bring to this.

For indifferent audiences, you have to start with why it matters for these specific people, even on groups of elected law making representatives, you will have some people who are indifferent to a particular cause.

So you have to model the interest you hope to instill.

Be interested even when they are not.

Keep it super simple.

Ask questions, wait for their responses.

And wait that painful 30 seconds.

And I'm talking here mostly about in the room where it happens, right?

There are places where political decisions about how much funding libraries will have, there are places where those decisions happen, there are rooms where it happens, to quote the "Hamilton" musical.

We have to think about can we ask questions and give it that uncomfortable 30 seconds to understand from people back what they do or don't care about.

Then we have polarized audiences who are in the game for a different reason.

They do not want decisions to be made.

They want it to seem as though certain proposals are so inflammatory or controversial that they can't be voted on, they want to endlessly defer a vote.

So in that case, the goal is to be nonreactive, stay on message, never repeat false information, not even to refute it.

This is really important.

Think about messaging here.

I've talked with a specialist in human behavior about this as well.

We are tempted, when outraged, to repeat someone else's message and say, we're not those kinds of horrible people you just accused us of being.

Don't say the words.

Don't even say the words.

Say different words.

Stay on message.

Consider the value of debate very strategically.

You do not have to debate someone whose goal is to strip society of the fundamental premise of democracy.

You don't have to debate someone who is doing that.

In fact it's going to be a bad idea, because they will have arguments ready to keep you going down the line.

Stay on your message.

If the intent is to confuse the voters, avoid the debate.

So that's an example of what's in our guide to audiences, that, again, I have to thank the many, many library staff and professionals who have joined us in this research, some of it published in different places that I have for you at the end.

We also have an interactive part of the toolkit that's based on a 12-part matrix.

I won't go through all 12 parts here because it would be very tedious, but on the website you can go to goals and motivations and start clicking through.

You'll see that once you pick one of each of these, then you will be able to see some guidelines specifically for one of these settings.

Let's imagine we're going to pick addressing deficits and justification.

Let's say we've lost something recently, we don't have money for a particular program or service and we need to justify how we'll get that money back or how we'll move forward next through that situation.

Here is a little tiny sample of what that looks like.

I've cut out the data visualization graphics, but I give you a sense of it from just the broad strokes.

So it looks like this.

We have kind of a fill-in-the-blank.

We don't necessarily expect people to use it exactly, adapt it as you will, but this is what we're doing, we're assessing deficits in something in order to justify resources that improve something we're doing.

So for example, and in the second part is always a worked example with this framing, we're assessing our library's deficits in services to rural areas following the recent closure of our mobile services.

We will use what we learn to justify resources that improve access for remote users at existing library building locations.

And this is a story that comes from the UK.

So in the toolkit you'll see there are usually three actual news stories all pulled from real libraries that connect with this.

But the first one always connects with the template and the example, then the first story is that same information.

So this is based on a real story where mobile library services had to be closed because of budget deficits.

Then there is advice on data visualization.

If you had this kind of story to tell, what in general might you think about doing in order to organize that?

And in this case, we've actually refined the guidance.

That's the second part.

The third part is structuring narratives.

I could spend a long time on this.

And I won't right here, because I'm going to walk through step by step some of the pieces of narrative structures.

So you know that narratives have beginnings, middles and ends, that's not news.

However it's also not a great way to construct a narrative because the important thing about a narrative is that it connects information with emotion.

And both those things move through the course of a story.

We learn more but we also gather more information.

So we also gather more investment over time.

We get more connected to what's happening.

So the research has kind of been correlated around.

This is partly me borrowing from narrative theory and part of me looking at the interviews I've done, a hundred interviews since 2014.

It's not exactly causal one way or the other, but they've emerged from this process as best we can describe them.

And there are three of them we'll go through.

There's transformation, continuity, and discovery.

And we'll actually start with continuity in just a minute.

But the idea is that they each have different emotional impact.

So transformation, we're thinking about awe and triumph and the joy of a hero.

Continuity, we're thinking about stability and resilience.

Discovery, we're thinking about mystery and intrigue.

So you can pause here just for a minute, because usually when I say the words continuity, transformation, discovery, people have some intuitive sense of what their story that they're thinking of already might need to be organized around in terms of that narrative structure.

What is it that might benefit your story the most?

Okay.

So here's the chronological structure of narrative, done by a wonderful graphic artist, Hillary Pope, who works for me sometimes.

Chronological structure of narrative is not the logical structure of narrative.

