Information Science in the Cloud Era: Data Infrastructure, AI, and Modern Information Management
In our previous exploration, we established the foundational connections between Library and Information Science (LIS) and digital marketing technologies. This second installment extends that analysis to cloud computing platforms, data science methodologies, and modern data architectures. As organizations increasingly rely on AWS, Azure, and GCP to build sophisticated information ecosystems, the principles of information organization, retrieval, and management pioneered in LIS become even more relevant—not merely as historical analogies, but as practical frameworks for effective system design.
Cloud Platforms as Information Infrastructure
The Evolution from Physical to Digital Information Repositories
Traditional libraries developed sophisticated physical infrastructures to house, organize, and provide access to information resources. Modern cloud platforms have evolved these concepts into digital form:
Imagine the library building/facility itself. This physical structure, with its controlled environment for housing and accessing information, is akin to the data centers with regional distribution offered by cloud providers. These data centers are the physical locations where the cloud infrastructure resides, ensuring reliability and availability across different geographic areas.
Think about the stacks and shelving systems that organize the books. In the cloud, these are mirrored by storage services like Amazon S3, Azure Blob Storage, or Google Cloud Storage. These services provide the digital "shelves" for storing vast amounts of data in an organized and accessible manner.
The reading rooms and access points in a library are where patrons go to consume the information. In the cloud world, content delivery networks (CDNs) such as CloudFront, Azure CDN, or Cloud CDN serve a similar purpose. They distribute content geographically closer to users, ensuring fast and efficient access, much like having multiple reading rooms in convenient locations.
The catalog systems that help you find the books you need have their digital equivalent in database services like Amazon RDS, Azure Cosmos DB, or Google Cloud SQL. These services provide structured ways to organize, index, and query data, making it easy to locate specific information within the vast digital library.
Finally, consider the interlibrary loan networks that allow you to access resources from other libraries. This concept translates to multi-region replication and edge computing in the cloud. By replicating data across multiple regions and processing it closer to the user (at the "edge"), cloud platforms ensure data availability and reduce latency, effectively allowing users to access information from anywhere, just like an interlibrary loan expands the reach of a local library.
So, in essence, cloud platforms provide the digital infrastructure and services that mirror the essential functions of a physical library, enabling the storage, organization, access, and sharing of information on a massive scale.
This evolution maintains the core information science objectives while addressing contemporary scale, accessibility, and performance requirements.
Comparative Analysis: AWS, Azure, and GCP Information Services
Each major cloud provider implements information management services that reflect traditional library functions:
Amazon Web Services (AWS)
AWS organizes its vast service catalog according to information lifecycle principles:
Information Creation and Ingestion
Amazon Kinesis: Real-time data collection and processing
AWS Data Pipeline: Orchestrated data movement
AWS Glue: ETL service for data preparation
Information Organization and Storage
Amazon S3: Object storage with metadata capabilities
AWS Lake Formation: Centralized permission management for data lakes
Amazon DynamoDB: NoSQL database for flexible schema management
Information Discovery and Access
Amazon Athena: Query service for analyzing data in S3
Amazon Kendra: AI-powered search service
Amazon QuickSight: Business intelligence for data visualization
Information Preservation and Governance
Amazon Glacier: Long-term archival storage
AWS Backup: Centralized backup management
Amazon Macie: Data security and privacy service
Each service category mirrors functions historically performed by library departments: acquisition, cataloging, reference services, and preservation.
Microsoft Azure
Azure's information architecture emphasizes organizational knowledge management:
Information Creation and Collaboration
Azure Data Factory: Data integration service
Azure Synapse Analytics: Analytics service integrating big data and data warehousing
Azure Cognitive Services: AI capabilities for content understanding
Information Organization and Retrieval
Azure Cosmos DB: Globally distributed database service
Azure Cognitive Search: AI-powered search service
Azure Knowledge Mining: Content extraction, enrichment, and exploration
Information Governance and Compliance
Azure Purview: Data governance service
Azure Information Protection: Information classification and protection
Azure Sentinel: Security information and event management
Azure's integration with Microsoft 365 particularly reflects the evolution from document management systems to comprehensive information ecosystems—a journey that parallels the evolution from traditional library catalogs to integrated library systems.
Google Cloud Platform (GCP)
GCP leverages Google's information retrieval expertise:
Information Processing and Analysis
Cloud Dataflow: Stream and batch processing
BigQuery: Serverless, highly scalable data warehouse
Cloud Dataproc: Managed Hadoop and Spark service
Information Organization and Discovery
Cloud Storage: Object storage with extensive metadata capabilities
Cloud Spanner: Globally distributed relational database
Cloud Search: Enterprise search platform with natural language processing
Machine Learning and Knowledge Extraction
Vertex AI: Unified ML platform
Document AI: Document understanding and processing
Natural Language API: Text analysis and understanding
GCP's heritage in search technology particularly reflects information retrieval science principles, with services designed to extract meaning and relationships from unstructured information.
