Generative AI is a branch of artificial intelligence that focuses on creating new content or data from scratch, such as images, text, music, code, diagrams, etc. Generative AI has been making impressive advances in recent years, thanks to the development of deep learning models such as generative adversarial networks (GANs), autoencoders, transformers, and others. Originally published at LinkedIn Pulse on 8 January 2024. Photo by Google DeepMind @ Pexels.com Generative AI ha

In the modern business landscape, the utilisation of data and analytics has transcended its conventional role of providing retrospective insights and has emerged as a catalytic force for driving meaningful impact across organisations. This article explores the concept of transformative analytics, delving into its significance, challenges, and strategies while shedding light on real-world instances of organisations that have successfully harnessed analytics to bring about prof

Multiple recent recruitment surveys revealed that the demand for data engineers has recently exceeded the previous demand for data scientists. The Dice 2020 Tech Job Report said data engineer was the fastest growing job in technology with a 50% year-over-year growth in the number of open positions. Many mentees asked me how do I perceive this shifts in demand, and what area should they pursue. Actually, I see a course correction happening in the industry. Most organisations t

As CDAO Melbourne marks its 10th anniversary with the 2024 conference, it’s an opportune moment to reflect on a decade of transformative evolution in Melbourne’s data and analytics landscape. As a regular participant, I have witnessed firsthand the remarkable progress. This article analyses my observations over the years, aiming to assess the evolution, identify the challenges for large-scale adoption, and develop a practical strategy to address the identified issues. A refle

Imagine a world where information and opportunities are not just a click away but are rather confined to certain demographics and regions. Sadly, such a world existed well before the late 20th century when the term 'digital divide' was coined. It served as a stark reminder of the gaping chasm that separated those who had the privilege of accessing modern information and communications technology from those who were left behind, either with no access or restricted access. Orig

With the advent of the data science practises, especially the likes of Artificial Intelligence (AI) or machine learning and their adaptation in sensitive areas, has drawn massive attention to this technology in the recent years. Whether it is cancer diagnosing system, Amazon’s AI recruiting tool or the US court’s profiling system Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), we can always disagree to the decision made by the AI systems. In fac

Artificial Intelligence (AI), or more precisely Machine Learning (ML), has become an industry trend in the past 10 years. From a buzzword to a new way of automation and decision-making, ML has become mainstream. I have had conversations with multiple organisations keen to use ML, even without a real-world use case. Graduate schools are offering lightweight courses to the masses, consulting companies are providing services on adopting ML, and technology companies are building

Most corporate structure consists of various departments that contribute to the company’s overall mission and goals. Common departments include Marketing, Finance, Operations, often collectively referred to as Business, Human Resources, and Information Technology (IT). These five, or three, divisions represent the major departments within a publicly traded company, though there are often smaller departments within autonomous firms. With recent interest in Artificial Intellige

Defining success is a crucial part of managing a data science experiment. Of course, success is context-specific. However, some aspects of success are general enough to merit discussion. A list of hallmarks of success includes: New knowledge is created. Decisions or policies are made based on the outcome of the experiment. A report, presentation, or app with impact is created. It is learned that the data cannot answer the question being asked of it. Some more negative outcome

We’ve all heard the phrase “data is the new oil”, and companies are capturing immense amounts of it. Despite this, a surprising number still struggle to realise its full potential, not because they lack data, but because they fail to move up the data value chain strategically. This journey from raw information to tangible business value is not a single leap, but a progressive and methodical ascent. It can be conceptualised as a climb up a value chain, with each step offering