It is just the order in which the story occurs.

In order to build a story, we can't really start from the endpoint of having a whole story.

There are a few people's brains who work like that but it's not most of us.

Most of us actually need to think through, what am I going to put in what order to make this happen?

The place I recommend beginning is with the middle, because the middle is where you have to make sure that you sustain a sense of progress, a sense of overcoming obstacles, or a sense of increased informational interest over time.

So, for example, when libraries were faced with the global pandemic, there was -- and I'll bring this example back in just a little bit -- there was the need to continue to loan things out.

Things started to mean something different when there was a tremendous loss of physical accessibility to books, magazines, even e-book readers that were borrowed.

So the digital parts got more creative in terms of thinking about how can we provide more digitally.

Digital use didn't increase as much as the physical had been, so there was still a gap there.

It was very important that libraries also found physical ways to make things accessible through things like more distance kind of drop-off access points, contactless  borrowing, contactless pickup.

So we have a strong sense of the order in which our details should go and how we can make that happen.

So let's start with continuity.

A continuity story is a kind of story that is both factual and it's making an argument, all of these narrative structures are.

So it is a fact that libraries sustain service through a major global pandemic.

And what that feels like is, we're in a still pool of water, then there's a big splash of change, then we get back to a still pool of water.

Ah, people can borrow things again, ah, people can borrow physical books again, we've figured how to make this work.

An example would be achievement through building on strengths.

You build on your strength in being the center of a community in an academic setting by celebrating the achievements of that community.

And in this case, I wrote an article about 20 years of library book plate celebrations, a faculty promotion ritual at my university to celebrate promotion and tenure.

And it's really something to think about, what is it to hold that space of continuity for a community.

If you can remain somewhat level, we change, we always change, but if you can negotiate and navigate the inevitable changes and remain a stable presence in people's lives, it makes a big difference.

If we can innovate forms of telling that story to help extend the sense of the impact of the libraries, the bedrock of the academic community, that can make a big incidence.

That's one example for a continuity story.

For a transformation story, and this is probably the one you're most familiar with because you have likely heard of Joseph Campbell's work on the hero's journey, which is an amazing story.

Campbell's research was based on the major world religious figures across the planet.

It's really impressive research.

However, it doesn't usually apply to you and me.

And in fact when we tell stories of awe, stories of amazing heroic adventure about ourselves, it tends to come across as a little bit plastic.

So this is where I like to caution people, we do not have one universal story structure, no matter what Campbell said.

We have many story structures.

And you need to find the right one for your stories.

So this idea of there being a sense of awe, a sense of wonder, does apply when we think about our library users and the people who have the benefits of the things that we provide.

So we could argue about this for public libraries as well, but certainly there is clear research for academic libraries and school libraries, that their use increases students' success.

So you can look at all of -- so this is coming out of -- ACRL is really the source for many of these kinds of -- many of this kind of research.

But the idea here is that we know that the library helps people achieve higher levels of academic shoves if the heroes are the people who use the library, and I'll give you an example that I heard at a new director's seminar I was speaking at, maybe a two-parent family with both parents in school at a community college, and they have two children, and the two children need a place to be while the two parents are studying, well, this particular community college was flexible enough that they went ahead and changed how they had their service basis set up.

They made a space for the children to study.

It made a massive difference.

Not only does it make a difference in the achievement of success, like these parents got their degrees.

If you step back and think about the data we have about degree-earning in the United States, overall lifetime earnings are about 85% higher with even one higher degree than without that higher degree.

So you could say, we are champions of lifelong learning, but have we really told the story of the ways that our work contributes to lifelong earning?

Remember that it's not only the data that we collect that are important.

It's also data that we have about the society around us.

So the third kind of story, and one that is especially appropriate, if you don't know where to start with your data, if all you've got is data and a set of questions you think might need answering or you've got somebody coming at you with questions and you're not even sure you have the data to answer them, you may need to justify further data collection.

That does happen.

So a discovery story is great for that.

A discovery story is like a mystery story.

And the mystery story here is, what do we need to be doing next, right?

Like what should the library be prioritizing?

We serve all kinds of needs.

We have all these different identities.

But it makes it very hard to choose sometimes which project is next.

And they can't all happen at once.

So we ask a question of some kind, and then we raise that uncertainty and we provide some data.

We bring that question and we answer it with information.

We heighten a sense of mystery and we heighten a sense of revelation.

And that's the emotional part of this work.

