What about data literacy? Business librarians and the ACRL Framework for Information Literacy
Condon, Patricia B.
Pothier, Wendy G.
To meet current and future workforce needs, business students entering the job market should be literate in working with and using data for a variety of purposes. Our presentation focuses on addressing business librarians as key stakeholders in the development of services to help improve data literacy in business and economics. While general and discipline-specific data literacy competencies have been identified, our work focuses on the data literacy needs seen in the disciplines of Business & Economics. In previous work (2019), we identified seven baseline business data literacy competencies that filled gaps in student and employee knowledge around data and data literacy. More recently, we mapped those data literacy competencies to the ACRL Framework for Information Literacy for Higher Education (Framework). This mapping helps establish a bridge between foundational library professional documents, business librarianship, and data literacy both in higher education and in the workplace, extending the conversation to how the Framework informs data literacy instruction. In this presentation we summarize the seven baseline business data literacy competencies and outline how they can be mapped to the Framework. From this mapping, we explore how business librarians can incorporate teaching data literacy skills and provide instruction informed by the Framework. This research will provide audience members with both context and foundation to develop strategies that integrate data literacy and the Framework into library instruction specific to the disciplines of Business & Economics.
 I'd like to welcome to our digital stage Patricia Condon. Patricia are you there? Can you hear me? Can you see me? Yes, hello. Yes, hi wonderful. Hi, very good to see you. I see here Patty. Do you prefer Patricia Patty? What would you prefer I call you? Oh, you can call me Patty. I go by both. Thank you. Okay, I just saw Patty on the on the screen, screen name. Okay, so so Patty if I may I wanted to ask you physically what town or city are you in right now and if we ever come to visit what is one of the tourist attractions or something that you have to check out when you're in that region? Yes, I'm in Durham, New Hampshire, which is the home of the University of New Hampshire main campus. The most common reason tourists come to New Hampshire is usually for outdoor recreation. So it's hiking, camping, swimming, walking on our little bit of the Atlantic coastline. But if you come to visit the U and H campus, I'd recommend staying at the three chimneys end, which is rumored to be haunted. Oh, okay, now that's interesting. And by the way, a personal note, my aunt lived in Nashua, New Hampshire for many years. So I had the chance to go up there when I was a kid. Beautiful, beautiful state, absolutely gorgeous. Okay, wonderful. So Patty, Patricia is an assistant professor and research data services librarian at the University of New Hampshire, as you just said. And her topic is what about data literacy, business librarians and the ACRL framework for information literacy. And just to review, the ACRL is the Association of College and Research Libraries. So Patty, you'll have 15 minutes for your presentation, then afterwards about five minutes for Q&A. And I would say your screen is shared. I'd like to give you the digital stage, please. Thank you very much. And thank you all for attending today. And thank you also to the conference organizers. Today, we're going to be going to, we're going to explore the connections between data literacy and information literacy, especially as it relates to library instruction and discuss strategies for integrating data literacy and the ACRL framework for information literacy for higher education. I'll refer to that as the framework from now on. So I am Patty Condon, research data services librarian, my co-presenter, Wendy Pothier, is the business and economics librarian also from the University of New Hampshire. She's unable to join us today, but she sends her best regards as she's on holiday in Iceland. And she's sending me beautiful photos. So I think that she both sends her regards and maybe only small regrets that she's not here because the pictures are lovely that she's sending. The University of New Hampshire is located in a small town called Durham. And it's the flagship research institution for the state of New Hampshire with an enrollment of about 15,000 students. So in our session, we're going to discuss our proposed business data literacy competencies. I'm going to outline our recent work on how they've mapped to the ACRL framework and also close with a brief summary and future directions of our work. In the professional business literature, there's been an ongoing discussion about talent development in the workforce around data literacy. And many businesses have been focusing on the infrastructure to support data in their organizations, but they've seen a gap in data literacy skills of employees coming into the workforce. So taking this conversation into consideration and our roles as information professionals in higher education teaching about data. In 2019, we introduced seven business data literacy competencies, which we proposed as baseline competencies for business students to help prepare them entering the workforce and working with data at various levels on the job. The competencies reflect data literacy requirements for working professionals at an entry or broad level within the organization, recognizing that greater levels of competencies would be developed in areas as needed by job type or through experience. The link that's provided on the slide goes to a research guide that has definitions of the competencies that you can review as we discuss them. And there's also a link to the ACRL framework for information literacy that you can also take a look at as well. So building on the development of the data literacy competencies, we recently mapped those competencies to the ACRL framework for information literacy. So briefly, the framework is a foundational document adopted by the Association of College