 Good morning everyone. Welcome to the Data Management and Visualization with Tableau. We have our guest speaker with us today, Sarah and Murphy from Ohio State University. I'd like to highlight that this is the first of four sessions we have on Data Management and Visualization with Tableau. The next one is on March 10 with Jeremy Buehler from the University of British Columbia and on March 17 with Rachel Llewellyn from the University of Massachusetts, Amherst, and all three of them will be recorded and made available on the YouTube channel. And then we will have a discussion session with all of them on April 21. I am Martha Curie-Lidu and I work here at ARL at the Statistics and Service Quality Program. And one of the reasons we are interested in this topic is because, as this quote from the IBM website says, every day we create 2.5 quintillion bytes of data so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere, sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records and cell phones to name a few. And this data is big data. Now in our libraries we also have a lot of data. And we have started using tools that are known as business intelligence and analytics platforms. And this slide presents the findings from the latest Gardner report. And I recommend that you take a good look at it. You will see in the top quadrant to the right the leaders in this field are being identified including their products like SAS and IBM and Microsoft and Oracle. But below you will notice has a very high score on the ability to execute, the ability to deploy quickly. And we will see some wonderful examples from our colleagues in the libraries. So the gold standard in this area according to this report is tabloid and that's why we are focusing on that. So without any further delay, Sara, the floor is yours. Thank you, Martha. First I'm very happy to be with you today to talk about Tableau. It's one of my favorite things to talk about these days. I have been an enthusiastic Tableau user now for about two and a half years. I started using it. I started with a trial in June of 2012. And I found Tableau through a book actually, through Steven Fuse. Now you see it, Simple Visualization Techniques for Quantitative Analysis. He used visualizations from a number of different data visualization products but the ones that I was most drawn to and resonated the most with were the Tableau visualizations. So I stumbled around and played with Tableau and I haven't turned back. So just a moment here. So why are they interested in data visualization? Why can it be benefit of benefit to your library? Well first, great visualizations help your library to be more efficient. They help your library to process very large quantities of data very quickly. And also they also help groups to ask questions and to drill into your data without much delay and discover insights about problems, the nature of problems, and new understandings. They also help groups to create a shared view of a situation and figure out some ways to move forward. But why Tableau? I've been getting this question often from others in the field who are interested in deploying Tableau at their institution or learning more about the product. What's really neat about Tableau is that you do not need to be a business analyst. You do not need to be a technical expert to query your data and use it. If you're using Tableau you can gather and analyze data in essentially real time. You can share your data with others. And this is data that resides in servers and often messy ways. You can blend data from different data sets together. And Tableau can help your organization support a culture of assessment and data informed decision making. So why Tableau? Why not Excel? Why not Access? And I've really been struggling with answering this question for others. But I did find in a couple of readings that I've done, Phil Simon is one of them, that it's not apples to apples. You have to select the appropriate tool for your situation and what you're doing. Excel might be perfectly acceptable for the project at hand. Access might be acceptable. It just depends on what you're doing. But still, I like this quote. I laugh when I see it. In terms of generating useful multi-dimensional visual analysis, it's like going from an etch just gets to industrial light and magic. And that's pretty true because one of Tableau's inventors has an Oscar because he worked for Pixar. Anyway, I thought today that I would share some visualizations I've done here at Ohio State based on the role of the visualization, its intention, its purpose. And I've embedded at the top of each slide a website that you can go to so that you can play with the visualizations either now or later because the bulk of their value is their interactivity. So PowerPoint cannot do it justice. So I'll just start though with this visualization right here. This is at go.osu.edu ARL statistics. And this is a comparison of OSU's ARL rank to member CIC institutions, which for our organization is of interest for benchmarking purposes. And I plotted out the ARL index for this time period on box plots. And right now Ohio State is highlighted. So this means there's a little highlight tool over here. And if you click on the highlight tool, you can then go click on Ohio State and it will highlight all of OSU's data. This is Michigan way up here, Lucky Michigan. But that's really useful when you are giving a presentation to gray out all the other data and focus on your own. OSU has different benchmark institutions, so we can see the same graph if we click here on for benchmark institutions, double click here. It will just all repopulate with the benchmark institution names and all of the data over here on the right will change. This visual right here is our library's gate count. And it collects OSU has I think 10 or 12 different library locations on the Columbus campus. So this just gathers the daily gate count from each location and aggregates all of that data here in three different ways. So two different ways, sorry. So here we have just the trend line over time from 2013 to December 31, 2014. We have a trend line, which Tableau added for me. And if you roll over this on the visualization, it will give you the regression calculation and the p-value so you can see if this trend is a valid trend or not. And then it also breaks the data down by library location by quarter. And they have filters over here on the right that says that the gate count goes back to 2013. It actually truly goes back to 2002. So you can change the time period