So coming back to our earliest example, when we do a survey to understand what technology people need, we are asking -- 

we're beginning a discovery narrative.

The process of doing that survey and what we learn from it could by itself be a story of, here's the survey we did, here's what we learned.

That's usually a story we tell internally.

The important thing that happened with this story, this example from Emory, is that they told the story externally.

They actually brought that story out into the world.

So that the rest of the world could understand that when libraries make changes, we hope they understand, it's not just because we have the resources or want to.

It's because we've done the best that we can to ascertain what kinds of data do we have and what do we need to meet our needs.

So that's three narrative structures that I hope you can take with you today.

That plus the idea, and I'll come back into it in a second with a different visualization, plus the idea of who is going to retell your stories, those are your big takeaways.

So who is going to retell your stories?

The thing about retelling is it also works in this way, when we think about the work with folk tales in that context, we're talking about stories that have been told over and over again for hundreds or even thousands of years.

These are extremely impactful stories in the history of humanity.

And so what is told and listened to gets stored by the person's brain who is able to do the retelling.

This is not only another pitch for you to find those allied rehearsal audiences.

Maybe it's a leader from a different library that you can run your draft by who can tell you back what they heard.

It's also a pitch just for remembering that our stories only last insofar as they can be retold.

We need representatives, we need lawmakers, we need all kinds of people to be able to retell the story of the value of the library.

So let me mention a story that's been retold a lot lately.

And I'm not here to talk about anybody's particular library practices.

That's not what I'm here today to do, I don't want to make you feel any kind of way about what you are doing or not doing.

But I will note that since my childhood, when library fines were a fact of life, I had to pay them myself out of my allowance, since my childhood, the library world has changed quite a bit.

And whether or not your library still has late fees, it is very clear that we have a different narrative going, especially since about 2020, about how it is that public libraries in particular relate to the public, sometimes ending late fees, making that case for equity, which is not based just in library data.

It's based in socioeconomic data about the United States that shows that the gap, the income inequality in the U.S. is at the highest level it's ever been.

It's really astonishing how little those who have less have and how much those who have more have in this day and age.

And for most of us, we have very little of the overall economic power in the United States.

And so that has created this story that has really taken off.

And this would not have happened without retelling.

This would never have occurred and didn't occur when just individual isolated people were advocating for this change.

I know because I was one of them in the '90s who started advocating for this change, which is, again, not to judge, but it's just to say that I had connected my dots myself and some of my colleagues had and maybe some of you had as well.

But it's not it will the story is retold that the difference really matters.

So this work has a long term vision.

The long term vision is to cultivate data storytelling expertise as a signature expertise of our field so that when communities have data storytelling needs they are met at libraries and by librarians.

That is most of what I have for you here today.

There's lots of time for a few questions.

I look forward to talking with you, especially about the ethics of storytelling, bearing in mind that my starting point is the data that we already gather as libraries.

So whatever you're already gathering should be done in those ethical ways.

But I have a lot of thoughts about this as a data storytelling professor.

But first, let me just say there are ways you can reach me.

I'm on sabbatical right now, which means I'm finishing this book called "Critical Data Storytelling for Libraries," should out next year-ish, keep an eye out for that.

There's my email, my website.

LinkedIn is one of the best ways to get in touch with me, and the DSTL, Data Storytelling Toolkit For Libraries website.

These slides have references.

Anything you want to find may very likely be there.

If you are a fan of story but not of data, I cannot recommend this book highly enough to you, because this makes data visualization accessible to anyone through a series of postcards that you can see that two friends sent back and forth between each other to record the daily events in their lives.

So I recommend that to you.

If you are a fan of data and you know this is your thing, then I recommend to you the book "Counting: How We Use Numbers to Decide What Matters," because it brings back the idea that we have to decide what counts as something before we can count up something.

Sometimes that reframe is very critical to understanding how our library data fits into our community, state, world data.

Finally, there's even more that I've published.

It's all there for you.

I hope you enjoy it.

There are image credits for these slides there for you.

I need to acknowledge my collaborators, could I not do I -- I could not do this work alone.

The AIVC team that we changed our name at Illinois.

Especially my colleagues listed here who think with me all the time.

I'm so grateful to them.

So let me just go back to the how to find me page, and I'll sort of leave that there, and we can talk through your questions.

And thank you so much for your attention here today, I'm so pleased many of you were able to be here and to stay.

>> JENNIFER PETERSON: Fantastic, Kate, thank you so much, it's so exciting.

So much to go back and explore further.

