In many organisations, Chief Data and Analytics Officers (CDAOs) find their roles confined to traditional data management tasks, mirroring the responsibilities typically associated with Chief Data Officers (CDOs). Their focus often centres on governance, compliance, and platform investments — essential areas, but ones that risk overshadowing their ability to deliver strategic business value through analytics. Originally published at LinkedIn Pulse on 13 April 2025. Photo by

Vibe coding is an emerging paradigm in software development where developers express high-level intent in natural language, and AI assistants generate the underlying code. It flips the developer’s role from coder to orchestrator, with a focus on reviewing, testing, and tightening what the machine produces. In the right context, this is a game-changer. It slashes time spent on boilerplate, accelerates prototyping, and lets non-specialists turn ideas into working models faster

In 2023, the field of generative artificial intelligence (AI) experienced remarkable progress, with significant advancements in natural language processing, computer vision, and audio synthesis. These developments have unlocked a plethora of new opportunities and applications, shaping the future trajectory of technology across a wide range of sectors. This article provides an overview of the ethical considerations and responsible strategies for the development, deployment, an

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

A Large Language Model (LLM) is a notable AI system, powered by neural networks, designed to comprehend and generate human-like text from vast amounts of training data. These models are like language virtuosos, capable of matching context, generating coherent text, and answering questions in a way that seems human. They have gained significant attention due to their potential applications in chatbots, content generation, language translation, and information retrieval. Origin

Once businesses have started collecting and combining all kinds of data, the next elusive step is to extract value from it. Collected data may hold tremendous potential, but no value can be created unless insights are uncovered and translated into actions or business outcomes. Originally published at LinkedIn Pulse on 23 December 2019. Photo credit: www.pexels.com Data storytelling is the process of translating data analyses into layman’s terms to influence business decision

I have seen many articles on types of analysis. Most of these articles discuss four types: descriptive, exploratory, diagnostic (usually either exploratory or diagnostic!), predictive, and prescriptive analysis. But throughout my early career, I’ve personally experienced eight types of analysis. I encourage all aspiring data scientists to be familiarised with all these types of analysis and, if necessary, apply them. Originally published at LinkedIn Pulse on 22 December 2019

Generative AI (GenAI), especially LLMs, has transitioned from a buzzword to an essential business asset. Leading organisations have moved beyond the proof-of-concept (POC) phase and are now actively deploying GenAI solutions. Many non-technology companies have realised that developing proprietary LLMs is unnecessary for solving internal challenges . Instead, they acquire LLMs from vendors or the open-source marketplace. Originally published at LinkedIn Pulse on 23 March 2025

The buzz around AI often overshadows a critical reality: over 85% of employees anticipate that AI will impact their jobs within the next 2-3 years, leading to a mix of excitement and apprehension. There’s a notable disconnect between leadership’s perception of readiness and the reality on the ground, with C-suite leaders more likely to cite a lack of employee readiness as a barrier, even as employees are already adopting generative AI tools at a higher rate than expected. Thi

The transformative power of Artificial Intelligence (AI) is evident, but its full potential is often left untapped. This isn’t due to technical challenges, but rather a sizable communication gap between technical teams and senior leaders. For mid-to-senior-level leaders, knowing how to communicate effectively about AI isn’t optional; it’s crucial for driving business growth and successfully implementing AI. Originally published at LinkedIn Pulse on 30 November 2025. Effectiv

In today’s data-driven landscape, CDO, CAO, and CDAO are critical in steering organisations towards success. The journey began with the CDO, who introduced a data-centric culture by developing robust data strategies, ensuring data governance, and effectively managing data assets. As the volume and complexity of data grew, the need for specialised analytics expertise became apparent, leading to the creation of the CAO role. The CAO focused on transforming raw data into actiona

Over the past two decades, I’ve used analytics to support decision-making across nearly every part of enterprises. Yet, I’ve never once built a dashboard. They’ve consistently failed to deliver the insight and agility needed for real-world decisions. Dashboards demand a fixed set of questions and perfectly cleaned data up front, take months to build, and quickly become obsolete. In practice, they trap insight in rigid charts, leaving users to scroll through stale reports inst

Artificial Intelligence (AI) is advancing rapidly, and agentic AI is emerging as a transformative innovation. This approach enables systems to tackle complex, multi-step challenges autonomously. Generative AI (GenAI) has laid the groundwork by demonstrating the ability to understand and generate human-like text, code, images, audio, and video based on vast datasets. These foundational models are crucial for agentic AI, providing capabilities for understanding and generating n

In the wake of the advent of Large Language Models (LLMs), a multitude of initiatives have been undertaken to enhance user experiences via Natural Language Processing (NLP). A vast array of articles has been penned to encourage organisations to embrace generative artificial intelligence (AI). These pieces, however, have predominantly focused on the impressive ability of LLMs to understand user inquiries and generate responses, and often criticise the fact that they overlook f

Hallucination in Large Language Models (LLMs) refers to the generation of content that may deviate from factual accuracy or the provided source content. While these instances may occur, it’s crucial to recognise that LLMs are primarily designed for general-purpose language understanding and generation, not for delivering absolute precision. Originally published at LinkedIn Pulse on 7 January 2024. Photo by Andrea Piacquadio @ Pexels.com Consider an example where an image gen

Synthetic data has become a buzzworthy topic in recent times, offering a glimmer of hope for addressing the challenge of limited high-quality data for training AI and ML models. The other day, an enthusiastic salesperson came to me with a pitch for a product that claimed to generate synthetic data. Now, don’t get me wrong, AI and ML models are undoubtedly going to shape the future of work. However, I have some reservations about relying solely on synthetic data to build these

When I embarked on my first data role over a decade ago, my organisation was supported by a team of 30 business analysts responsible for generating insight reports by extracting a month’s worth of transaction data from an Oracle database, conducting analysis, creating visualisations, and documenting observed trends. They used a custom web interface with different parameters to extract the data from the database. Unfortunately, this entire process, from start to finish, took a

In today’s highly competitive environment, the rush to harness the transformative power of Artificial Intelligence (AI) is apparent. However, beneath the surface of many organisations, there is a subtle, often hidden, threat: Shadow AI. This isn’t the stuff of sci-fi thrillers; it involves the widespread use of AI tools and initiatives by individual teams or departments, such as marketing, HR, or customer service, without central oversight, approval, or understanding from IT

The honeymoon phase is over. What comes next will reshape every company on earth. There is a quiet revolution underway inside enterprise technology—and it bears no resemblance to the breathless headlines about artificial general intelligence or trillion-parameter models. It looks like budget reviews. It looks like an infrastructure audit. It looks like boards are demanding to know why AI investments are producing inconsistent returns at unsustainable cost. The era of deployin

To drive value from data, analytics need to be operationalised. Ad-hoc data exploration can be unclear, depends on each individual functional team’s preferences to generate insight, acted on and then forgotten under the pile of documentation. The preference may vary from the way the experiments are conducted, tools being used, and the finding has been disseminated. Every project starts from scratch. And if someone with a vast knowledge leaves the organisation, the prior knowl