?? Part 2: Connecting the Dots: A Summary of Data Architecture Evolution (1960-1980)
Recap of Part 1: Data Architecture - Beyond the Buzzword: Our preceding article delved into the essential principles of data architecture, highlighting its pivotal importance in today's data-centric landscape. We examined the significance of data quality, governance, integration, and security, and their collective impact on developing strong data architectures that propel innovation and uphold strategic goals. Reference the Article here. --> Data Architecture- Beyond the Buzzword
Executive Summary
This article provides a summarized overview of the evolution of Data Architecture from 1960 to 1980. It highlights significant technological developments, the evolving roles and responsibilities of data management teams, the rise of silos, and the enduring challenges that have remained. For a comprehensive understanding, please refer to the detailed article here. --> Part 2.1 : 1960 to 1980 - The Dawn of Computer Systems and the Birth of Data Architecture
Connecting the Dots: A Summary of Data Architecture Evolution (1960-1980)
During the 1960s to 1980s, data architecture evolved significantly, laying the foundation for modern data management practices. This period saw the development of Operational Systems designed for day-to-day business operations, and Decision Support Systems (DSS) aimed at aiding decision-making. Operational Systems, such as IBM’s System/360, handled large-scale transaction processing, while DSS leveraged relational databases introduced by Edgar F. Codd to support complex queries.
?? Operational Systems and DSS: Different Worlds
Operational Systems: Designed to handle day-to-day business operations, each system had its own data model, processes, and usage patterns tailored to specific business needs. For example, in the 1960s, mainframes like IBM’s System/360 were the backbone of these systems, handling large-scale transaction processing with hierarchical databases such as IBM’s IMS.
Decision Support Systems (DSS): Developed to aid in decision-making, DSS required a different approach to data storage and processing. Emerging in the 1970s, DSS leveraged relational databases introduced by Edgar F. Codd. These systems were designed to support complex queries and decision-making processes.
??? Creating a Holistic Data Architecture
To bridge the gap between Operational Systems and DSS, a holistic data architecture was necessary. This architecture started with understanding the data sets, processes, data models, and data life cycles within Operational Systems. These systems produced, enriched, and consumed data sets to support business operations.
DSS, as consumers and enrichers of operational data, required a different approach to store and process this data. This involved modeling and mapping data from source systems to meet decision-making and insight requirements.
?? Data Integration and Batch Transfers
Data integration was crucial to connect Operational Systems and DSS. Initially, this was achieved through batch transfers, which evolved and advanced over the years. Data integration relied heavily on batch processing, with jobs scheduled to run during off-peak hours to minimize impact on operational systems. Data was often transferred using magnetic tapes in the 1960s, transitioning to disk storage in the 1970s for faster access.
The data extraction process involved pulling data from source systems, while the ingestion process loaded this data into DSS based on user-defined data models.
?? Data Transformation
Data transformation was a critical step in the ETL process. Common and complex transformations included:
?? Integrating Multiple Source Systems
Integrating data from multiple source systems into DSS involved several steps:
??? Data Modeling in DSS
Building the data model in DSS was a multi-phase process:
The relational model introduced in the 1970s revolutionized data modeling, allowing for more flexible and efficient data structures. Peter Chen’s Entity-Relationship Model in 1976 provided a graphical way to design databases, enhancing the conceptual and logical phases of data modeling.
?? Data Integration for Business Decisions
Once data was stored in DSS, it needed to be integrated to support business decisions. This often involved answering complex questions that required data from multiple operational systems or application categories.
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?? Analytics and Consumption Framework
After populating DSS, analytics and consumption systems retrieved data using various applications. The data architecture team had to design and build a consumption framework to facilitate data retrieval from DSS. SQL became the standard for querying relational databases, while COBOL remained prevalent for data retrieval and report generation. The introduction of early spreadsheet software like VisiCalc in 1979 marked the beginning of spreadsheet analytics, allowing users to perform basic data analysis and visualization.