So definitely lots of opportunities for folks to dive in deeper.

There have been some great questions.

Let's see.

What if you have limitations in the database software that you use that makes it difficult to extract pertinent information?

>> KATE MCDOWELL: This is hard.

What you're talking about there is not just pertinent information, it's insights, how can you have insights.

So I would recommend a couple of things to you.

The top one by far, if you've got limitations in that way, is to get connected to RIPL, the research institute for public libraries.

I assume you're coming from a public library, if not, we can talk, but it's assuming that because that's statistically highly likely based where we are.

They can give you some ideas what you can do with Excel.

They've got 101 themselves, 101 and 102 on library research, how do you do it.

It involves thinking through what Excel can do, basically.

I don't think you need fancy data visualization to do any of this.

There are some pedagogical reasons why I don't bring a lot of data visualization into what I do.

I could, and I don't, because I want you to think of yourselves as people with ability with language, that can make things happen.

But I look at the RIPL resources.

I also think about, if you've got Excel, welcome.

Our research shows 75% of libraries, that's really what they use for everything data-related.

So you're in good company.

So that's where you can explore even some basic things like YouTube Excel tutorials to help get you to what you need is the place where you can get to insights.

What does insights look like?

It's often the case that you need to compare months to months to get good insights.

That could be a way you could start thinking about that data.

It's not just a trend line.

It's how is it happening through the course of a year over two to five years, I would say three is kind of a minimum for gaining some insights in that way.

That's some basic pointers for you.

I know you can do it.

I've seen so many people solve problems with these tools.

Our tools don't limit us in many of these ways.

>> JENNIFER PETERSON: Excellent.

There was a great question on whether or not you can recommend any resources on the ethics of data collection.

>> KATE MCDOWELL: So I really like "Data Feminism" by Ignazio and Klein.

They bring together important insights on how data is used and abused, collected and miscollected, when our whole lives lost because of pulling away from the realities of things like injustices that include kidnappings, that include rapes of women.

I'm sorry, I probably should have said trigger warning, but that book comes with a trigger warning, so you should be aware that data feminism is not an easy read but it's an important read.

If you want, I think the best orientation to data ethics, that's where I would go.

I would also recommend, I'm looking at my bookshelf right now, "Race after Technology" by Ruha Benjamin.

I would also recommend ethics in general, especially trying to deal with our legacies of racism in the library world.

I cannot help but mention my top collaborator's name there, Nicole Cooke, because Nicole, Dr. Nicole Cooke at the University of South Carolina, she and I co-authored an article for Library Quarterly called "Social Justice Storytelling" that I'll also recommend you to.

Her work is outstanding.

If you need to think about ethics in general, that is the starting point.

Yeah, that's a few things.

>> JENNIFER PETERSON: I don't know if you saw Nicole was here.

>> KATE MCDOWELL: Oh, yeah, Nichol, mwa.

>> JENNIFER PETERSON: How important is choosing the right platform when sharing your story and data respectively?

So, social media, lectures, articles?

>> KATE MCDOWELL: Yeah, you know, it's interesting to me, the right platform to say it that way, that's interesting.

I think every platform requires some different storytelling approaches.

But just like your rehearsal audiences can be real people that you Zoom with or call up on the phone, your rehearsal audiences can be people who use social media like you do or use these tools like you do, right?

So when I think about the list that you gave, you know and I know too some differences right away, there's length of attention that you're going to have or not have from people.

There's depth that you can go into.

There's how much information can you present versus what can you link out.

Even in an hour with you all, I'm presenting a lot, and I'm linking out to a lot more.

What do you need people to know?

I've worked with Dr. Angelica Loduca in Italy.

Three types of audiences include the general public, professionals, and executives.

The general public needs very succinct, the insights have to be distilled and really easy to understand.

Professionals are people who are kind of in our same field.

They want a lot of detail.

They want to be able to unpack how did you get from the data to the insights.

So that's a different framing of the story and it's going to take a longer time for sure.

Lawmakers might also want this in a City Council kind of space, a lower level lawmaker might want that level of professional detail.

 Executives, again, need succinct things but they need justifications built in there.

Just as important as the platform is the audience.

And I can imagine a world in which there was a matrix of those things too, I probably won't stop to make that, but what I'll say is I think your question is a good one.

The insight in that question is a very good one, that it will be different depending on the different formats that you use.

Rehearse them all.

Find your people.

Try out all your stories.

That's what I would recommend.

