Course Introduction
Libraries and information services are increasingly data-rich environments where effective management depends on understanding how users discover, access, use, and engage with information resources. Libraries generate valuable data through circulation systems, electronic resource platforms, catalogue searches, repository activity, reference services, visitor records, interlibrary loans, acquisitions, and user surveys. The Library and Information Services Data Analytics Training Course equips participants with practical skills for transforming these diverse datasets into actionable intelligence that supports service planning, resource allocation, user engagement, collection development, digital transformation, and evidence-based library management.
Library data originates from integrated library systems, discovery platforms, institutional repositories, databases, e-journal platforms, digital libraries, authentication systems, circulation records, acquisition systems, reference desks, learning platforms, and user feedback mechanisms. Participants will learn how to collect, clean, validate, integrate, and manage these datasets while addressing common challenges such as inconsistent metadata, duplicate records, fragmented systems, missing information, incompatible data structures, and changing usage patterns. The course emphasizes data quality, appropriate data definitions, documentation, and integration as essential foundations for reliable library analytics.
The programme explores analytical methods for understanding library users, collections, services, and resource utilization. Participants will learn how to analyse circulation patterns, database usage, electronic-resource access, search behaviour, reference enquiries, facility utilization, interlibrary loans, repository downloads, and other service indicators. These techniques can help library professionals identify high- and low-use resources, understand user needs, detect changing information behaviours, evaluate service demand, and support collection development decisions. The course also examines segmentation approaches that can reveal differences in information-use patterns among students, researchers, faculty, staff, and other user groups while respecting privacy and ethical requirements.
Financial and operational analytics are also central to effective library management. Participants will develop practical capabilities for analysing acquisitions expenditure, subscription costs, cost-per-use, supplier performance, staffing, opening hours, space utilization, equipment, collection growth, and service delivery efficiency. Spreadsheets, databases, business intelligence platforms, dashboards, statistical methods, and visualization tools will be used to convert routine library records into management intelligence. Participants will learn how to develop meaningful key performance indicators and communicate findings through reports and dashboards that support library directors, university managers, information professionals, and institutional decision-makers.
The course incorporates emerging technologies that are reshaping library and information services, including artificial intelligence, machine learning, natural-language processing, recommendation systems, digital repositories, usage analytics, automated metadata tools, and predictive analytics. Participants will explore how these technologies can support demand forecasting, collection recommendations, user-service personalization, discovery improvement, automated reporting, and resource optimization. Attention is also given to privacy, cybersecurity, intellectual property, data governance, algorithmic bias, responsible AI, and ethical handling of user information, particularly where analytics involve sensitive behavioural or personally identifiable data.
By the end of the training, participants will be able to use data analytics to strengthen library planning, information-resource management, user services, digital services, financial decision-making, and institutional reporting. They will develop practical skills for analysing resource usage, user behaviour, service performance, collection effectiveness, expenditure, digital engagement, and emerging information-service trends. Through applied exercises and an integrated analytics project, participants will develop dashboards, analytical reports, and decision-support products that translate library data into actionable insights for improving service quality, user experience, operational efficiency, resource allocation, and strategic planning.
Duration
5 days
Who Should Attend
-
University and college librarians responsible for library management, planning, services, and performance improvement.
-
Library and information services managers overseeing physical, digital, and electronic information resources.
-
Academic librarians supporting teaching, learning, research, and student information needs.
-
Digital librarians and repository managers working with electronic resources and digital information systems.
-
Library systems and information technology professionals managing integrated library and discovery platforms.
-
Collection development and acquisitions professionals responsible for resource selection, procurement, and evaluation.
-
Electronic resource and subscription managers analysing database, journal, and digital-resource usage.
-
Reference and user-services professionals seeking to understand demand and improve information support services.
-
Library monitoring, evaluation, research, and assessment professionals measuring service performance and user outcomes.
-
Institutional data analysts and information management officers supporting library and higher education analytics.
-
Records, archives, documentation, and information professionals working with information-use and service datasets.
-
Library directors and senior administrators responsible for strategic planning, budgeting, and resource allocation.
-
Researchers and academics studying information behaviour, library services, digital resources, and user engagement.
-
IT and digital transformation professionals supporting library technology modernization and data integration.
-
Consultants and technical advisors working on library modernization, digital transformation, information management, and analytics.
Course Objectives
-
Develop practical knowledge of data analytics concepts and methods applicable to modern library and information service management.
-
Apply systematic approaches for collecting, cleaning, validating, integrating, and managing circulation, usage, collection, financial, and user-service datasets.
-
Analyse library user behaviour, information-resource usage, search patterns, service demand, and engagement across different user groups and platforms.
-
Evaluate collection performance, electronic-resource utilization, circulation trends, database usage, repository activity, and information-resource effectiveness.
-
Apply financial and cost analytics to acquisitions, subscriptions, licensing, cost-per-use, supplier performance, and library resource allocation decisions.
-
Develop dashboards and performance indicators for monitoring library services, digital resources, user engagement, collections, facilities, and operational performance.
-
Use statistical, comparative, and trend analysis to identify changing information needs, emerging usage patterns, service gaps, and opportunities for improvement.
-
Explore applications of artificial intelligence, predictive analytics, recommendation technologies, natural-language processing, and automation in library services.
-
Strengthen the ability to interpret library analytics and communicate evidence-based findings to library leadership, institutional managers, users, and other stakeholders.
-
Design an integrated library analytics framework that supports user-centred services, collection development, digital transformation, resource optimization, privacy, and strategic decision-making.