and make it larger if you want. All of these are little different widgets so that if you were doing a presentation for a different constituency group, I could cut and paste this. You could cut and paste this out and put it into your presentation. So it's just some nice things. You can also filter by the library location. So hang on here, I'm getting used to ReadyTalk. So right here there's a quick filter so there's a pull down menu. So this whole graph is reset for the 18th Avenue library location. And this way you can see if the trend line has changed for that location. This next dashboard is our live answers data back to 2011. And we have our reference questions separated out by directional questions, basic reference questions which Ohio State views as inventory control type questions. Can you look up a book in the catalog for me? And then research consultations. And you notice we have this huge peak up here and it's been going down, down, down. And that's in the purple. That's for the directional questions. And back in 2009 the Thompson Library opened in a new location and so many of these questions were at the bathroom. So that has changed. But we also have highlight actions on this dashboard and all of the elements of this dashboard are linked together. So I highlight the action on research consultation. It highlights the text table. It highlights the research consultation bar graphs by quarter and year. And this is called a multi-paying graph down here. And it also highlights the research consultations in the trend chart. And what's interesting here is that this trend has been going up since 2011. And that is good for our libraries because we've been making a conscious effort to rebrand ourselves and create engaged librarians. And so we're trying to promote that program in that service. On the right down here we also have a heat map. And this is a filter action in Tableau. So if I click on heat map this takes me to a new dashboard. And this looks at the questions that have come in by hour, by year. So it's a multi-paying graph again. And here are the hours. And then by semester. So winter, semester, spring, summer, fall. And you notice when you get to 2013 it's just spring, may, summer, fall. And that's because in 2012 Ohio State went from quarters to semesters. But when we look at the data this way we have filters on the bottom. And with these filters, oops, let me go back. I jumped ahead of myself. With these filters, because OSU switched from quarters to semesters just recently it's sometimes helpful to look at the week and the year. So we could look at the 34th week and the year which is typically the start of fall semester on the semester system. And we could look at the, I think it's the 38th week of the year is the typical start of fall quarter. And that way then you can see how reference death transactions have evolved by hour, by day in those two time periods. We can also look at the type of question, the format of the question, the day of the week if you want to. And all of this data, this is coming from three different systems right now because previously we had different software that was handling our research and consultations and reference transaction recording. So we're blending data from three different sources right now to create this output. So this visual is quite recent. I was learning how to do hidden worksheets on dashboards and was involved with the library's diversity programming. So we were trying to find a way to promote the library's collections as well as the program. And the topic for this diversity lecture was related to human trafficking. So we mined our library's catalog for subject headings related to human trafficking and child trafficking. And it kind of then threw it into this word cloud all of the subject headings for those records that had those two subject headings in them. And then this is set up with actions that if you click on a word in the word cloud, so if I clicked on women's rights for example, and then it will open up a list of the titles related to women's rights. So that's the hidden table. And then if I click on a title, it takes me directly to the library's catalog and I can access the synthesis and electronic resource. I can access it here. So a couple more dashboards as we have time that I have here. Hang on a second. I have some dashboards that bring together some operational data related to our Iliad borrowing and our special collections. And I also have one that if we have time, I don't know. But I can share the website that you can go to where you can look at the whole Thompson Library here at OSU and find the location of a book on a shelf. It still has some quirks. It's a work in progress, but it can be done. So here is the Iliad dashboard. So this right now, it's kind of a proof of concept. We don't really have it in use yet here at OSU, but it's taking our Iliad transactional data for borrowing and it's mashing it together with some data that was extracted from the university's HR system and their student information system because right now OSU's departments for users in Iliad are not the same as the departments that the university assigns to majors or what else. My point is they do not blend nicely or play nicely. And then they don't blend well with the subject areas that we've assigned to our librarians. So this takes and looks at all of the subjects. This particular visualization is looking at all of the subjects that are assigned to our liaison, David Linkove. And it shows that history is the major, for his particular portfolio, history is the major user. And it just kind of gives, these are like little spark lines that show that there's an ebb and flow over the year of requests. But it shows the breakdown of articles versus books. But you can actually get to a list of the titles that were scanned by clicking on this link or the list of the titles that were borrowed. So the title scan would be like the document delivery. The titles borrowed would be the physical book. And so once he clicks on those, he gets to this list right here. And it's broken down by user department. There's some filters that we can't see right now that are over on the side. So he can filter by groups of years to see some trends of the currency of what is being borrowed, et cetera. We have another slide here. So this data has been blended from Iliad, Sierra, and other sources. Our librarians are assigned to the user department, but we have found that the user department is not a good surrogate for interdisciplinary research areas. So we