and Research Libraries, the ACRL, in 2016 to engage information literacy more fluidly in higher education settings. It's anchored by six core concepts referred to as frames that outline potential learning outcomes. So we mapped those to our competencies to those six frames. We conducted the mapping because we wanted to more clearly articulate the connections between data literacy and foundational documents in our field. And we wanted to contextualize the conversation around our proposed business data literacy competencies as complementary to information literacy to help promote implementation. Through this work, we provide examples and strategies to help business information professionals move the competencies into practice. And we felt that aligning them with the frames would help provide a guide for doing this. And by mapping the competencies to the framework, we wanted to normalize data literacy as part of existing library instruction. And we wanted to strengthen the voice of business information professionals as key stakeholders in conversation and instruction of data literacy. I provided the link again, Kesa. You didn't catch it on the earlier slide. Again, it will provide definitions of the competencies and a link to the framework so that you can have a reference while we're while I'm talking. But before we dive into the mapping, I want to review a couple of assumptions that we had. So first, we extended the concept of information need that you find in the framework to data need. So we didn't just so we so we while the framework talks about information and information need, we really focused on data and data need. We approached our interpretation of the framework from the lens of how business students would apply the frames in their professional lives. And we discussed the competencies at both looking outward towards professional roles and inward at students learning in the classroom. So for the first frame, the first frame that we talk about is authority is constructed and contextual. This frame highlights that the trustworthiness of an information source depends on many factors such as who created it, why it was created, and how it's being used. The frame acknowledges biases within information sources, biases within the availability of information, and biases in the worldview of both the content creator and the content user. We map this frame to the the competency understanding data in a business context and evaluating the quality of data sources. So data used in business environments often vary from data that's used in scholarship. The context around collection and use of data has different motivations. The recognition of authoritative voices is distinct and the systems for establishing trustworthiness, authority, and credibility are also unique. And sources for business data can be challenging to evaluate because the proprietary nature of much of the data leads to a lack of transparency about the collection and analysis of it. So exposing business students to commonly used business databases which are subscribed to by libraries and also corporate entities furthers the conversation about authority and the context of data literacy. Using these sources information professionals can explore the conversation about how authority is determined and how quality is evaluated. Our next frame is information creation as process. This emphasizes that format of information sources, modes of delivery, and the process of creating and publishing an information source. And we can use this frame to look at data as a special format as a particular format. We mapped this frame to data organization and storage. So depending on one's role in an organization, the value of business data may lie in the interpretation and presentation of that data or it might be in that original organization storage of the data. So responsibilities for different stages of the data lifecycle often align with different positions in an organization. So the one who reads and interprets raw data, for example, might be a completely different individual from the person who makes decisions from those interpretations. So professionals can benefit from just understanding the basic concept that data are created, they're organized and stored before the point that they can be analyzed, interpreted, summarized, and presented. Business information professionals can introduce business students to the data lifecycle. Students don't have to be experts in all stages of the data lifecycle. Rather the goal is to help them understand the journey that data takes from being generated to presented to reused in secondary analysis. And this can help students contextualize their future interactions with data as employees. So moving on to our next frame, one of my favorites, information has value, which addresses the power of information by highlighting the legal, societal, and economic value and how that value can impact production, dissemination, and use of information. The value of information is contextual, right? When extrapolated to the often proprietary nature of data for business use, value comes from data contributing to an economic and strategic edge. So we map this frame to understanding data used in business context, data-driven decision making, and data ethics and security. Data is a business asset and contains real currency in terms of profit, operations, logistics, and many more. When a business can access better quality data in higher quantities, the more likely that business can apply those data to make decisions which would ideally increase profit and help companies achieve missions and goals. The value of data is further emphasized in making business decisions. While often the role of management and leadership, decisions actually happen at many levels of the organization and data plays a role along the way. And significant here is the legal value of data, including intellectual property and attribution, and also the social value of data, including the intention and use of data. Students can start to see the concept of this play out in the classroom as they commonly rely on access to data through campus library subscriptions. And we can discuss the value, the cost, and the ethics of access