ARTIFICIAL Intelligence (AI)-enabled data governance is termed a new revolution in the digitalization of business, governance, and society. These technologies offer unmatched accuracy, efficiency, and ease of doing business. Data governance is defined as internal and external policies and regulations over data usage, processing, and presentation. The struggle for seamless data management is hindered by data quality, privacy, compliance, and trustworthiness. AI and data governance offer optimum solutions for data management, security, and automation goals.
Small and medium-sized organizations are increasingly adopting AI-enabled data governance and big data frameworks. Technologies like machine learning and deep learning serve as the backbone for automated decision support systems across various sectors, including finance, education, social media, e-commerce, banking, trading, retail, industries, manufacturing, and e-learning. These technologies facilitate improvements in trust, fairness, transparency, and equality within services, impacting every aspect of life. The widespread use of terms like B2B and B2C underscores their significance in supply chain management systems, reflecting the growing reliance on AI-driven solutions to enhance operational efficiency and customer experience. Data governance has deep ties to these benefits and shortcomings that are inherently part of these AI decision-making systems. So, data governance typically suffers from or enjoys these successes and failures. Data governance is solely focused on data management, organisation, and retrieval methods. The ultimate goals associated with data governance are up-scaling features of data availability, usefulness, and integrity. Governance itself has a very broad meaning and scope in the context of technological-based solutions. It provides guidelines, benchmarks, regulations, and legislation. Data governance is important due to the information flow and progress witnessed in this digital era of social and organizational fabric. The purpose of these integrated solutions is to maintain data availability, readiness, reliability, and protection.
In today’s data-driven landscape, AI serves as a trusted ally in data governance, playing a multifaceted role aligned with modern organizational needs. It acts as a diligent custodian of data integrity, sifting through vast datasets to identify and rectify errors, ensuring a sturdy foundation for governance. AI excels at complex data classification, aiding in sensitivity, usage, and compliance navigation, streamlining management processes. Additionally, it safeguards organizations through real-time monitoring against breaches and non-compliance, standing as a vigilant sentinel to ensure adherence to regulations and policies. Through continuous monitoring and proactive interventions, AI preserves data integrity and protects organizations from compliance risks.
In essence, AI transcends its technological phase to become a trusted partner in the journey of data governance, embodying qualities of diligence, insight, and vigilance. With AI by their side, organizations can navigate the complexities of data management with grace and confidence, harnessing its transformative power to drive efficiency, compliance, and strategic value creation in the digital age.
In short, AI-enabled data governance is a new era that promises efficiency, accuracy, smooth organizational decision support, control, and innovation. Security, privacy, and trust are the key challenges hindering the digital landscape. The AI algorithmic design and built-in biases, high costs, and lack of emotional and collective intelligence are problems associated with the dream. Human oversight may also require investigation to fit into the technological landscape. Automated solutions are a blessing or affliction that will be decided in the near future. The transformational capabilities of AI and the value of data in business and technological era are still leading to progress and popularity against the fears and pitfalls of technological advances. The future progress still demands ethical regulations, vigilant human insights, adoptions for newness, and trust-building legislation. AI and ML have established a force of progress in the evolutionary scene of data governance.
—The writer is contributing columnist.