Information Science Principles in Cloud Architecture
Beyond specific services, cloud architectures implement core information science concepts:
Collection Development Theory → Resource Provisioning Models
Just-in-time acquisition → Auto-scaling resources
Collection assessment → Resource utilization monitoring
Deselection policies → Resource decommissioning automation
Information Organization Principles → Tagging and Resource Management
Classification schemes → Resource tagging taxonomies
Authority control → Naming conventions and standards
Subject headings → Resource metadata standards
Access Management Frameworks → Identity and Access Management (IAM)
Circulation policies → Access policies and permissions
Patron records → Identity management
Usage agreements → Terms of service and access conditions
Data Architecture Through an Information Science Lens
Data Lakes: Modern Special Collections
Data lakes represent the evolution of special collections in traditional libraries—repositories of diverse, often unprocessed materials requiring specialized access and management approaches:
Think of a library's Special Collections as a parallel to a Data Lake. When a library acquires a rare manuscript for its special collection, it doesn't immediately dissect and categorize every word. Instead, it's kept in its original form. Similarly, a data lake ingests raw data without immediate transformation, meaning data from various sources (like websites, sensors, or applications) is dumped in as-is, without being cleaned or structured upfront. Just as a special collection houses materials in their original formats – old books, maps, recordings – a data lake stores data in its original formats like JSON, CSV, or images, without forcing everything into a uniform structure.
Finding specific information in a special collection often involves using finding aids, which are less structured guides describing the collection's contents rather than detailed catalog entries for each item. This mirrors how data lakes utilize metadata catalogs and data discovery tools. These tools help users understand what data exists and its basic characteristics without a rigid database schema. Finally, access to special collections is usually mediated, requiring permission and careful handling due to the materials' unique nature. This aligns with the governed access through security policies in a data lake, where access to raw data is controlled to protect sensitive information and ensure appropriate use. In essence, both special collections and data lakes are repositories of diverse, less processed assets requiring specialized methods for discovery and controlled access.
Information science principles for managing special collections translate directly to data lake management:
Provenance documentation: Tracking data lineage and sourcing
Original order preservation: Maintaining data in its original structure
Minimal processing approaches: Storing raw data while creating sufficient metadata for discovery
Progressive arrangement: Refining organization as usage patterns emerge
Data Warehouses: Structured Knowledge Repositories
Data warehouses parallel reference collections in libraries—curated, structured collections organized for specific analytical purposes:
Subject organization → Dimensional modeling
Reference resource selection → ETL transformation processes
Ready-reference structures → Pre-aggregated measures
Citation verification → Data quality validation
The star schema common in data warehouse design conceptually resembles a faceted classification system, with dimensions representing facets through which information can be analyzed and measures representing the quantified properties of interest.
Data Mesh: Distributed Information Stewardship
The emerging data mesh architecture implements distributed stewardship concepts long practiced in library consortia:
Domain-specific collections → Domain-owned data products
Shared cataloging standards → Federated governance
Inter-institutional resource sharing → Self-serve data infrastructure
Collection development agreements → Distributed data ownership
This architecture acknowledges that information is most effectively managed by those closest to its creation and use—a principle established in library science through subject specialist roles and departmental libraries.
Data Science as Applied Information Science
Information Behavior Studies and Data Science
Data science methodologies reflect the evolution of information behavior research in library science:
User needs assessment → Requirement gathering and problem definition
Information-seeking behavior analysis → Exploratory data analysis
Information use studies → Model evaluation and impact assessment
Both disciplines seek to understand how information resources can be transformed into actionable knowledge, with data science applying computational methods to questions traditionally addressed through qualitative research in information science.
Knowledge Organization in Machine Learning
Machine learning systems implement knowledge organization principles:
Taxonomy development → Feature engineering
Classification schemes → Supervised learning algorithms
Thesaurus construction → Word embedding models
Authority records → Entity resolution systems
The process of training machine learning models parallels the development of controlled vocabularies—both attempt to create structured representations that capture meaningful patterns while accommodating ambiguity and evolution.
Scientific Data Management
Research data management, long a concern in information science, now influences data science practices:
Research documentation standards → Reproducible research protocols
Data curation workflows → ML operations (MLOps) pipelines
Long-term preservation planning → Model versioning and archiving
Metadata standards development → Feature documentation frameworks
These practices ensure that data science outputs—like library collections—remain discoverable, usable, and trustworthy over time.