have a second dashboard, which I don't have time to share. Yeah, I don't think I put in today. But this dashboard looks at Iliad borrowing by language. And that helps for our Jewish studies area, where we have a program here at Ohio State, but it's a program supported by faculty from multiple departments. And Sara, what's Sierra? What system is Sierra? That's our triple I, our triple I innovative catalog system. So this visual right here, this is another proof of concept. I'm calling it our OSU Special Collections Deflected Titles List. And I've been trying to embed more context in dashboards. So it talks about how interlibrary loan algorithms automatically deflect requests for items in our library's special collections. So our librarians in our interlibrary loan office never see these requests. But our special collections librarians have indicated that they would like to see the requests that are being denied to help inform our digitization priorities. So there is a way to query this information out of Iliad. And we've taken this information and we've matched it together with our library's Google pick list. And then we've told Tableau to only show us items that are not on the Google pick list. And so right now if I select a collection like our Cartoon Library and Museum, it shows that in the time period, I think it was fiscal year 14, our library's deflected 413 interlibrary loan requests for this collection. And 398 of these were not available in Google Books, so they're candidates for digitization. And so if they click here on the purple, it will take them to the list of those titles. And then lastly, we've been just starting to play around with service profiles for our subject librarians. And this is going to change. It's really just throwing some ideas right now. It's not informing anything. But we're trying to see the distribution of our workload across all different departments at OSU. And so over here on the left, we just have the basic faculty FTE by college here at OSU. And then we can look at the colleges assigned to the librarians and see how many faculty FTE are in the portfolio for an individual librarian. So this is a work in progress and it's not, again, I want to emphasize it's not informing any decisions right now. We're just playing with it. But one thing that is helpful is to look at these profiles in groups. So looking at arts and humanities as a separate group to look at other indicators and the number of students and faculty that are served by one individual library liaison. So it is 225 and I wanted to be sure I left time for questions. So I can bring certain dashboards up and show you them directly on Tableau right now. Or again, I did put the URLs at the top of every slide so that you can have that and you can look at them after the webinar today if you'd like to spend some more time with some of these visuals. Not all of them are available because not all of them are public information but some of them are. Lisa Horowitz is asking if you can show some of the back end. Yes. Okay, hang on a minute. Let me remember how to use ReadyTalk. Let's see, I'm going to share my desktop. Okay, can you see my desktop? Yes. Alright, I opened my Tableau file. Let me just go start with a simple one. Let's start with the human trafficking. What I'm looking for. Okay, so here is the dashboard. And so again, if I click on human rights, it opens all of the titles that are related to that particular subject heading. And if I click on anthropological approaches to gender-based violence, it's going to send me waiver pretty fast to the subject, I'm sorry, the catalog record. But to nuts and bolts, all of these are just little widgets. So if I go to the worksheet, it's just a word cloud. And we have dragged the subject onto the text field. If I change this, this is what it really looks like when you first start. If I go back to automatic, it looks, it's called a tree map. But if you take a tree map and you turn it to text, it turns into a word cloud. And then the other part of this dashboard here is just a simple text table. I go to sheet. What I really like about Tableau is this show me box over here on the right. It sees these, some of these, it grays out all of the visuals that are not appropriate for the dimensions and the measures that you have selected. And so right here it's just saying for what I've selected, again, if you would be appropriate or a text table. So that's really, I like that feature. It saves me a lot of time. Let's see if I just open the new sheet. You're opening, there is a question about whether you are building those or whether they are already turnkey. Clearly you are building these, but very easily it looks like. Very easily. I'm getting there. So right now I'm working with an extracted dataset because this is, I had packages to share it with you, but let's see. These are the fields that I, I have four different datasets that I'm pulling together here. And so I think I just, excuse me, it's been a while since I put this together, but like here I have the subjects and I can say just give me a count of the number of records for those individual subjects. And then I go to show me, make that a tree map, turn that into a word cloud. And then you just have to clean up some of the formatting. So it's a drag and drop. Now there is a little bit of a learning curve, but Tableau does provide a lot of really good training videos on their website. And yes, so I can overwhelm you very easily. A couple more questions here. Steve Healer is asking, how difficult was it to clean up and blend the data you use from lead banters and your other consultation database into Tableau? And who did the cleaning up work? You know, it really just depends on the project. Let's see, live answers. I did that one a while ago. I think for live answers I did a lot of the clean up the hard way in Excel and it was a lot of matching fields and getting fields in the right column in just one giant Excel spreadsheet. But other times I clean up data, like whenever I put library catalog data together, I take it, our library catalog, I don't have the ability right now to write a direct SQL query onto our library catalog. I wish I did. So sometimes I get really messy data out of our search system to extract the data from that system. And so rather than spending a lot of time cleaning the data and matching everything up, I will only extract like two fields at a time. So usually something like a unique number like the bib record number and then like subject headings. And then I will use some techniques