>> JENNIFER PETERSON: Yeah, I was really struck by your quick guide to audience attitudes.

I'm a big proponent of thinking about your audience, who is your audience and your emphasis on them being able to tell the story.

So it could be that, you know, across demographics, you know that maybe your teen advocates need something that they can share on social media quickly, maybe it's a social card or something.

So really thinking about your audiences as well as then their capacity to retell your story.

>> KATE MCDOWELL: Yeah.

>> JENNIFER PETERSON: Fantastic.

I don't see -- oh, someone shared a great -- a book about Augusta Baker, since we talked about her at the beginning.

>> KATE MCDOWELL: Yay!

>> JENNIFER PETERSON: Excellent.

Even when you began, thinking about understanding your audience, you know, really who is your audience, there's so much opportunity for really understanding your audience.

We talked a lot now about community-led understanding, so how can you really help them understand.

So much great work.

Any final thoughts you want to share, Kate, before we head off?

>> KATE MCDOWELL: I'll come back, so ethics really fascinates me, and I appreciate that question, and I'll come back, I'll share one other thing, and this is -- I've thought a lot about the difference between the permission you get for telling a personal story, where if it's mine I can tell you but if it's someone else's, I need to ask that person's permission, I need to know they agree to that, right?

And I probably don't want to tell a lot about someone else's suffering if I don't really understand that that person would know what that's going to mean.

So, personal stories, right?

Like when my brother who gives me the permission to tell this story, he's a person with multiple disabilities.

When he was cut off of Medicaid, this was a disaster for my family, but not just my family, there were 250,000 people who --

88% of them were wrongly excised from Medicaid at the end of the pandemic in the State of Florida.

Unfortunately a lot of states have made their -- the rights that people have for appropriate medical support, they've made it very difficult to get to that.

That's a personal story.

So I have my brother's permission to tell you that much about his personal story, right?

But not more.

I'm not going to go into a lot of detail there.

You might want to think about that, how much -- contextualize the suffering, it's not just one person's suffering, but we suffer, humans suffer under regime than dehumanize people, I'll say.

Then there's a culture story.

This is where it gets very tricky indeed.

I told a story that comes from Haiti today and I prefaced it by saying I might not be the best person to tell this story, in fact I know I'm not the best person.

I'm one person and I love the story and I'm doing my best to -- I've done the research to understand as much as I can about that story and I gave you a very quick version here.

Culture stories, there are times when you shouldn't tell another culture's story.

And there are times when -- I don't think you should ever try to be anybody else's voice than your own in a professional context.

Acquire -- we are not comedians.

We want to sustain the trust.

The third kind of audience is institutional stories.

Think in terms of, it's not like we can get permission from a whole institution, but we represent institutions, and institutions have cultures, and those cultures have spaces in them.

You might be in the middle, you might be in two cultures, you might be in six cultures.

So when you think about these questions, the thing that you can -- the thing that you can consider is, there are a lot of good ethical orientations to storytelling.

But they do depend on the context.

And so that is like the quickest two-minute version of something that I spend a couple of weeks on in my 16-week courses.

But since you asked about ethics, I thought I might just dip in there for a minute, because it's such a great question.

I'm so glad people are asking these questions.

You're asking good, hard questions.

And I think that's what we need to do as a field right now.

I think it's what will help to us survive and hopefully, if we can weather these storms, to thrive on the other side and continue to be the institutions that we've always been, which are some of the most important institutions to the functioning of our democracy.

Thank you so much.

>> JENNIFER PETERSON: 

Absolutely.

Thank you so much, Kate.

So much great work.

We're excited to continue to follow it and use it.

And we'll definitely look forward to updates, we can share updates through our channels as well.

So thank you so much.

>> KATE MCDOWELL: Yay, happy to be here.

>> JENNIFER PETERSON: Thank you to all of you who are here.

A reminder that I will be sending you email later today, once the recording is posted.

I'll also automatically send you a certificate for attending today so you don't need to request that.

I'm also going to send you to a short survey as you leave.

The link to the survey will also be in the email.

So if you need to head back to the desk and not complete a survey, but please take some time later.

We really appreciate your feedback.

We'll share it with Kate.

It can help guide their work as well.

So definitely, we appreciate your feedback, it helps us guide our ongoing programming.

Thank you all so much, thanks to our captioner.

And everyone have a great rest of your week.

>> KATE MCDOWELL: Thank you, Jennifer, take care.

>> JENNIFER PETERSON: Thank you so much, Kate.
	
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