Training Course on Library Data Management and Analytics

Training Course on Library Data Management and Analytics

Introduction

In an increasingly data-driven world, libraries are transforming into dynamic hubs of information, requiring robust data management and sophisticated analytics capabilities to optimize services and demonstrate value. Training Course on Library Data Management and Analytics empowers library professionals with the essential skills to harness the power of library data, transitioning from traditional collection management to strategic, evidence-based decision-making. By mastering data lifecycle management, understanding user behavior, and leveraging emerging technologies like AI and machine learning, participants will unlock actionable insights that enhance user experience, streamline operations, and ultimately solidify the library's critical role within its institution and community.

This program goes beyond basic statistics, delving into the intricacies of research data management, metadata standards, and data privacy. It equips participants with practical tools and techniques for data cleansing, data visualization, and predictive analytics, enabling them to identify trends, forecast needs, and proactively adapt library services to evolving demands. Participants will gain hands-on experience with industry-standard software and frameworks, fostering a culture of data literacy and innovation within their organizations.

Course Duration

10 days

Course Objectives

Upon completion of this training course, participants will be able to:

1. Strategically manage library data throughout its entire lifecycle, from acquisition to archiving.
2. Implement best practices for data collection, organization, and preservation.
3. Apply metadata standards and practices for improved data discoverability and interoperability.
4. Utilize data cleansing techniques to ensure data accuracy, consistency, and reliability.
5. Perform descriptive and inferential statistical analysis on diverse library datasets.
6. Generate compelling data visualizations using modern tools to communicate insights effectively.
7. Analyze user behavior and engagement patterns to optimize resource allocation and service delivery.
8. Understand the principles of research data management (RDM) and support researchers in their RDM needs.
9. Navigate data privacy and security considerations, ensuring ethical and compliant data handling.
10. Explore the application of artificial intelligence (AI) and machine learning (ML) in library operations and analytics.
11. Develop data-driven strategies for collection development, outreach, and resource planning.
12. Leverage library analytics to demonstrate institutional value and advocate for resources.
13. Foster a culture of data literacy and evidence-based decision-making within their library.

Organizational Benefits

• Move from intuition to data-driven insights, leading to more effective strategic planning and resource allocation.
• Identify underutilized resources and high-demand areas, ensuring collections and services are aligned with user needs.
• Tailor services, recommendations, and outreach based on a deep understanding of user behavior and preferences.
• Streamline workflows, automate routine tasks, and identify areas for process improvement through data analysis.
• Quantify the library's impact and value with compelling data, strengthening arguments for increased support and funding.
• Anticipate future trends and user needs through predictive analytics, allowing for agile and responsive service innovation.
• Ensure adherence to data privacy regulations (e.g., GDPR, local privacy laws) and best practices for data security.
• Equip staff with critical skills, fostering a culture of continuous improvement and interdepartmental collaboration around data.

Target Audience

1. Librarians
2. Library Managers and Administrators
3. Data Stewards and Data Coordinators.
4. Information Scientists.
5. Digital Services Librarians.
6. Assessment Librarians.
7. IT Professionals.
8. Graduate Students

Course Outline

Module 1: Introduction to Library Data Ecosystems

• Understanding the diverse types of data generated in libraries (circulation, usage, catalog, e-resources, patron data).
• The evolving role of data in modern librarianship and information science.
• Concepts of data literacy and data-driven decision-making in library contexts.
• Identifying key stakeholders and data sources within a library system.
• Case Study: Analyzing a university library's e-resource usage data to identify popular databases and inform subscription renewals.

Module 2: Fundamentals of Data Management for Libraries

• Data lifecycle management: planning, collection, organization, preservation, sharing, and reuse.
• Developing a Data Management Plan (DMP) for library projects and initiatives.
• Principles of data integrity, accuracy, and consistency.
• Choosing appropriate data storage solutions and backup strategies.
• Case Study: Designing a DMP for a new digital archival project, considering data formats, storage, and long-term access.

Module 3: Metadata Standards and Practices

• Introduction to metadata: descriptive, structural, administrative, and preservation metadata.
• Key metadata standards in libraries (e.g., MARC, Dublin Core, RDA, MODS).
• The role of metadata in data discoverability, interoperability, and quality.
• Strategies for creating and managing high-quality metadata.
• Case Study: Applying Dublin Core metadata to a collection of digitized local history photographs to enhance searchability.

Module 4: Data Collection and Acquisition

• Methods of data collection: automated system logs, surveys, interviews, web analytics.
• Strategies for collecting clean and relevant data from various library systems.
• Understanding APIs and their role in data extraction.
• Ethical considerations in data collection, including informed consent and anonymization.
• Case Study: Developing a strategy for collecting user feedback data on a new library service using a combination of online surveys and system analytics.

