In my experience, It’s easy to be captivated and distracted by cutting-edge data technologies such as the latest data streaming offering or the plethora of available AI options, but much of the value of a well defined data architecture lies in its foundational data capabilities like enterprise data cataloging. An often overlooked capability, enterprise data cataloging is a vital cog in any data architecture.

Talking about data-driven CX is a bit like talking about reversing backwards – if you’re reversing, you’re already going backwards. The same is true for CX; if you’re talking about CX, it should be, by its very nature, data driven.

Compliance with POPIA should not scare us. 😊 The heart of this legislation is to ensure that those organisations that use data, do so responsibly and transparently. Plus, as a marketer, we should respect our customers’ data in the same way we would expect ours to be respected.

The ever-increasing volume, velocity, and variety of data presents both challenges and opportunities. To navigate this complex landscape and unlock the power of data for competitive advantage, financial institutions are increasingly establishing dedicated data leadership roles. Three critical roles are the Chief Data Officer (CDO), Chief Analytics Officer (CAO), and Chief Artificial Intelligence Officer (CAIO). As financial institutions strive for peak performance, a pivotal question arises - can, and should, these roles be unified into a single, cohesive position?

In the digital age, data has become the lifeblood of organizations. It drives decision-making, shapes strategies, and can be the difference between success and failure. However, the value of data is intrinsically linked to its quality. High-quality data can lead to insightful decisions and successful outcomes, while poor-data quality can lead to misguided strategies and negative business outcomes.

In today's digital age, data serves as the lifeblood of organizations, driving decision-making, strategies, innovation, and competitive advantage. However, amidst the vast sea of data lies a dilemma that organizations must navigate: data quality. Ensuring the accuracy, completeness, and reliability of data poses a significant challenge, yet it is essential for organizations to extract meaningful insights and maintain trust in their data-driven initiatives. “Data’s Dilemma: Navigating Data Quality and the Tools that Shape Our Reality” explores the intricacies of data quality management and the innovative tools and technologies that play a crucial role in shaping our data-driven reality. From understanding the challenges of poor data quality to harnessing the power of data quality tools, this exploration aims to equip organizations with the knowledge and strategies needed to navigate the complex landscape of data quality and unlock the full potential of their data assets.

Generative AI (GenAI) is revolutionising how we tackle some of society’s most pressing issues, extending its influence far beyond the traditional boundaries of technology. This innovative field pioneer’s advancement in data creation emulates human creativity and intelligence. This article explores three pivotal case studies from the past five years, illustrating the profound impact of GenAI on everyday life. Additionally, it explores some key considerations that businesses across industries should note when pursuing a happy medium in applying all things AI.

Explore the evolution of data management in 'Navigating the Data Revolution: From Legacy Tools to Cloud and AI.' This article provides an overview of the transitions from traditional legacy tools to the current landscape of cloud computing and artificial intelligence. Gain insights into the challenges faced by organizations, the integration of big data, and the adoption of advanced technologies.

In the intricate world of finance, where every decision carries significant weight and every piece of information holds potential, the art of credit risk prediction is akin to a delicate dance. It's a world where the balance between raw and modelled data can mean the difference between success and failure.

Book recommendations on where marketing meets data from thought leaders in the industry.

How Uber Eats encourages experimentation to maintain an accelerated growth path.

Understand the power of data in shaping the way businesses connect with customers.

Working with so many younger professionals in our domain, I wonder how well we are doing in giving them opportunities to practice their skills in communication. To take risks, propose ideas and engage with stakeholders. What can we do to develop the soft skills of young analytics talent to ensure we get the best out of their technical skills?

The enchanting philosophy of breaking the fourth wall emphasizes an open dialogue, fluid collaboration, and an unbreakable bond between various stakeholders. It's about bringing everyone to the center stage, and transforming them from mere spectators into active participants

After a few wild weeks in retail, Black Friday mania is finally floating into the abyss, and while the festive spending frenzy is still in full swing, many of us in retail and e-commerce have crept far enough out of the catatonic Black Friday panic to be able to reflect on what the period actually means, not only for businesses, but for their consumers.

The role of a well-structured data ecosystem in project finance, especially when it comes to funding large-scale infrastructure projects, can prove to be critical.

So, in a world of data, decisions, and endless spreadsheets, decision scientists are among the unsung heroes who turn numbers into knowledge, chaos into clarity and coffee into insights. They’re the wizards behind the curtains of the data-driven world, armed with Python spells and excel wands.

One of the main benefits of adopting good data governance practices is the production of high-quality data, which in turn, are extremely important for the performance and accuracy of AI models used for decision making.

Much like F1 where one could say there is a fairly simplistic goal: "drive a car, win a race," building a data strategy is simple—mention "data-driven" a few times and use fancy jargon related to identifying, storing, provisioning, integrating, and governing data, right? Wrong. Just as an F1 car and team need a driver to win the race, your strategy and execution plan, or tactics, need people to execute them. It is the synergy and interaction of the three components that win the race—the people you hire in your data team, the data strategy you build aligned with your business objectives, and the tactics you create to achieve your strategy.