Artificial intelligence has been the topic of conversation in recent years, growing quickly in different sectors and making progress in solving difficult problems. AI is quickly creating value for many organizations through risk management, automation, reducing human error, analyzing large volumes of data, generating insights, etc. Through technological leaps and mass investment, AI has also become a reality and a key part in everyday life.
AI has become more accessible to the general public through the use of well-known tools available for public consumption. For example, ChatGPT has become an extremely popular tool and a household name allowing users to do many things with it, such as analyzing reports, creating budgets, building virtual assistants, writing code, etc. The reliance on AI tools has had a significant impact on individual knowledge, livelihood and even personal health. AI tools have saved its users time and often money through immediate and creative solutions.
Although AI is often spoken about, it is not always understood. At a high level, AI is a technology that enables machines to simulate human learning, comprehension, problem-solving, decision-making, creativity and autonomy. There are also different types of AI, such as reactive, general, super, limited, theory, etc. All include the ability to process large volumes of data very quickly.
What is the relationship between AI and data?
The relationship between AI and data is crucial. AI relies heavily on data for operation and evolution. AI uses data to learn, improve and to make different predictions. The quality of the data has a significant impact on how the system learns and adapts. This is why data governance is essential in ensuring effective and even ethical use of AI.
Data governance looks at the data lifecycle management through availability, integrity and security of the data in enterprise systems. This also includes compliance with legal frameworks, such as data privacy laws and enabling ethical data practices through accountability, fairness and transparency.
Effective data governance helps organizations mitigate potential risks, including producing flawed insights, generating biases and making wrong decisions. These further impact customer trust, overall reputation and could end up costing organizations a lot of money.
As organizations look to introduce AI into their environments, they need to shift their focus on their overall data foundation. What does their data governance and data strategy program look like? Is the data fit for purpose?
Organizations need to audit data sources to ensure accuracy and quality. Consider the sensitivity of the data and potential biases. Review security and privacy requirements, including global laws and industry best practices. Ensure transparency and accountability by documenting an audit trail and how the data will be used in the context of AI systems.
Shift the focus, and remember that it all starts and ends with data.
Editor’s note:Maria will share additional insights on this topic as a co-presenter at the session, “Data Governance for AI: It Starts and Ends with Data” at ISACA 2024 Europe Conference, to take place 23-25 October in Dublin, Ireland.