of data. Just as different universities may have access to different databases for research, so will different companies have access to different data sources. Next we look at research as inquiry. This frame draws on transferable ideas emphasizing that research is an iterative process of asking questions, explorations, exploring findings, and engaging with that information, even though it might look different in business context than it does in academic research. We've mapped this frame to interpreting data and data-driven decision making. Interpreting data involves engaging with the data and understanding the purpose and the context of that data. The interpretation of data takes a distinct significance in research for business operations because those interpretations can help drive decision making, sort of data-driven decisions. And we can look at decision making as iterative and driving business research. Each decision leads to a new question, a new data need, and then additional decisions are made. Information professionals can contextualize data in non-academic scenarios by highlighting business practices that mirror this research's inquiry process. So for example, iteration is a foundation of lean startups and is often used in entrepreneurial endeavors in which professionals will try something, change what they're doing based on the data or experience, and then adapt something new in order to maintain that progress. So our next frame, scholarship as conversation, underscores that the body of academic and professional literature is a product of many voices and different perspectives participating in a back and forth discussion across time and space. Scholarship is an open and participatory exchange of ideas, right? So we've mapped this frame to evaluating the quality of data and communicating and presenting effectively with data. When evaluating data sources for the inclusion in the conversation, it's important to recognize that businesses will not be represented, some businesses won't be represented as well. For instance, a public business is required to publish annual reports, a private business can be less transparent and isn't required to do so. And so that means that the conversation can be biased potentially towards public companies, which, whether there's more information available about those companies. And an important piece of the conversation is how to tell a story with data through effective communication and presentation. Business information professionals can explain to students why companies contribute evenly to the conversation and how this may skew the record. And the frame scholarship as conversation can be interpreted broadly and has valuable learning outcomes such as helping students learn to share and demonstrate ideas through skilled presentation. And lastly, we move to the, to searching as strategic exploration. This frame emphasizes that search process is a complex, systematic journey that's contextualized by the worldview of the searcher, the scope of the investigation, the context of that data need, and also the tasks required to achieve the outcome. We match this frame to data organization and storage and understanding data used in business context. Data organization and storage is foundational to beginning the exploration of data. To explore data, it must have been collected or generated, organized and stored. And if the data are not securely stored, cannot be located or not well documented, but then the time and money will be lost to having to recreate it if recreation is possible. And identifying who produces data and what kinds of data they produce helps students and employees understand the kinds of documents and data that are available and can be accessed. Understanding what's accessible within one scope of the search is essential when looking for available and usable data. Information professionals can provide exposure to databases, exposure to different kinds of tools for finding and creating data and discuss the structures and formats of data. We can also discuss what data are available, why some data have not been created, and what decisions can be made with the data that is accessible. This can lead to discussions around the kinds of reports and data that exist, conversations about primary and secondary data, and equities of data access, and training in the responsible conduct of research. To summarize, we identified these seven, we proposed seven baseline business data literacy competencies for students to prepare them entering the workforce. We mapped them to the AcerL framework for information literacy to create a bridge at the intersection of library canon, business librarianship, and data literacy. We began to introduce strategies to support the integration of data literacy into library instruction for business and economics. And in future research, we plan to explore the relevancy of the proposed business data literacy competencies to the workforce and if they address the skills gaps that employers have witnessed in the professional spaces. So I want to thank you and I would welcome future conversations and collaborations. Feel free to reach out to either Wendy or I. We will be at taking questions now, but also at the speaker's meetup tomorrow. So thank you so much for attending and listening. Thank you very much, Patricia Condon. Yeah, a really fantastic presentation. We'd like to give a virtual applause for wherever you are watching. We're lighting up here. We have a couple of questions for you. So I'll jump right into our audience questions. The first one is, do you have examples of student aha moments of thinking more about data authority or value? That's a really, that's a great question. I'm trying to think outside of this context as well, outside of business context. I don't have a specific example that comes to mind off the top of my head. I do love aha moments. So I will say that one of my colleagues often refers to me on the data services librarian as the librarian you'll think later. Because a lot of times the librarians will go into a classroom around information literacy when the students need resources. They need to write a literature review. They're looking for something specific. Where