Data - The New Oil: Data is the raw material of the information age and the need for strengthened data governance.

The phrase "data is the new oil" has been in use since approximately 2006 when British mathematician Clive Humby first coined it. He highlighted the idea that, like oil, raw data on its own is not useful but can be incredibly valuable when processed and analysed. Similar to oil, Michael Palmer said that data is "valuable, but if unrefined, it cannot be used."The concept gained widespread popularity and acceptance in 2017 after an article in The Economist further emphasised the comparison, positioning data as a crucial asset driving the modern information economy.

The world today has widely embraced the notion that "data is the new oil," recognising data as a critical asset driving technological advancement and economic growth. This perspective is evident in the increasing investments in data analytics, artificial intelligence, and machine learning across industries. Companies leverage data to enhance operational efficiency, personalise customer experiences, and innovate new products and services.

The importance of data in the information age can be attributed to its transformative impact on various sectors. According to Mayer-Sch?nberger and Cukier (2013), data is fundamentally altering the way businesses, governments, and individuals operate. They argue that data, when harnessed correctly, can lead to significant advancements in efficiency, innovation, and decision-making processes. Brynjolfsson and McAfee (2014), who contend that data-driven decision-making is a key factor in enhancing organisational performance and competitiveness, provide additional support for this viewpoint.

However, the comparison also highlights significant challenges concerning data privacy and security. The rise of surveillance capitalism, as critiqued by scholars like Shoshana Zuboff, underscores the ethical implications of data commodification. Privacy breaches and the misuse of personal data have led to calls for stronger regulatory frameworks and ethical standards to ensure that data usage benefits society without compromising individual rights (Zuboff, 2019).

Economic Value of Data

The economic potential of data is substantial. A study by McKinsey Global Institute (2016) highlights that data-driven strategies have the potential to generate significant economic value across various industries. For instance, in healthcare, data analytics can lead to better patient outcomes and cost savings by enabling personalised treatment plans and predictive maintenance of medical equipment. Similarly, in the retail sector, data-driven insights can enhance customer satisfaction and increase sales through targeted marketing and inventory optimisation (Manyika et al., 2016).

Benefits of Data in Artificial Intelligence

The benefits of data extend significantly into the realm of artificial intelligence. AI systems rely heavily on large datasets to learn and make predictions. According to Goodfellow, Bengio, and Courville (2016), the effectiveness of machine learning algorithms improves with the quantity and quality of data. Data enables AI systems to recognise patterns, make decisions, and even predict future outcomes with high accuracy.

1. Enhanced Learning and Predictions: AI algorithms, particularly in machine learning, thrive on vast amounts of data to train models. For instance, in image recognition, datasets containing millions of labelled images are used to teach AI systems to identify objects with remarkable precision (LeCun, Bengio, & Hinton, 2015). This capability has far-reaching applications, from medical diagnostics to autonomous vehicles.

2. Personalisation and Customisation: Data-driven AI enables highly personalised experiences in various domains. In e-commerce, for example, AI algorithms analyse user data to provide personalised product recommendations, thereby improving customer satisfaction and boosting sales (Aggarwal et al., 2016). Similarly, in education, AI-driven platforms use student data to offer customised learning pathways, enhancing educational outcomes (Luckin et al., 2016).

3. Efficiency and Automation: Data-powered AI systems enhance operational efficiency through automation. In manufacturing, AI-driven predictive maintenance, based on data from sensors, can anticipate equipment failures before they occur, reducing downtime and maintenance costs (Lee et al., 2015). In finance, AI systems analyse vast amounts of transaction data to detect fraudulent activities in real-time, thereby enhancing security and trust (Ngai et al., 2011).

Despite the widespread acknowledgement of data's value, some scholars caution against overestimating its benefits. Zuboff (2015) warns of the potential risks associated with the commodification of personal data, including privacy violations and the erosion of individual autonomy. She argues that while data can drive innovation, it can also be used to exert control and manipulate behaviour, raising ethical and societal concerns.

