The Future of Data: From Drucker's Vision to Ontology-Driven AI
The Dawn of a New Civilization
At the turn of the century, management guru Peter Drucker made a prescient observation: the introduction of computers had set humanity on a path towards a new basic civilization. Drucker envisioned a future of hyper-connectivity, where information would flow freely, and knowledge workers would thrive. He predicted that the paradigms inherited from the past would give way to new frameworks of norms and models of commerce yet to be conceived.
At the time, Drucker's vision seemed far-fetched. The internet was in its infancy, cloud computing was non-existent, and concepts like the Internet of Things (IoT) and platform businesses as we know it today were still in the realm of science fiction. Yet, with the benefit of hindsight, we can now see the profound accuracy of his foresight. The journey to this new civilization has indeed been propelled by relentless technological advancement.
Four Motifs of Technological Progress
The evolution of automation and digital technology can be distilled into four fundamental themes: computation & communication, abstraction, storage, and integration. Each of these areas has seen exponential progress over the past seven decades.
Computation & Communication: The Engine of Progress
The rapid growth in computing power is the foundation of our digital world. Powerful personal devices and extensive supercomputing networks are the new normal, which have transformed our ability to process and analyze large datasets quickly. Tasks that once took days can now be done in minutes or seconds, enabling insights to be uncovered through machines that can “learn”!
Additionally, advancements in communication technologies, such as fiber optics and 5G, facilitate real-time data processing and integrated cloud solutions that deliver insights from millions of data sources worldwide. This combination of computation and communication drives innovation, paving the way for the next generation of data-driven technologies.
Abstraction: Simplifying Complexity
Abstraction has been the driving force in software development, liberating programmers from the constraints of hardware. From the early days of assembly language replacing machine code, through the evolution of high-level programming languages, to modern paradigms like Functional Programming, no-code-lo-code and object-based platform composition, abstraction has continually raised the level at which developers can work.
This principle extends beyond programming languages. Database systems have evolved from simple sequential files to sophisticated relational and NoSQL databases - and Big Data. Each step in this evolution has abstracted away complexities, allowing developers to focus on higher-level concerns.
As Google's Quentin Hardy notes, software has taken "abstraction and modelling of things and experiences to an entirely new level." This has given rise to concepts like APIs, frameworks, microservices, and metadata, each representing a new layer of abstraction that simplifies complex systems.
Abstraction has played a crucial role in managing the growing complexity of data systems. Data modeling languages, ETL tools, and frameworks like Apache Spark have abstracted away many of the low-level details of data processing, allowing Data Engineers to focus on higher-level data architecture and pipeline design. This trend towards higher levels of abstraction is paving the way for the next generation of Data Engineering tools, which will leverage ontologies and AI to further simplify complex data operations.
Storage: The Foundation of the Data Age
The progress in data storage technologies has been nothing short of revolutionary. From punch cards to magnetic tapes, to today's advanced storage-class memory and high-performance object storage, our ability to store and access data has grown exponentially.
This growth in storage capabilities has been matched by an explosive increase in the volume of data being generated and stored. In 1986, the world's total data storage capacity was less than 3 exabytes. By 2022, it exceeded 10,000 exabytes, with the total amount of data that is created and replicated annually projected to reach 181 zettabytes (or 181,000 exabytes) by 2025. This explosive growth in data volume requires new approaches to data storage, processing, and analysis, and as such it is a key driver in the evolution of Data Engineering.
Integration: Connecting the Digital World
Of all the technological advancements, integration has perhaps had the most profound impact. Networking technologies have built upon the breakthroughs in abstraction and storage, enabling infinitely scalable data processing and access.
Integration has connected data in ways previously unimaginable. By 2007, more than half of the world's digital data was stored with an IP address, making it accessible and connectable. This connectivity has enabled the identification of patterns and the extraction of knowledge at an unprecedented scale.
The Rise of Data Engineering
As these technological themes have converged and matured, they've given rise to the field of Data Engineering. Data Engineers have become crucial in designing and maintaining the complex data ecosystems that power our digital world.
However, as we stand on the cusp of a new era in Data Engineering, we face both exciting possibilities and significant challenges. The exponential growth in data volume, variety, and velocity has pushed traditional data management approaches to their limits. The need for more intelligent, automated, and business-aligned data systems has never been more pressing.
The Next Frontier: Ontology-Driven AI producing Actionable Business Knowledge
As we look to the future, the integration of ontologies and artificial intelligence presents a transformative opportunity for data practitioners (or “Knowledge Workers” as Drucker referred to them), building on the progress made in abstraction and integration, taking them to new heights.
Bizcloud's ontology-first approach, centered around the MONJO ontology, positions it at the forefront of this revolution. By providing a comprehensive business ontology and the tools to leverage it, Bizcloud offers a foundation for building the next generation of intelligent, knowledge-aware data systems.
As AI capabilities are integrated into this framework, we can expect to see advanced features such as AI-assisted application composition, intelligent data pipelines, and proactive analytics and insights.
Drucker's Vision Realized?
At this point in the evolution of abstraction, we can clearly see the realization of Peter Drucker's vision: a new civilization built on knowledge and information. The combination of ontologies and AI will be the key catalyst in propelling us towards this future, providing a rich, semantic foundation for data operations and leveraging AI to automate and optimize these operations. This foundation will enable us to create data systems that are not just more efficient, but truly intelligent and business-aligned. It will unlock unprecedented levels of insight and value from our ever-growing data resources, driving innovation and transformation across industries.
We are not merely adapting to the future Drucker envisioned; data practitioners are actively shaping it, creating the tools and frameworks that will define our new, knowledge-driven civilization.
Hi! I'm Jan Posthumus, co-founder of Bizcloud - an open-source framework revolutionizing how platform businesses are built. Together with Franco Benedetti, we're transforming complex platform development into elegant simplicity. We're building a vibrant community of technologists, architects, and innovators around our framework. Whether you're a seasoned developer, data practitioner, business strategist, or just curious about the future of platform development, we'd love you to explore what we're about. If our vision resonates, join us in reshaping how business platforms are created.
Interesting perspective on this topic—you've given me some fresh ideas to think about.
Great balance of technical depth and practical application. Really valuable read.