the data pieces are often come a little bit later. There's not often a point of need that I go in and help students with unless they're coming to me to find specific data. So that's a good question. I hope that I do get one of those. Sorry I didn't have an example. Fair enough, no problem. Great. Let me move on to our next question from our audience. And that says, can anything be done about the lack of transparency of the business data sources? Big issue of course. Yeah, yeah, that is a big issue. And it's a conversation that Wendy and I have had back and forth, right? Because she works very much in the business and economics realm. And I often work more in the open science and the stem areas where where transparency is extremely important. And that's why we really wanted to highlight that that distinction between the public and the private sources of data. So because it's proprietary, because when we are thinking about business data, there's so it doesn't align with the other scientific data literacy competencies out there because it's it's proprietary. We are talking about institutions that are using data for an economic and strategic edge. And so therefore, I don't I don't know if there's a way because it's if the value is actually a cash value at this point. And so that transparency, the the not sharing of data gives the businesses the the additional value towards their towards their mission and goals. So while it would be great to see, I'm not sure what the answer to that would be. But it is a it is a complicating factor. And one of the reasons why we did think that that that that business data literacy competencies kind of have a unique twist than say the science data literacy competencies do. Super. Okay. Very good. I apologize. My chat box here bounced around up and down. So hopefully, this isn't a repeat of something. The one I have here is that the students have any completely unexpected results while playing with the data visualizing it. That was our first question. I remember that. So let me just see. Again, I apologize. But sometimes we bounce here. I think this is it. Could I ask my technical team please to give me the last question that was there. Fortunately, I'm bouncing up and down very good. I have it now here. Excellent. So it says here, and this is replied to your last answer. I see very interesting. What implications does this have for business data literacy instruction? Yeah. So I think part of it, I think that, yeah, I think that part of that is is definitely highlighting we're able to talk about the bias in the in the in the data source at this point. So we're able to use it almost as a teaching moment to think about to think about how we identify authority, how we identify trustworthiness in business data and sort of what we do have access to. Also, we can talk about the cost of data, you know, in terms of if we're if we're able to purchase data, what does that mean? So I think that the impact or the implications potentially is that we have to focus on public data, but we have to make sure there are students know that it's a piece of the data that's out there, and that when they're in the professional setting that they may be generating data or or buying data that isn't open and isn't transparent, and they need to think about the quality and the evaluation of that data. So I think it's a it has a huge impact, and we have to we have to be we have to be transparent about that lack of transparency. Yeah, very good. I think those are great closing words. And with that, I'd like to thank you for your presentation, Patty, as well as taking the time to answer some of our questions. And again, once again, a virtual applause. Thank you so much. Thank you so much. And yeah, greetings on beautiful New Hampshire. Absolutely a gorgeous thing. Great. Thank you. And yeah, I was
 I'd like to welcome to our digital stage Patricia Condon. Patricia are you there? Can you hear me? Can you see me?  Yes, hello. Yes, hi wonderful. Hi, very good to see you. I see here Patty. Do you prefer Patricia Patty?  What would you prefer I call you? Oh, you can call me Patty. I go by both. Thank you. Okay, I just saw Patty on the  on the screen, screen name. Okay, so so Patty if I may I wanted to ask you physically what town  or city are you in right now and if we ever come to visit what is one of the tourist attractions  or something that you have to check out when you're in that region? Yes, I'm in Durham, New Hampshire,  which is the home of the University of New Hampshire main campus. The most common  reason tourists come to New Hampshire is usually for outdoor recreation. So it's hiking, camping,  swimming, walking on our little bit of the Atlantic coastline. But if you come to visit the U and H  campus, I'd recommend staying at the three chimneys end, which is rumored to be haunted. Oh, okay,  now that's interesting. And by the way, a personal note, my aunt lived in Nashua, New Hampshire for  many years. So I had the chance to go up there when I was a kid. Beautiful, beautiful state,  absolutely gorgeous. Okay, wonderful. So Patty, Patricia is an assistant professor and research  data services librarian at the University of New Hampshire, as you just said. And her topic is  what about data literacy, business librarians and the ACRL framework for information literacy.  And just to review, the ACRL is the Association of College and Research Libraries. So Patty,  you'll have 15 minutes for your presentation, then afterwards about five minutes for Q&A.  And I would say your screen is shared. I'd like to give you the digital stage, please.  Thank you very much. And thank you all for attending today. And thank you also to the  conference organizers. Today, we're going to be going to, we're going to explore the connections  between data literacy and information literacy, especially as it relates to library instruction  and discuss strategies for integrating data literacy and the ACRL framework for information  literacy for higher education. I'll refer to that as the framework from now on.  