Moreover, Carr (2020) challenges the notion that data is the sole driver of progress in the information age. He emphasises the importance of human judgement and critical thinking, arguing that an over-reliance on data can lead to a devaluation of human expertise and intuition. Carr's perspective suggests that while data is a valuable resource, it should be integrated with other forms of knowledge and experience to make well-rounded decisions.

Strengthened Data Governance

More focus is placed on adhering to strict data protection laws, such as the Protection of Personal Information Act (PoPIA) and the General Data Protection Regulation (GDPR) in the EU. Organisations are required to put in place stringent data protection safeguards and guarantee openness in data handling procedures.

Legal Compliance: Data must comply with the local laws of the country where it originates. This includes data protection regulations, privacy laws, and any other relevant legal frameworks.

Ethical Standards: It is critical to create and impose ethical guidelines for data use. Organisations must weigh the benefits of data-driven innovation against the moral issues of justice, permission, and privacy (Zuboff, 2019).

Data Stewardship: Designating specific data stewards or officers to manage data governance frameworks has become standard procedure. These positions will guarantee that data is handled sensibly and that the security and integrity of data are safeguarded by well-defined processes and data practices.

As they make sure that data produced inside their borders is subject to local laws and regulations, nations and regions will be claiming data sovereignty more and more. For multinational companies, data sovereignty means navigating a complex web of regulations. Compliance can involve adapting data management practices, investing in local data centres, and ensuring that data transfer mechanisms meet legal standards. This will affect how international businesses handle data transfers across borders and meet legal obligations. Cloud service providers must ensure that their services comply with data sovereignty requirements, potentially by offering localised storage solutions. Businesses must implement safeguards such as standard contractual clauses, binding corporate rules, or other mechanisms to legally transfer data across borders.

Conclusion

Numerous studies and expert opinions support the claim that data is the foundation of the information age. The ability of data to drive economic value, enhance decision-making, and foster innovation underscores its significance in contemporary society. The integration of data with artificial intelligence further amplifies its transformative potential across various sectors. However, it is crucial to remain cognisant of the potential risks and limitations associated with data dependency. A balanced approach that integrates data with human judgement and ethical considerations is essential for harnessing its full potential.

In essence, while data's value is universally acknowledged, balancing its benefits with ethical considerations remains a crucial global discourse. In the future, we will likely see more sophisticated data management practices and robust governance to address these challenges.

References

Aggarwal, C.C. and Aggarwal, C.C., 2015.?Mining text data?(pp. 429-455). Springer International Publishing.

Brynjolfsson, E. and McAfee, A., 2014.?The second Machine Age: Work, progress, and Prosperity in a time of brilliant technologies. WW Norton & Company.

Carr, N., 2014.?The glass cage: Automation and us. WW Norton & Company.

Goodfellow, I., Bengio, Y. and Courville, A., 2016.?Deep learning. MIT Press.

Humby, C., 2006. Data is the new oil. Proc. ANA Sr. Marketer’s Summit. Evanston, IL, USA, 1.

LeCun, Y., Bengio, Y. and Hinton, G., 2015. Deep learning.?nature,?521(7553), pp.436-444.

Lee, J., Bagheri, B. and Kao, H.A., 2015. A cyber-physical systems architecture for industry 4.0-based manufacturing systems.?Manufacturing letters,?3, pp.18-23.

Luckin, R. and Holmes, W., 2016. Intelligence Unleashed: An argument for AI in education.

Mayer-Sch?nberger, V. and Cukier, K., 2013.?Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung Byers, A. (2016). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.

McKinsey Global Institute. (2016). The Age of Analytics: Competing in a Data-Driven World. McKinsey & Company.

Zuboff, S., 2023. The age of surveillance capitalism. In Social theory re-wired (pp. 203-213). Routledge.