Who is to blame when data goes wrong?

Data and analytics, along with artificial intelligence, have become essential tools in today's business world, driving growth and innovation. However, as their usage increases, so does the trust gap among executives. A recent report, 'Guardians of Trust' by KPMG International, reveals that only 35% of executives have a high level of trust in the way their organization uses data and analytics. This lack of trust raises critical questions about accountability when things go wrong. Joining CNBC Africa to shed light on this issue is Denis Muli Mutinda, Senior Manager for Data & Analytics at KPMG Advisory Services Limited. Mutinda emphasizes that trust is a fundamental element in both systems and individuals. Without credibility and reliability in the data being produced and the people handling it, trust cannot be established. Mistrust often arises from unreliable data and the increasing complexity of systems and algorithms. Executives must ensure that the data being collected is accurate and that the algorithms being utilized are robust to maintain public confidence and encourage investment. When asked about who is to blame when a machine or algorithm malfunctions, Mutinda points out that the responsibility often falls on senior executives. While developers and tech analysts play a role in setting up algorithms, the ultimate accountability rests with the executives. Interestingly, blame is no longer placed on individual employees but is distributed across the organization. Surveys have shown that a majority of respondents hold organizations accountable for errors, even in cases where regulatory bodies are involved. This shift in the blame game highlights the need for strong governance structures within organizations. The research conducted by KPMG surveyed executives from nine countries, including the US, UK, Brazil, and India. Surprisingly, Brazil and India had the lowest percentages of executives taking responsibility for data-related mishaps, despite being prominent players in the digital solutions market. Mutinda credits India's success in handling data to its long-term investments in IT, emphasis on accuracy, and the trust built over time. He notes that Africa, while progressing in building capabilities, still has room for growth in governance of data. Looking ahead, Mutinda discusses the importance of accountability in data and analytics. He suggests that CEOs may need to enhance their understanding of machine accountability to ensure effective governance. While CEOs can delegate responsibilities, they remain ultimately accountable for the organization's data practices. Mutinda stresses the significance of establishing clear data governance structures to determine who owns and uses the data, emphasizing the importance of collecting data ethically and responsibly. In a rapidly evolving digital landscape, trust in data and analytics is paramount. Executives must prioritize building robust governance frameworks and fostering a culture of accountability to navigate the challenges of data usage effectively. By embracing transparency, integrity, and accuracy in data practices, organizations can enhance trust among stakeholders and drive sustainable growth in the digital era.