As artificial intelligence (AI) becomes increasingly integral to business operations, data governance is essential to ensure the ethical and effective use of this technology. Integrating AI responsibly into business processes must be an organisational priority.
Recently, I was involved in foundational work at a start-up client where the focus was on establishing data governance from scratch. This entailed aligning data governance with the company’s strategy of prioritising customer data privacy and ethical data handling.
Understanding the data they have and where it resides were crucial components of the project. This played in the space of metadata management and data quality to make sure it is accurate and that the business is processing high-quality data for their customers.
Moreover, this reflects a broader shift toward business owners playing a significant role in data quality. It can partly be attributed to the availability of AI tools that simplify the creation of data quality rules. AI technology allows for general users to input layperson sentences and the data quality tool then suggests the necessary data quality rules. Furthermore, the AI tools can then transform these rules, once approved, into executable actions.
Data accuracy
Of course, the accuracy of AI predictions hinges on the quality of training data. Data must be accurate, relevant, and ethically sourced to ensure AI models perform their intended functions correctly.
Every organisation intending to adopt AI needs to put a comprehensive data governance framework in place to manage data effectively, ensuring it is fit for purpose and ethically used.
Ethical considerations are vital in this regard. Yet, ethics can vary according to companies, individuals, and countries. Responsible AI involves integrating privacy, security, inclusivity, transparency, and accountability from the outset. AI is not purely a technology. Instead, it is an organisational shift that requires structural adjustments within companies if they are to manage AI responsibly.
Bad data is a personal irritation of mine. I strongly believe that it is critical to develop business data quality rules and involve business owners in the data quality process.
Governance structure
However, there are several key considerations for companies when it comes to AI and data governance. The AI model is only going to be as accurate as the data it has been trained on. Feeding inaccurate data will result in inaccurate results.
If the company does not contextualise the data the AI model will use when it comes to the role it must fulfil as an output, it will give answers to the wrong questions.
The data must be relevant to the topic, which amplifies the mission critical importance of data governance. Therefore, processes and a framework must be in place to ensure the company uses the right data at the right time for the right purpose. This is where metadata becomes essential as well as data quality in terms of knowing whether the data is fit for purpose and accurate.
Furthermore, companies must also be cognisant of the ethical pitfalls when it comes to AI processing customer data without clear consent. The AI tools must respect customer data processing agreements already in place. Additionally, the local regulatory environment must still catch up with AI.
At the moment, AI adoption is faster than any of the previous phases of big disruption in the industry – and currently there is no set of comprehensive legislation to govern the adoption and use of AI and machine learning in the country.
But that is not to say that business could not still find themselves in hot water with the Information Regulator and other stakeholders if their AI deployment is not compliant with already enforceable data protection regulations such as POPIA and GDPR at a minimum. There could be some interesting scenarios where things will go wrong, and regulations will adopt and adapt. Until then, responsible AI deployment comes down to keeping with sound business ethics.
Petrus Keyter | Consultant | Data Governance | PBT Group | mail me |