Metadata Management in Enterprise Information Systems
Enterprise Metadata Repositories
Enterprise metadata management systems extend traditional catalog functions:
Descriptive metadata → Business glossaries and data dictionaries
Structural metadata → Data models and schemas
Administrative metadata → Ownership and stewardship records
Preservation metadata → Data lifecycle policies
These systems serve as organizational knowledge bases, providing context that transforms raw data into meaningful information resources.
Metadata Standards in Cross-Platform Environments
Just as libraries developed standards like MARC and Dublin Core, modern information ecosystems require cross-platform metadata standards:
Common Metadata Framework (AWS)
Azure Purview Data Catalog
Google Cloud Data Catalog
Cross-platform standards (DCMI, schema.org)
These standards facilitate discovery across information silos, enabling organizations to leverage diverse information resources regardless of physical location or technical implementation.
Semantic Enhancement Through Knowledge Graphs
Knowledge graphs represent the evolution of authority files and controlled vocabularies, establishing relationships between entities that enhance information retrieval and analysis:
Subject authority files → Domain ontologies
Name authority records → Entity resolution systems
See-also references → Semantic relationships
Classification hierarchies → Taxonomy structures
Cloud providers increasingly integrate knowledge graph capabilities:
AWS Neptune
Azure Cognitive Services Knowledge Mining
Google Knowledge Graph API
These services enable organizations to implement semantic approaches to information organization that extend traditional classification methods.
Information Governance in the Cloud Era
Data Governance as Collection Development Policy
Traditional collection development policies addressed questions still central to data governance:
What information should we acquire?
How should it be organized and maintained?
Who should have access and under what conditions?
When should information be archived or removed?
Modern data governance frameworks extend these considerations to digital information assets:
Data acquisition standards: Quality, relevance, and compatibility requirements
Data classification schemas: Sensitivity, criticality, and retention categories
Access control matrices: Role-based permissions aligned with organizational needs
Data lifecycle management: Retention, archiving, and deletion policies
Regulatory Compliance and Information Ethics
Information ethics frameworks developed in library science now inform regulatory compliance:
Intellectual freedom principles → Open data policies
Privacy protection practices → Data protection requirements (GDPR, CCPA)
Information equity concerns → Algorithmic fairness considerations
Professional responsibility standards → Data ethics frameworks
These ethical foundations provide context for compliance activities, ensuring that organizations understand not just what regulations require but why those requirements matter.
Information Risk Management
Risk management approaches from special collections and archives inform digital information protection:
Preservation risk assessment → Data loss prevention
Access security protocols → Identity governance
Collection disaster planning → Business continuity management
Theft and vandalism protection → Cybersecurity controls
These parallels highlight that protecting information value has been a core concern in information science long before digital threats emerged.
Integrated Analytics Platforms: Modern Reference Services
From Reference Desk to Business Intelligence
Reference services in libraries share conceptual foundations with business intelligence platforms:
Ready reference collections → Dashboards and visualization libraries
Reference interviews → Requirements gathering processes
Information literacy instruction → Data literacy training
Subject guides → Self-service analytics portals
Analytics platforms extend these services through:
AWS QuickSight
Microsoft Power BI
Google Data Studio
Third-party tools (Tableau, Looker, etc.)
These platforms transform the reference function from individual service interactions to scalable self-service resources while maintaining the core goal of connecting users with relevant information.
Knowledge Synthesis and Decision Support
Just as reference librarians synthesize information from multiple sources to answer complex questions, modern analytics platforms integrate diverse data sources for comprehensive analysis:
Literature reviews → Data integration processes
Annotated bibliographies → Curated datasets with documentation
Subject expertise → Domain-specific analytical models
Reference consultations → Data science advisory services
The evolution from isolated reports to integrated analytics environments parallels the development from standalone reference works to integrated digital libraries.
Conclusion: Towards Information-Centered Cloud Architecture
This examination reveals that cloud platforms, data architectures, and data science methodologies implement information science principles at unprecedented scale. Organizations that recognize these connections can:
Leverage established frameworks: Apply information organization principles proven effective over centuries of library practice
Enhance knowledge transfer: Bridge traditional information management and modern technical implementations
Develop integrated approaches: Address technical, organizational, and ethical dimensions of information management
Build sustainable systems: Create architectures that accommodate information growth and evolution
The most effective cloud and data architectures will be those that consciously implement information science principles—not merely storing and processing data but organizing it into meaningful, accessible, and trustworthy information resources that drive organizational value.
As we continue this journey from physical libraries to digital information ecosystems, the foundational principles of information science remain our most reliable guides—not as historical curiosities, but as practical frameworks for addressing the complex information challenges of our time.
DigiCompli holds credentials in Information Science with specialization in digital information systems. DigiCompli’s research focuses on the application of information organization principles to emerging digital marketing technologies and cloud architectures.
