For months now, the media and AI evangelists alike have been heralding the AI Revolution in business. But with everyone clamouring to capitalise on the benefits of the seemingly endless array of AI technologies, it can be difficult for businesses to know where to start. Before ingraining AI into wider business processes, business leaders must make a number of considerations, including their data governance framework, ethical and regulatory compliance and workforce impact.
Since AI depends heavily on data, the creation and implementation of a well structured Data Governance framework is paramount in any AI implementation strategy. There are several reasons for this, including:
Data Quality: AI algorithms rely heavily on data to train, test, and validate models. If the data is of poor quality or contains errors, the AI will learn from these errors and produce inaccurate results. By implementing a robust data governance framework, you can ensure that data is accurate, reliable, and consistent across the organisation, leading to more accurate AI outputs.
Regulatory Compliance: AI applications often involve the use and processing of sensitive data. Data governance policies and processes ensures that data privacy is protected in line with regulations, including GDPR and CCPA.
Data Availability:Companies with strong data governance have clear policies and procedures for data access, ensuring that data scientists and AI developers have the data they need to utilise AI technologies.
Ensuring Ethical Use of AI: Data governance plays a crucial role in ensuring fairness, interpretability, transparency, and accountability in AI systems. It helps to avoid biased outcomes, unjustified decisions, or discrimination that can arise from faulty data or misuse.
Trust and Reliability: A company's ability to demonstrate that it has robust data governance can build trust both internally and externally. Stakeholders, customers, and regulators will have more confidence in a company's AI applications if they know that the data used is well-governed.
Security: Data governance involves protecting data from breaches and leaks, which is especially important when using sensitive data in AI applications. Effective data governance policies can mitigate the risk of data breaches and the potential misuse of AI.
Data Integration: AI often requires data from different sources and in different formats. Data governance helps ensure data consistency throughout systems so that data can be integrated effectively, making it suitable for use in AI models.
By creating and implementing the right Data Governance strategy for your business, you can not only ensure data security and accuracy, but you can start to reap the benefits of AI usage throughout your business.
David has 15 years’ experience in leading PMO teams and software engineers in enterprise organisations. His passion for data analytics started during his previous roles, driving change through process automation with Alteryx and Tableau. In November 2020, David became an Alteryx ACE. “I love analysing data to derive value and uncovering truths that enable business leaders to make better business decisions,” he says.