Comprehensive Course Outline
Module 1: Foundations of Data Analytics for Library and Information Services
-
Understanding the role of data analytics in library planning, information services, collection management, user experience, and institutional decision-making.
-
Key concepts covering library metrics, usage analytics, information behaviour, service indicators, benchmarking, performance measurement, and evidence-based management.
-
Mapping library stakeholders, information flows, service processes, data sources, reporting requirements, and analytical decision-making needs.
-
Identifying common library data challenges, analytical gaps, data-quality issues, and opportunities for strengthening evidence-based information services.
Module 2: Library Data Collection, Quality, Integration, and Information Management
-
Designing reliable data-collection processes for circulation, catalogue use, electronic resources, reference services, repositories, acquisitions, and user feedback.
-
Applying data cleaning, validation, standardization, deduplication, metadata management, and consistency checks to library datasets.
-
Integrating integrated library systems, discovery platforms, electronic-resource systems, authentication tools, institutional repositories, and survey data.
-
Developing data dictionaries, metadata standards, quality-assurance procedures, documentation practices, and information-management workflows for library analytics.
Module 3: Spreadsheet, Database, and Business Intelligence Analytics
-
Using spreadsheets to organize, clean, summarize, calculate, analyse, and visualize library and information-services datasets.
-
Understanding database structures, relationships, queries, data integration, and efficient management of large collections and usage datasets.
-
Developing interactive dashboards for circulation, electronic-resource use, collections, acquisitions, reference services, facilities, and user engagement.
-
Applying business intelligence techniques to consolidate multiple library information sources and generate timely management reports and decision-support products.
Module 4: User Behaviour, Service Demand, and Engagement Analytics
-
Analysing library visits, circulation, catalogue searches, discovery activity, reference enquiries, resource access, and other indicators of user engagement.
-
Segmenting usage patterns across students, faculty, researchers, staff, departments, programmes, and other appropriate user groups while protecting privacy.
-
Identifying peak service periods, changing information behaviours, underused services, emerging user needs, and opportunities for service improvement.
-
Developing user-engagement dashboards and analytical reports to support service design, outreach, staffing, opening hours, and user-experience improvements.
Module 5: Collection Development, Resource Usage, and Electronic Resource Analytics
-
Analysing circulation, downloads, database access, journal usage, ebook activity, repository engagement, and other indicators of information-resource utilization.
-
Evaluating collection performance by subject, format, age, programme relevance, user group, usage frequency, and other appropriate characteristics.
-
Applying cost-per-use and comparative analytics to electronic resources, subscriptions, databases, journals, licensing arrangements, and information-resource investments.
-
Using resource-usage evidence to support collection development, renewal decisions, deselection, acquisitions planning, and alignment with teaching and research needs.
Module 6: Library Finance, Procurement, Operations, and Resource Analytics
-
Analysing library budgets, acquisitions expenditure, subscription costs, licensing fees, procurement activity, and expenditure patterns across resource categories.
-
Monitoring supplier performance, procurement lead times, contract utilization, resource delivery, and other operational indicators affecting library efficiency.
-
Analysing staffing levels, workload patterns, service points, opening hours, facilities, study spaces, equipment, and technology utilization.
-
Developing financial and operational dashboards to support budgeting, resource allocation, procurement planning, staffing decisions, and service optimization.
Module 7: Research Support, Digital Libraries, and Institutional Repository Analytics
-
Analysing institutional repository deposits, downloads, research-resource usage, scholarly communication activity, and digital-content engagement.
-
Monitoring research-support services, reference activity, information-literacy programmes, research consultations, and other academic library contributions.
-
Evaluating digital-library performance using access trends, content usage, user journeys, search activity, and availability indicators.
-
Using analytics to strengthen research support, open-access services, repository development, digital collections, scholarly communication, and research visibility.
Module 8: AI, Predictive Analytics, Automation, and Emerging Library Technologies
-
Exploring artificial intelligence, machine learning, natural-language processing, recommendation systems, and intelligent search applications in library services.
-
Applying predictive analytics to forecast resource demand, service utilization, collection requirements, subscription needs, and emerging user-service patterns.
-
Examining automated metadata generation, digital discovery, content classification, chatbot services, and workflow automation for information professionals.
-
Evaluating emerging technologies with attention to privacy, intellectual property, algorithmic bias, data quality, transparency, cybersecurity, responsible AI, and human oversight.
Module 9: Library Data Governance, Privacy, Cybersecurity, and Ethical Analytics
-
Establishing library data governance frameworks covering user records, circulation information, electronic-resource usage, access controls, data standards, and accountability.
-
Protecting sensitive user and institutional information through appropriate privacy, anonymization, retention, access-control, and cybersecurity practices.
-
Identifying data-quality, licensing, copyright, cybersecurity, operational, and analytical risks that may affect library information systems and decision-making.
-
Promoting responsible analytics through ethical data use, transparency, privacy protection, inclusive analysis, secure information management, and clear communication of limitations.
Module 10: Integrated Library and Information Services Analytics Project
-
Designing an integrated library analytics framework connecting users, collections, digital resources, services, finance, operations, research support, and institutional priorities.
-
Cleaning and analysing a practical library dataset to identify usage trends, service demand, resource performance, cost patterns, and improvement opportunities.
-
Developing a professional library dashboard, collection analytics report, service-performance scorecard, or management decision-support product.
-
Presenting evidence-based findings and an actionable improvement roadmap for strengthening user services, resource management, digital transformation, operational efficiency, and strategic planning.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
