Generative AI refers to an artificial intelligence (AI) paradigm with the capability of generating novel content, encompassing textual, visual, or auditory elements. It falls within the domain of machine learning (ML), a discipline that employs algorithms to extract insights from existing data and subsequently generate novel data bearing similarity to the source material. The origins of generative AI trace back to the 1950s, marking its inception during the nascent stages of ML.

Women constitute much of the South African population, however, that is not reflected in the percentage of female representation in the STEM (Science, Technology, Engineering and Mathematics) field, nor in the representation of woman leaders within the technology sector. I conducted research in the past to gather information pertaining to what makes a woman-leader successful in this industry, and it was through the application of those findings that I got to this point in my career. Like most people within the technology field, I started my career as a specialist, and it took me a good eleven years before progressing to my very first leadership role. My journey was nothing short of educational and today I will be sharing some of the eye-opening findings, from an expedition of a women navigating the leadership labyrinth of the tech world. Let’s go straight into the key lessons that made a difference in my career.

People really seem to be wired to connect and feel. Which is what I believe makes humans “human” and, furthermore, successful. However, desensitisation can lead us to accept our and other people’s fate whether it is acceptable or not, therefore almost normalising bad behaviour. From my perspective, when you employ data with empathy, you are skillfully merging the logical aspects on the one side, and the emotional aspects on the other, by enabling data to serve as a bridge between them.

The organisational use of artificial intelligence (AI) has rapidly spread across various sectors. The recent developments in Large Language Models such as ChatGPT have caused an even more exponential rise in the use of AI, capturing the imagination of people around the world, thanks to their broader utility- almost anyone can use them to communicate and create solutions with ease. Alongside the recognition of the benefits brought by AI, there is a growing consensus on the necessity of tackling the risks and potential harms, such as bias and discrimination, brought about by advanced AI technologies. Implications of these technologies can reach far beyond the realms of the organisation to have a wider societal impact. A multitude of AI ethics principles have been proposed to tackle these risks, but the outlines of organisational processes and practices for ensuring socially responsible AI development are largely lacking.

Bias in AI models refers to the systematic favouring or discrimination against certain groups, often due to skewed training data or inherent human biases. These biases can be manifested in various ways, such as racial, gender, or socioeconomic bias, leading to unjust outcomes and reinforcing existing disparities.

Two aspects clearly emerge: data mining cannot continue unchecked and ‘for public benefit’ is a concept that must be unpacked. What we do know is that technological advancements will raise many questions, challenge conformed traditions of legal protection, stretch the boundaries of creativity and create loopholes for exploitation. Which side of the AI battle will citizens and society find themselves?

Investment banking has been dramatically influenced by the onset of Artificial Intelligence (AI). From a time when bankers relied on human cognition, intuition, and traditional business methods, we have progressed to an era where algorithms can now process and interpret vast amounts of financial data at speed and scale.

Striking the right balance between technological advancement and ethical responsibility is critical in shaping a future where AI benefits all of humanity, especially in the context of Africa. Ethical AI development and governance are essential for fostering trust, ensuring fairness, and promoting social cohesion in an AI-driven society. As we continue to harness AI’s potential, let us prioritize the ethical principles that uphold human values, respect individual rights, and promote the collective well-being of our global community. Conscious creation is required.

Metrics that matter extend beyond charts and graphs, they are transformational and represent actionable insights and informed decisions that can propel a business forward. To achieve this value, data and analytics leaders must acknowledge the significance of both qualitative and quantitative data, align these metrics to strategic objectives and embrace the power of storytelling, integrating data into compelling narratives that resonate with stakeholders.

AI models rely on existing data for learning and generating content, which means their creations are inherently derivative. Human creativity, on the other hand, can be wholly original, exploring uncharted territories and unique perspectives. The personal touch, emotions, and life experiences that human creators bring to their work make it inherently authentic and reflective of their individuality.

Data has become the foundation for understanding and making sense of the world around us. As an ecosystem, the internet is a prime example of a platform that has facilitated the creation, editing, sharing, and consumption of information. Similarly, as the use of data continues to evolve, so too must our understanding of the myriad of specialisations and tools that are now available to us.

In 2017, thought leader and visionary Phillimon Zongo wrote an ISACA journal article titled 'The Automation Conundrum'. This article highlighted three strategic concerns that AI systems would present to organisations. These included flawed algorithms that may adversely impact organisations, cultural resistance from people fearing that AI would take away their jobs, and lastly an expanded cyber threat surface. Fast-forward to 2023, and we are seeing all three risks have come to life. The question, therefore, is, if AI presents such significant risks, how should organisations approach this emerging technology?

While the future of the CDO role in the age of generative AI presents exciting possibilities, it also comes with significant challenges. CDOs will face complex issues related to data privacy, security, and algorithmic transparency. They will need to navigate regulatory landscapes, address societal concerns, and build trust among stakeholders. Moreover, managing the ethical implications of generative AI will require ongoing vigilance and a deep understanding of the potential risks associated with AI-generated content.

Moving past the limitations of correlation and embracing causal reasoning is a powerful tool for a data professional to have. Delivering answers grounded in causation, rather than correlation, is critical in meeting the expectations of business users and guiding informed decision-making.

It is evident that the path to meet reporting obligations as well as to inform relevant stakeholders involved in policy development and research is by entering into strategic and mutually beneficial partnerships which are people-centred and that ensure that no one will be left behind.

Approximately a decade since Artificial Intelligence (AI) and Machine Learning (ML) emerged as technical solutions, some capabilities are firmly embedded in business with measurable benefits. These technologies have, over the years, added business value in various areas including, inter alia, operational efficiency, cost reduction through automation, and improved customer experience.

Millennials and post-millennials may be called lazy but they often just find the easier way of doing things and that's not necessarily a bad thing! So yes, we may have a few negative perceptions working against us, but once we are aware, we can inform these reputations and really bring out our strengths as young people in the workplace.

As a data visualization designer, you’ve likely encountered the dilemma of whether to stick to “best practices” or venture into uncharted territories with creative approaches. Should you go with what works? Or should you push for something new?

In this article I explore a dimension of storytelling which I consider to be the most important; structuralism- a method of intellectual inquiry that provides a framework for organizing and understanding areas of human study concerned with the production and perception of meaning. We are all looking for meaning in data, the hidden structure within, rather than merely describing the way the data appears. This ability is what turns data into insights and inspires action from an end user. All data leaders should care about this. After all, we all desire for our organisations to be data-driven, or as I call it, value-driven through data.

With nearly thirty years of experience in telecoms, Megan Nicholas is no stranger to being the only woman in the boardroom. She's firm in the belief that the industry won't see any balance between male and female leaders until women become more intentional about the change they want to see.