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Data Science is an exciting field for knowledge workers because it increasingly intersects with the future of how industries, society, governance and policy will function. While it’s one of those vague terms thrown around a lot for students, it’s actually fairly simple to define.
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is thus related to an explosion of Big Data and optimizing it for human progress, machine learning and AI systems.
I’m not an expert in the field by any means, just a futurist analyst, and what I see is an explosion in data science jobs globally and new talent getting into the field, people who will build the companies of tomorrow. Many of those jobs will actually be in companies that do not exist yet in South and South-East Asia and China.
Data science is thus where science meets an AI, a holy grail for career aspirants and students alike. Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills.
This article will attempt to outline a brief overview of some of what’s going on and is by no means exhaustive or a final take on the topic. It’s also going to focus more on policy futurism rather than technical aspects of data science, since those are readily covered in our other articles on an on-going basis.
Augmented Data Management
In a future AI-human hybrid workforce, how people deal with data will be more integrated. Gartner sees this as a pervasive trend. For example, augmented data management uses ML and AI techniques to optimize and improve operations. It also converts metadata from being used in auditing, lineage and reporting to powerful dynamic systems.
Essentially augmented data management will enable active metadata to simplify and consolidate architectures and also increase automation in redundant data management tasks. As Big Data optimization takes place, automation will become more possible in several human fields, reducing task loads and creating AI-human architectures of human activity.
Hybrid Forms of Automation