in Excel to clean the data. Other times I don't need to clean the data, I can match it up in Tableau. Tableau has some nice features so that I can come in here and like the subject broad here I can create groups and sets. And so I can do some data cleaning here and saying I only want abuse together as one idea and then I'll just call it abuse and say okay and then it will make another field right here that has those named groups. So that's really a useful feature. I'm going to get rid of that because I don't want it though. Let me get out of this. I don't want to save. Hang on a minute. I don't know what I put things. Yeah, and Jason Mitchell is asking once you've created a visualization how are you able to share that visualization? Is that shared visualization interactive or just a flat image? It is interactive. It is interactive and there's a couple different ways. If it's public data and not sensitive I can create what's called a package of workbook in Tableau and I can upload it to my Tableau public space and then it can be embedded in websites. So for instance one of the visuals I showed you today is actually embedded in our assessment departments page. I think it's right here. I'll clean that up right here. So this is when you create a Tableau workbook it gives you the code and you can embed it in a website so I can click on here and what it's really doing is taking me to the CIC comparison page. That's right here and I can change this pretty quickly to the benchmark institutions right there. So here's the highlighting feature I was showing. So if I click the highlighter and then click Ohio State I can look at Ohio State. If I hold down my shift key I can look at Ohio State and Michigan and it just shows Michigan. It just shows Ohio State. See how this text is opening. Those are called tooltips so I can edit them to say whatever I want it to say. So for instance let me let's see hopefully my memory will serve me well here. Thompson. Okay well hang on a minute. I think I have another way of getting there. Actually I know what I can do. I can dub Tableau. Here we go I know what to do. Excuse me here. So I have a little blog at that u.osu.edu slash murphy.465 and if you click right here on the library of viz at OSU it takes you directly to my Tableau public page. It's easier to explain how to get there that way. And so if you come down here to, here it is. This is what I was looking for. This is the fun one that I was talking about where you can map where your book is in the library. So it's not perfect to have some quirks but like if I looked for b125.c4 which is the first example it's going to find it up here. See it's not perfect it finds all of those. I think I have to put it in quotes. But this book, The Humanist Way in Ancient China so basically I have taken a tool tip and this is insert the title field insert the call number is located in. This is joining a couple different data sets together to create this and everything is interactive you can even click here to get to information about the title in the library catalog which is not there. There it is. Lots of different things you can do. Where is the data stored? Which data for this? Well because this one is Tableau Public it's been uploaded to the Tableau server. So I lost track of the question. So this is one way of sharing the data. Another way for data that is of a more sensitive nature. What I was showing you in Tableau was kind of the production side. So this is a good analogy would be like Adobe Acrobat. So if you have the production side of Adobe Acrobat you can edit PDFs, you can create forms all this other stuff. And then I can come up here and save it as an export whatever I've made into a packaged workbook. Once it's a packaged workbook it cannot be edited and the data you're only going to see is the aggregate of the data for the worksheets. And then I can store that packaged workbook in a central place. So I could store it in a shared server space here at OSU. And then others in our organization who do not have Tableau can download the free Tableau reader and open their packaged workbook using the reader. And the functionality is just a little bit different. It looks a little bit different but they are able to use the filters. They can see what I can see. They just can't change the fields that I've put together. They can't change the formulas, the calculations, things like that. And do you use only Tableau public? Do you use a couple of different versions of it? Right now I think I'm using Tableau 8.2. So this right here is the Tableau professional. This is Tableau professional, desktop professional. Not desktop personal but the desktop professional. So this is one step up and this allows me to connect to all these different this one I'm using on my Mac. It looks a little different on my PC but I can connect to all these different kinds of data sources. And usually on my PC it would say down here it would say ODBC and I could connect right there to ODBC but not right now on my Mac. Usually I just connect to a Tableau data extract because if you work directly on the server it's just too slow so it's better to high find it's easier to extract the data. And you can set a data extract to extract every Friday at 4 a.m. if need be and it will refresh your extract so that's my preference. But I haven't disclaimer here that I haven't done that with a file yet. I'd like to. You can also connect directly to an access. I don't have access on my machine so I think that's why it's not showing up. But yeah there's a lot of you can connect directly to Google Analytics if you want to. Lots of different options. And there is a question about how does it connect to the ILS system but I believe you said you cannot do direct SQL query. Right now I'm not able to do. I suspect that this Postgres SQL is how but I understand that I need some kind of connector so I haven't been able to do it yet. But you've been doing a lot of wonderful things and very quickly. And thank you to you and to all the people who are asking questions. There are a couple of questions we haven't addressed today but I'm going to write them down and we're going to have the fourth session of this series. It's just going to be Q&A and we're going to have Sara again with us. Thank you Sara. No thank you. So if you are using Tableau and other analytics platforms please join the arl-assess at arl.org it's a Google group and post examples there and let's keep the discussion and the sharing going. Thank you very much. Enjoy the rest of your day. Thank you. Bye. Bye bye.