Module 5: Data Cleansing and Preparation

• Common data quality issues (missing values, inconsistencies, duplicates, errors).
• Techniques for data cleaning: standardization, de-duplication, error correction.
• Data transformation and normalization for analysis.
• Introduction to data manipulation tools (e.g., Excel, OpenRefine, basic SQL).
• Case Study: Cleaning a patron demographic dataset to remove inconsistencies and prepare it for analysis of service utilization.

Module 6: Introduction to Statistical Analysis for Librarians

• Descriptive statistics: measures of central tendency (mean, median, mode) and variability (standard deviation, range).
• Inferential statistics: understanding hypothesis testing and confidence intervals.
• Basic statistical tests relevant to library data (e.g., t-tests, chi-square).
• Interpreting statistical results and their implications for library services.
• Case Study: Using descriptive statistics to analyze circulation patterns and identify peak usage times for different collection types.

Module 7: Data Visualization Principles and Tools

• Principles of effective data visualization: clarity, accuracy, impact.
• Choosing the right chart type for different data and purposes (bar charts, line graphs, pie charts, scatter plots).
• Introduction to popular data visualization tools (e.g., Tableau Public, Power BI, Google Data Studio, Excel charts).
• Best practices for creating visually appealing and informative dashboards.
• Case Study: Creating an interactive dashboard to visualize library website traffic and user engagement patterns using Google Analytics data.

Module 8: Library Usage and User Behavior Analytics

• Analyzing circulation data, e-resource usage statistics, and physical space utilization.
• Understanding user search queries and information-seeking behaviors.
• Segmenting users based on their demographics and usage patterns.
• Leveraging analytics to personalize recommendations and improve discoverability.
• Case Study: Analyzing overdue book data to identify patterns and implement proactive communication strategies to reduce late returns.

Module 9: Research Data Management (RDM) Support

• Overview of the RDM landscape and its growing importance in research institutions.
• The role of libraries in supporting researchers with RDM planning, data storage, and sharing.
• Understanding data repositories and persistent identifiers (DOIs).
• Compliance with funder mandates for data sharing and preservation.
• Case Study: Developing a mock RDM plan for a research project, outlining data types, storage, and sharing protocols.

Module 10: Data Privacy, Security, and Ethics

• Understanding key data privacy regulations (e.g., GDPR, CCPA) and their impact on libraries.
• Best practices for data security: encryption, access control, anonymization.
• Ethical considerations in collecting, storing, and using patron data.
• Developing data governance policies and procedures for libraries.
• Case Study: Discussing a scenario involving a data breach in a library and developing a response plan focusing on privacy and communication.

Module 11: Introduction to Predictive Analytics in Libraries

• Concepts of predictive modeling and forecasting.
• Simple predictive techniques relevant to libraries (e.g., regression analysis for predicting future circulation).
• Identifying potential applications for predictive analytics in library services (e.g., collection development, staffing).
• Limitations and challenges of predictive analytics.
• Case Study: Using historical circulation data to predict future demand for specific genres or authors, informing purchasing decisions.

Module 12: AI and Machine Learning in Library Contexts

• Basic concepts of Artificial Intelligence (AI) and Machine Learning (ML).
• Current and potential applications of AI/ML in libraries (e.g., automated cataloging, personalized recommendations, chatbots, enhanced search).
• Ethical considerations of AI in library services (bias, transparency).
• Understanding the role of librarians in an AI-driven future.
• Case Study: Exploring how a library could leverage AI to improve its recommendation engine for new acquisitions based on user borrowing history.

Module 13: Data-Driven Storytelling and Communication

• The art of telling compelling stories with data.
• Crafting impactful narratives from data insights for various audiences (administrators, patrons, funders).
• Effective presentation techniques for data visualizations and reports.
• Using data to advocate for library value and demonstrate impact.
• Case Study: Preparing a presentation for library leadership demonstrating the impact of a new outreach program using key performance indicators (KPIs) and data visualizations.

Module 14: Building a Data-Driven Culture in Libraries

• Strategies for fostering data literacy among library staff.
• Encouraging a mindset of continuous improvement through data.
• Developing internal data sharing and collaboration frameworks.
• Leadership's role in promoting data-informed decision-making.
• Case Study: Designing a training program for library staff on basic data literacy concepts to encourage broader engagement with data.

Module 15: Future Trends and Emerging Technologies

• Current trends in library data management and analytics (e.g., linked open data, semantic web).
• Emerging technologies and their potential impact on libraries.
• The evolving skill set for library professionals in the data age.
• Opportunities for innovation and research in library data.
• Case Study: Brainstorming potential applications of blockchain technology for library resource sharing and intellectual property management.

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

·         Interactive lectures and presentations.

·         Group discussions and brainstorming sessions.

·         Hands-on exercises using real-world datasets.

·         Role-playing and scenario-based simulations.

·         Analysis of case studies to bridge theory and practice.

·         Peer-to-peer learning and networking.

·         Expert-led Q&A sessions.

·         Continuous feedback and personalized guidance.

 

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.