So I am Patty Condon, research data services librarian, my co-presenter, Wendy Pothier,  is the business and economics librarian also from the University of New Hampshire. She's unable to  join us today, but she sends her best regards as she's on holiday in Iceland. And she's sending me  beautiful photos. So I think that she both sends her regards and maybe only small regrets that  she's not here because the pictures are lovely that she's sending. The University of New Hampshire  is located in a small town called Durham. And it's the flagship research institution for the  state of New Hampshire with an enrollment of about 15,000 students. So in our session, we're  going to discuss our proposed business data literacy competencies. I'm going to outline our  recent work on how they've mapped to the ACRL framework and also close with a brief summary  and future directions of our work. In the professional business literature, there's been  an ongoing discussion about talent development in the workforce around data literacy. And many  businesses have been focusing on the infrastructure to support data in their organizations, but they've  seen a gap in data literacy skills of employees coming into the workforce. So taking this conversation  into consideration and our roles as information professionals in higher education teaching about  data. In 2019, we introduced seven business data literacy competencies, which we proposed as baseline  competencies for business students to help prepare them entering the workforce and working with data  at various levels on the job. The competencies reflect data literacy requirements for working  professionals at an entry or broad level within the organization, recognizing that greater levels  of competencies would be developed in areas as needed by job type or through experience.  The link that's provided on the slide goes to a research guide that has definitions of the  competencies that you can review as we discuss them. And there's also a link to the ACRL framework  for information literacy that you can also take a look at as well.  So building on the development of the data literacy competencies, we recently mapped  those competencies to the ACRL framework for information literacy. So briefly,  the framework is a foundational document adopted by the Association of College and Research Libraries,  the ACRL, in 2016 to engage information literacy more fluidly in higher education settings.  It's anchored by six core concepts referred to as frames that outline potential learning outcomes.  So we mapped those to our competencies to those six frames. We conducted the mapping because we  wanted to more clearly articulate the connections between data literacy and foundational documents  in our field. And we wanted to contextualize the conversation around our proposed business data  literacy competencies as complementary to information literacy to help promote implementation.  Through this work, we provide examples and strategies to help business information professionals  move the competencies into practice. And we felt that aligning them with the frames would help provide  a guide for doing this. And by mapping the competencies to the framework, we wanted to  normalize data literacy as part of existing library instruction. And we wanted to strengthen the voice  of business information professionals as key stakeholders in conversation and instruction  of data literacy. I provided the link again, Kesa. You didn't catch it on the earlier slide. Again,  it will provide definitions of the competencies and a link to the framework so that you can have  a reference while we're while I'm talking. But before we dive into the mapping, I want to review  a couple of assumptions that we had. So first, we extended the concept of information need that  you find in the framework to data need. So we didn't just so we so we while the framework  talks about information and information need, we really focused on data and data need.  We approached our interpretation of the framework from the lens of how business students would apply  the frames in their professional lives. And we discussed the competencies at both looking outward  towards professional roles and inward at students learning in the classroom.  So for the first frame, the first frame that we talk about is authority is constructed and contextual.  This frame highlights that the trustworthiness of an information source depends on many factors  such as who created it, why it was created, and how it's being used. The frame acknowledges biases  within information sources, biases within the availability of information, and biases in the  worldview of both the content creator and the content user. We map this frame to the the  competency understanding data in a business context and evaluating the quality of data sources.  So data used in business environments often vary from data that's used in scholarship. The context  around collection and use of data has different motivations. The recognition of authoritative  voices is distinct and the systems for establishing trustworthiness, authority, and credibility are  also unique. And sources for business data can be challenging to evaluate because the proprietary  nature of much of the data leads to a lack of transparency about the collection and analysis  of it. So exposing business students to commonly used business databases which are subscribed to  by libraries and also corporate entities furthers the conversation about authority and the context  of data literacy. Using these sources information professionals can explore the conversation about  how authority is determined and how quality is evaluated. Our next frame is information  creation as process. This emphasizes that format of information sources, modes of delivery, and  the process of creating and publishing an information source. And we can use this frame to look at data  as a special format as a particular format. We mapped this frame to data organization and storage.  