There must be good quality, intelligent data at the heart of AI models, fuelling the transformation all around us Intelligent data is at the heart of transformation
Introduction:
Generative AI (GenAI) has emerged as a transformative force that is disrupting businesses across industries. At its core, GenAI is an advanced form of artificial intelligence that can create completely new content across different modalities (text, images, audios and videos) based on the large volume of data used to train it. The crux of GenAI's effectiveness lies not only in the sophisticated algorithms, but also in the quality of this training data. It is very crucial for organisations to invest in its wealth of data in order to unlock the true potential of GenAI.
The foundation of GenAI: Organisational data wealth:
In order for GenAI to operate with utmost efficiency and effectiveness, a vast assortment of diverse data is imperative. Organisations (government and businesses) today are sitting on piles of diverse data, collected over years of operations that include customer interactions, transaction records, internal knowledge pieces, etc. In the context of GenAI, this data is not just a record of the past but a key ingredient for “generating” future content. For instance, a sectoral regulatory authority can use existing laws, violations, penalties, and referencing international similar policies to generate draft policies for the future, that can result in better quality of service and compliance.
We have to remember that the quality of data utilised to train models will have an impact on the results it generates. So, while low-quality data can produce misleading or inaccurate findings, high-quality data will generate accurate, dependable AI outputs.
Common data challenges
Organisations not implementing effective data management strategies face common challenges that will have an impact on their businesses.
Unmanaged data suffers from data quality issues such as inaccuracies, inconsistencies, and outdated information. This undermines the reliability of data analytics and business intelligence efforts, leading to flawed insights and decisions.
Lack of data management impacts its interoperability, both within the organisations, and within the ecosystem. In the absence of agreement on the meaning of data and its standard and format, it becomes almost impossible to interpret its meaning, exchange it with other systems, and use it for decision making.
It also increases the risk of data breaches, unauthorised access, and data loss. Without proper controls and policies in place, sensitive information may be exposed, leading to legal, financial, and reputational damage.
In addition, it results in a degraded customer experience. Customers expect personalised and seamless experiences across all touchpoints. A lack of data management can hinder an organisation's ability to provide these experiences, leading to customer dissatisfaction and churn.
If we project these common challenges into a healthcare provider, this might result in non-authorised access to patients’ sensitive data, coding clinical data using different data standards, thus complicating the integration efforts among the clinical systems, inaccurate health records due to data quality issues. This can have dire consequences, not just operationally and financially, but most importantly, in the safety and quality of care provided to patients.
The imperative of data management for GenAI:
Effective data management, governance, and data quality are critical attributes that should be considered to achieve the potential of GenAI and overcome common data challenges. A comprehensive data management strategy should define a clear motivation and direction, in alignment with the business counterparts. It should also institutionalise the data management practice to sustain data management efforts with clear roles and responsibilities, in alignment with the international and national requirements.
For example, in KSA, the National Data Management Office (NDMO) has enacted the “Saudi Personal Data Protection Law” to enhance transparency across government roles, nurture and support innovation, and achieve data-driven decision making. In addition, NDMO has defined data management policies to better leverage data hosted within organizations to foster the growth of the digital economy. Organisations will need to comply with the personal data protection law and the data management policies to increase trust and reliability in its data that will be used to train or fine-tune GenAI models. This in turn will improve quality and helpfulness of generated content by these models, and adhere to Responsible AI requirements.
Validating data – Ensuring quality in GenAI systems:
Data validation is a cornerstone step in the journey of GenAI. Data must be regularly checked for accuracy, completeness, consistency, freshness, uniqueness, and validity to cover all aspects of data quality as defined in the Data Management Association (DAMA International) framework. There are tools that can be employed to manage data quality, however, these tools require clear input on the data quality rules which shall be defined in close cooperation between the data owners, stewards and the data governance teams. The goal is to maintain a high standard of data quality, so the GenAI outputs are reliable and useful.
Ethical considerations and alignment with AI guidelines:
Adhering to ethical standards and responsible AI guidelines is imperative. This includes respecting user privacy, ensuring transparency in how GenAI operates, and aligning with Saudi Arabia's national AI strategy and international guidelines, such as AI Act for the EU and the “Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence” in the US. Ethical data usage ensures not just compliance but also builds public trust – a crucial factor in the age of data breaches and privacy concerns.
In Saudi Arabia, the NDMO issued the “AI Ethics Principles” document that aims to guide moral conduct in developing and using AI systems. The seven principles defined within this document target improving reliability and trustworthiness of AI activities in the country. It is critical that these principles are complied with throughout the various phases of GenAI system implementation.
The direct correlation: Data quality and GenAI efficacy:
The direct correlation between data quality and GenAI’s performance cannot be overstated. Inaccurate or biased data can lead to flawed AI decisions, which can have significant real-world consequences. On the other hand, high-quality, well-managed data can result in AI systems that are not only efficient but also innovative, capable of generating solutions and ideas beyond human imagination.
Conclusion:
As we advance into the digital age, the role of data has shifted from a mere historical record to the cornerstone of innovation. For organisations to pioneer in their fields, investing in and strategically managing their data isn't an option; it's a necessity.
Orgnisations need to unlock the value of data with a clear vision as they pave the way for innovation and breakthroughs. Data management strategy is, therefore, the first and critical step for organisations to embark on to achieve sustainable outcomes.
