Dewi Lestari, Nurul Hidayah, Fitri Handayani, 2026. "Data Alchemy: Transforming Raw Data into Autonomous Knowledge Assets", Journal of Machine Learning and Computational Intelligence (JMLCI) 1(1): 73-89.

Over the last two decades, digital technologies have had a complex and exponential impact on society, creating proverbial data everywhere at unparalleled volume, variety and velocity across organizations, industries and intelligent systems. Although data is now one of the most valuable assets in today's digital economy, as pickled/ raw it lacks structures, often inconsistently gathered, redundant and mostly not having too much contextual meaning – thus it lacks real value. Data Alchemy is a transformative approach to enterprise data where raw data become selfsustaining knowledge assets with the ability to generate actionable insights, informing intelligent decision making and organizational innovation. Just as ancient alchemist made lead into gold through the systematic application of arcane techniques, Data Alchemy uses the latest analytical techniques, artificial intelligence, machine learning and semantic technologies within knowledge engineering frameworks to transform data which is not connected together into useful knowledge resources that can evolve on its own. The advancement of machine learning and natural language processing has made automating knowledge creation and management a key driver for using data-driven intelligence as a way to create competitive advantage and achieve sustainable growth.
Hosted on an AI Autonomy Vault, we systematically investigate this literacy and unpack the novel paradigm of Data Alchemy that emerges as practitioners use modern computational technologies to extract new knowledge from raw data and turn it into autonomous knowledge assets. The present study investigates the whole life cycle of knowledge creation: data acquisition, integration, semantic enrichment (and subsequently), intelligent transformation to extract knowledge and finally machine learning-based processes for autonomous reasoning. It discusses the importance of knowledge graphs, semantic intelligence, context-aware analytics and edge-cloud computing design patterns in ensuring continuous generation and personalized use of knowledge. The research analyzes the role of Generative Artificial Intelligence, cognitive agents, autonomous learning systems and explainable intelligence frameworks in creating self-managing knowledge ecosystems that evolve with environmental changes and information needs.
The paper also discusses a critical data quality and trustworthiness challenges in knowledge systems for autonomy (governance, interoperability, privacy and being the lack of ethical consideration). We explore novel techniques for explainable knowledge generation, trustworthy AI and responsible data governance as critical tools to provide transparency and trustworthiness. These results show that Data Alchemy is a strong foundation for converting data into strategic knowledge-producing properties with the ability to continually learn, change and generate value. Providing innovative and autonomous knowledge ecosystems through self thinking machines by fusing computational intelligence and semantic knowledge management that empowers organizations with growth, decision making and intelligent digital transformation across application areas.

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