So depending on one's role in an organization, the value of business data may lie in the  interpretation and presentation of that data or it might be in that original organization  storage of the data. So responsibilities for different stages of the data lifecycle often  align with different positions in an organization. So the one who reads and interprets raw data,  for example, might be a completely different individual from the person who makes decisions  from those interpretations. So professionals can benefit from just understanding the basic  concept that data are created, they're organized and stored before the point that they can be  analyzed, interpreted, summarized, and presented. Business information professionals can introduce  business students to the data lifecycle. Students don't have to be experts in all stages of the  data lifecycle. Rather the goal is to help them understand the journey that data takes from  being generated to presented to reused in secondary analysis. And this can help students contextualize  their future interactions with data as employees. So moving on to our next frame, one of my favorites,  information has value, which addresses the power of information by highlighting the legal, societal,  and economic value and how that value can impact production, dissemination, and use of information.  The value of information is contextual, right? When extrapolated to the often proprietary nature of  data for business use, value comes from data contributing to an economic and strategic edge.  So we map this frame to understanding data used in business context, data-driven decision making,  and data ethics and security. Data is a business asset and contains real currency in terms of profit,  operations, logistics, and many more. When a business can access better quality data in higher quantities,  the more likely that business can apply those data to make decisions which would ideally increase  profit and help companies achieve missions and goals. The value of data is further emphasized in  making business decisions. While often the role of management and leadership, decisions actually  happen at many levels of the organization and data plays a role along the way. And significant here  is the legal value of data, including intellectual property and attribution, and also the social  value of data, including the intention and use of data. Students can start to see the concept of  this play out in the classroom as they commonly rely on access to data through campus library  subscriptions. And we can discuss the value, the cost, and the ethics of access of data. Just as  different universities may have access to different databases for research, so will different  companies have access to different data sources. Next we look at research as inquiry. This frame  draws on transferable ideas emphasizing that research is an iterative process of asking questions,  explorations, exploring findings, and engaging with that information, even though it might look  different in business context than it does in academic research. We've mapped this frame to  interpreting data and data-driven decision making. Interpreting data involves engaging with the  data and understanding the purpose and the context of that data. The interpretation of data takes a  distinct significance in research for business operations because those interpretations can  help drive decision making, sort of data-driven decisions. And we can look at decision making  as iterative and driving business research. Each decision leads to a new question, a new data need,  and then additional decisions are made. Information professionals can contextualize data in  non-academic scenarios by highlighting business practices that mirror this research's inquiry  process. So for example, iteration is a foundation of lean startups and is often used in  entrepreneurial endeavors in which professionals will try something, change what they're doing  based on the data or experience, and then adapt something new in order to maintain that progress.  So our next frame, scholarship as conversation, underscores that the body of academic and  professional literature is a product of many voices and different perspectives participating  in a back and forth discussion across time and space. Scholarship is an open and  participatory exchange of ideas, right? So we've mapped this frame to evaluating the quality of  data and communicating and presenting effectively with data. When evaluating data sources for the  inclusion in the conversation, it's important to recognize that businesses will not be represented,  some businesses won't be represented as well. For instance, a public business is required to  publish annual reports, a private business can be less transparent and isn't required to do so.  And so that means that the conversation can be biased potentially towards public companies,  which, whether there's more information available about those companies. And an important piece  of the conversation is how to tell a story with data through effective communication and presentation.  Business information professionals can explain to students why companies contribute evenly to  the conversation and how this may skew the record. And the frame scholarship as conversation can be  interpreted broadly and has valuable learning outcomes such as helping students learn to  share and demonstrate ideas through skilled presentation. And lastly, we move to the,  to searching as strategic exploration. This frame emphasizes that search process is a complex,  systematic journey that's contextualized by the worldview of the searcher, the scope of the investigation,  the context of that data need, and also the tasks required to achieve the outcome.  We match this frame to data organization and storage and understanding data used in business  context. Data organization and storage is foundational to beginning the exploration of data.  