??? Pipelines Auditing and Monitoring
To ensure data correctness, pipelines auditing and monitoring frameworks were developed. These frameworks monitored data processes and ensured the integrity of data pipelines. Auditing involved reviewing logs and output reports to ensure data was correctly processed and transferred. Monitoring was often manual, with operators checking the status of batch jobs and verifying data integrity through sample checks and validation routines.
?? Implementing Data Quality
Data quality was implemented within the pipelines. This involved capturing exceptions and handling them based on a Data Quality Management Remediation process. Validation checks were embedded in the batch processing scripts and programs to ensure data accuracy and consistency. Exceptions were documented and handled manually, following established remediation processes.
?? Data Lineage
Data lineage was established to track which objects were changed by which pipelines. This was crucial for further enhancements and maintaining data integrity. Data lineage was tracked manually through documentation, with changes to data objects and processes recorded in logs and manuals. Historical data was often archived on magnetic tapes, providing a form of data lineage by preserving snapshots of data at different points in time.
?? Data Governance and Security
Data governance aspects included defining who could access, own, approve, and make changes to respective data sets. This supported data security through authentication and authorization mechanisms. Formal data governance practices were limited, but efforts were made to define data ownership and access controls.
?? Data Organizational Structure and Responsibilities (1960-1980)
During this period, organizational structures often led to silos, causing transparency and alignment issues. The Chief Information Officer (CIO) oversaw all IT and data management activities, reporting directly to the CEO. Vice Presidents (VPs) or Directors of Operational Systems managed specific operational systems, ensuring they met business needs.
Operational Systems Management Teams included Systems Analysts, Programmers, Operators, and Database Administrators (DBAs), each responsible for different aspects of system management. The Director of DSS managed DSS applications, supported by DSS Analysts, Developers, and Data Modelers. The Analytics Systems Management Team, led by the Analytics Manager, used data from DSS to generate insights and reports.
?? The Birth of Silos and Organizational Debt in Data Architecture
The introduction of various data architecture components during this period inadvertently led to the formation of silos and the accumulation of organizational debt. Departments developed their own systems and databases to meet specific needs, leading to isolated data silos. These silos protected sensitive information, specialized functions, and assigned accountability but also caused inefficiencies and limited data accessibility.
Organizational debt emerged in terms of data, process, and technology. Data debt included inconsistent data models, redundancy, and accessibility issues. Process debt involved inefficient processes, lack of standardization, and delayed decision-making. Technology debt arose from legacy systems, high maintenance costs, and limited scalability.
?? The Challenges: Past to Present and Ongoing from 1960 to 1980
The challenges introduced during this period have persisted and evolved over time. Legacy systems continue to pose integration challenges, and data silos hinder accessibility and collaboration. Inconsistent data models and high maintenance costs remain issues, while data governance requires continuous effort.
Ongoing challenges include data privacy and compliance, data quality management, scalability issues, and integrating emerging technologies. Data security, change management, cost management, and talent shortage are also significant concerns. Migrating data from legacy systems to modern platforms is complex and resource-intensive, requiring careful planning to ensure data integrity and minimize downtime.
?? Conclusion
While modern data architecture practices can address many historical challenges, continuous effort, investment in technology, and fostering a data-driven culture are essential. It’s a journey rather than a destination, and organizations must remain adaptable and proactive in their approach to data management.
?? Call to Action
I hope you found this exploration of data architecture from 1960 to 1980 insightful. Whether you’re a data professional, a technology enthusiast, or someone interested in the history of data management, there’s something here for everyone. For a more detailed exploration, please refer to the detailed article here. --> Part 2.1 : 1960 to 1980 - The Dawn of Computer Systems and the Birth of Data Architecture
If you have any thoughts, questions, or experiences to share, please leave a comment below. Let’s continue the conversation and learn from each other’s insights. Don’t forget to follow me for more articles on data architecture and related topics. Thank you for reading!
Stay tuned for our next article, where we will explore the evolution of data systems from 1980 to 2000. We’ll dive into new findings, technologies, frameworks, and developments that shaped the data landscape during that period.
Regards,
Mohan