Using Data for DecisionMaking: An Environmental Scan Case
Using Data for DecisionMaking: An Environmental Scan Case Study NNLM Research Data Management Webinar Series June 2019 Welcome! Audio Questions? Everyone is muted to cut back on background noise.
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Using Data for Decision-Making: An Environmental Scan Case Study Donna Harp Ziegenfuss, EdD, MS, BSc Data Coordinator, NNLM, Midcontinental Region (MCR), and Associate Librarian, Faculty Services, J Willard Marriott Library University of Utah Email: [email protected] Outcomes At the end of this webinar, attendees will be able to: 1.
2. 3. Describe the process of planning and conducting an environmental scan Identify and use existing data sources when conducting an environmental scan Explore the Voyant Tools website and be able to upload and explore your own data The Environmental Scan Process Decision makers use environmental scans to collect, organize, and analyze data on their assets and shortcomings in external and internal environments, to guide strategic planning and decision making. (Wilburn, Vanderpool & Knight Prev Chronic Dis. 2016 PubMed PMID: 27536901;
PubMed Central PMCID: PMC4993117. 2016, p. 1) PEST Analysis SWOT Analysis Why an Environmental Scan? What is it? Explores multiple facets of an issue or a problem Both qualitative and quantitative data Not just a scholarly undertaking - involves a variety of data sources Perspectives of different stakeholders Uses external and internal sources Why do an Environmental Scan? Purpose is intentional and internal and use findings
Articulation to mission, goals and strategic plan How will you do an Environmental Scan? Is it a Data-Driven vs DataInformed process? It is not all about the data it is only one factor 7 Steps of an Environmental Scan (Wilburn, Vanderpool, & Knight, 2006) Step 1: Draw on experience to determine leadership and capacity for the project (usually a collaborative process) Step 2: Establish the focal area and purpose of the environmental scan Step 3: Create and adhere to a timeline and set incremental goals Step 4: Determine information to be collected for the environmental scan (a variety of sources and perspectives) Step 5: Identify and engage stakeholders opinions Step 6: Analyze and synthesize results from the environmental
scan into a concise summary report Step 7: Disseminate results and conclusions to key stakeholders Librarians & Data Science Case Study: Step 1: Identification of the problem/need Step 2: Conduct a situational analysis of the problem (existing data; both internal and external) to identify priorities Step 3: Analyze and synthesize results from the environmental scan Step 4: Explore the preliminary solution for my problem Step 5: Put findings into action with a website (Rapid Instructional Design approach) Step 6: Planning of stage 2 Evaluation of the developed resources by variety of stakeholders
Step 1: Identifying the Problem Data Science, Big Data, Small Data oh my. Learning about what you do not know Many definitions of Data Science Big Data Little (Small) Data Hard to know where to start The 4 Vs of Big Data Step 2: Conduct a situational analysis of the
topic/problem to identify priorities and patterns Job Postings (Data Librarians & Data Scientists) Past Data Related Webinar Transcripts Online Course Descriptions Online Experts (Interviews)
NNLM Existing Resources RD3website Scholarly Articles on Librarianship & Data Websites & Organizations Social Media
Capture Step 3: Data Analysis- to Begin No Programming Analyze data using Voyant Tools ( a free tool) concept maps, word clouds and most common words Used NVivo, (not a free qualitative research tool) to run word frequency and word phrase searches after preliminary identification Coding frequency numbers for categories; theme development Used Excel to gather and categorize internal NNLM resources that I will be able to link to as I develop the data roadmap resource Disclaimer: This is not a systematic review process of resources, I used what I could find and access but it provided some findings and themes that I will use to move forward with my original goal to design a Data professional Development Roadmap resources for librarians at any level of data knowledge to use
Easy to Use Data Analysis Tools Sinclair, Stfan and Geoffrey Rockwell, 2016. Voyant Tools. Web. http://voyant-tools.org/ Katherine Hanz and Sandy Hervieux, from McGill University who at the 2019 LOEX Conference, their presentation inspired me to share how I use Voyant Tools 1. Upload your file (or files) 2. Explore the settings to view data in each frame in different ways 3. Download
data to take into other data analysis tools The Voyant Tools Interface Job Descriptions Data Librarian Data Scientist
Hybrid Librarian (Digital Scholarship, Assessment, Metadata with Data) I think the biggest from data Quotes Enculturation misconception was that I needed to be an expert in data. The easiest part is just knowing all you
have to do is start talking to people. Laura Wright NNLM Capstone Summit 2018 Seeing Connections Exploration I came to be a data librarian it was actually kind of a natural extension of what I was already doing it's an area where the deeper that you get in you start to see more and more
possibilities open up in ways that you can expand your base of knowledge. Marisa Conte NNLM Capstone Summit 2018 I think people who are interested in data librarianship should first learn a lot about the research process first and contextualize what they need to know before they approach it from a libraries perspective to figure out what their needs. Kevin Read NNLM Capstone Summit 2018 The biggest misconception was that I was going
to have to be involved in all of the nitty-gritty of researcher data and curation and all of the details and I'm learning I can just be a consultant and be perfectly fine with that. Sheila Green NNLM Capstone Summit 2018 Emerging Identities Making Contributions I'm trying to like develop and foster a community of practice because I know I can't do this alone and so what I hope is that we create this learning environment so that we all support each other. Andrea Lynch NNLM Capstone Summit 2018
Step 4:Preliminary Findings Hard to know where to get started it is overwhelming Confusion in different definitions of data science roles A gap in reusing and sharing qualitative data Themes that emerged from all of the data analysis (Nvivo analysis) Seeing Connections (awareness) Exploration (space to experiment and explore low stakes) Enculturation (not just in big data, but in assessment communities, metadata communities, teaching communities) Emerging Identities (scoping out where you fit enhancing your librarian identity) Making Contribution (finding a level where you can contribute to build confidence; making data actionable for you)
Step 5: Making Data Actionable Will use the themes and insights to design and develop a scaffolded or leveled professional development data resource Aligns to NLM goals: Goal 2: Reach more people in more ways through enhanced dissemination and engagement 2.1 Know NLM users and engage with persistence 2.4 Enhance information delivery Goal 3: Build a workforce for data-driven research and health 3.1 Expand and enhance research training for biomedical informatics and data science 3.4 Engage the next generation and promote data literacy Step 6: Next Steps - Data collection of stakeholder engagement with new resources
1. Used a Rapid Instructional Design model to prototype ideas, test out and adjust 2. Interview Stakeholders (NNLM administration, Library Liaisons, NNLM members and partners) to further describe and create a rich description of the problem, project and solutions) Do a SWOT (strengths, weaknesses, opportunities, threats) analysis of the pilot program 3. Recruit partners and collaborators to help develop a richer and more meaningful roadmap from a variety of perspectives Develop a community of practice (CoP) to engage all librarians and library staff (academic and public) to help sustain this community Brief Voyant Tools Demonstration https://voyant-tools.org
Whats Next? Future RDM Webinar Series Topics: Whats in a Data Story? Understanding the Basics of Data Storytelling Thursday, August 15, 2019 1:00pm - 2:00pm PT Region/Office: National Instructor(s): Kay Deeney Please Fill Out Todays Evaluation!
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