To explore data, it must have been collected or generated, organized and stored. And if the data  are not securely stored, cannot be located or not well documented, but then the time and money  will be lost to having to recreate it if recreation is possible. And identifying who  produces data and what kinds of data they produce helps students and employees understand the kinds  of documents and data that are available and can be accessed. Understanding what's accessible within  one scope of the search is essential when looking for available and usable data. Information  professionals can provide exposure to databases, exposure to different kinds of tools for finding  and creating data and discuss the structures and formats of data. We can also discuss what data  are available, why some data have not been created, and what decisions can be made with the data that  is accessible. This can lead to discussions around the kinds of reports and data that exist,  conversations about primary and secondary data, and equities of data access, and training in the  responsible conduct of research. To summarize, we identified these seven, we proposed seven baseline  business data literacy competencies for students to prepare them entering the workforce. We mapped  them to the AcerL framework for information literacy to create a bridge at the intersection of  library canon, business librarianship, and data literacy. We began to introduce strategies to  support the integration of data literacy into library instruction for business and economics.  And in future research, we plan to explore the relevancy of the proposed business data literacy  competencies to the workforce and if they address the skills gaps that employers have witnessed  in the professional spaces. So I want to thank you and I would welcome future conversations and  collaborations. Feel free to reach out to either Wendy or I. We will be at taking questions now,  but also at the speaker's meetup tomorrow. So thank you so much for attending and listening.  Thank you very much, Patricia Condon. Yeah, a really fantastic presentation. We'd like to give a virtual  applause for wherever you are watching. We're lighting up here. We have a couple of questions  for you. So I'll jump right into our audience questions. The first one is, do you have examples  of student aha moments of thinking more about data authority or value?  That's a really, that's a great question. I'm trying to think outside of this context as well,  outside of business context. I don't have a specific example that comes to mind off the top of my head.  I do love aha moments. So I will say that one of my colleagues often refers to me on the data  services librarian as the librarian you'll think later. Because a lot of times the librarians will  go into a classroom around information literacy when the students need resources. They need to  write a literature review. They're looking for something specific. Where the data pieces are  often come a little bit later. There's not often a point of need that I go in and help students  with unless they're coming to me to find specific data. So that's a good question. I hope that I  do get one of those. Sorry I didn't have an example. Fair enough, no problem. Great. Let me move on to  our next question from our audience. And that says, can anything be done about the lack of  transparency of the business data sources? Big issue of course. Yeah, yeah, that is a big issue.  And it's a conversation that Wendy and I have had back and forth, right? Because she works very much  in the business and economics realm. And I often work more in the open science and the stem areas  where where transparency is extremely important. And that's why we really wanted to highlight  that that distinction between the public and the private sources of data. So because it's  proprietary, because when we are thinking about business data, there's so it doesn't align with  the other scientific data literacy competencies out there because it's it's proprietary. We are  talking about institutions that are using data for an economic and strategic edge. And so therefore,  I don't I don't know if there's a way because it's if the value is actually a cash value at this  point. And so that transparency, the the not sharing of data gives the businesses the the  additional value towards their towards their mission and goals. So while it would be great to  see, I'm not sure what the answer to that would be. But it is a it is a complicating factor. And  one of the reasons why we did think that that that that business data literacy competencies  kind of have a unique twist than say the science data literacy competencies do.  Super. Okay. Very good. I apologize. My chat box here bounced around up and down. So hopefully,  this isn't a repeat of something. The one I have here is that the students have any completely  unexpected results while playing with the data visualizing it. That was our first question.  I remember that. So let me just see. Again, I apologize. But sometimes we bounce here. I think  this is it. Could I ask my technical team please to give me the last question that was there.  Fortunately, I'm bouncing up and down very good. I have it now here. Excellent. So it says here,  and this is replied to your last answer. I see very interesting. What implications does this have  for business data literacy instruction? Yeah. So I think part of it, I think that, yeah, I think  that part of that is is definitely highlighting we're able to talk about the bias in the in the  in the data source at this point. So we're able to use it almost as a teaching moment to think about  to think about how we identify authority, how we identify trustworthiness in business data and  sort of what we do have access to. Also, we can talk about the cost of data, you know, in terms of  if we're if we're able to purchase data, what does that mean? So I think that the impact or the  implications potentially is that we have to focus on public data, but we have to make sure there are  students know that it's a piece of the data that's out there, and that when they're in  the professional setting that they may be generating data or or buying data that isn't open and isn't  transparent, and they need to think about the quality and the evaluation of that data. So I  think it's a it has a huge impact, and we have to we have to be we have to be transparent about that  lack of transparency. Yeah, very good. I think those are great closing words. And with that,  I'd like to thank you for your presentation, Patty, as well as taking the time to answer some of  our questions. And again, once again, a virtual applause. Thank you so much. Thank you so much.  And yeah, greetings on beautiful New Hampshire. Absolutely a gorgeous thing. Great. Thank you.  And yeah, I was
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