Artificial Intelligence is “the” word in the data world. It’s a concept that will shape the organisations of the future. For the better. It’s like a magic word, that assures promises of hyper personalization, competitive edge, increasing revenues, transformation of service and product delivery et all to that will enable organisations to create competitive strategies to thrive in the future.

In fact, most organisations today have already jumped onto the AI bandwagon- a few as part of a well thought out strategy and several others as mere followers of a trending concept.

However, AI’s success in throwing up winning recommendations solely depended on the data that is fed to it. Hence, maintaining and ensuring the quality of data must be the most critical goal of any organization today. For, good data equals good recommendations and garbage data will result in garbage out.

Good quality data has been a challenge over the years, with many organisations attempting to fix it with pointed solutions and technology. But, the fact remains, that good quality data is an output of good Data Governance practices and processes. This is the foundational block for Data Quality, and thus the most critical pillar for AI.

Why Data Governance is the bedrock for AI

As we are all aware, AI depends on vast data sets, which must be of the required quality. To ensure this, data needs to be put through rigorous governance practices.

Data Governance essentially is a set of standards, policies, processes and procedures that govern data through the entire lifecycle, from data sourcing/collection, usage, management, storage, access, archival to deletion. And the role played by Data Governance is to ensure good quality, accurate and secure data is accessible to the business users to take business decisions. And AI models use and learn from the data that is fed to them. Poor quality data can lead to incorrect or biased business decisions, which can be extremely costly for organisations, leading to regulatory scrutiny and penalties and create a profound business impact thereby threatening the competitive edge and existence of the organization. Hence, Data Governance is a prerequisite for the success of AI. And its time organisations recognize this interrelationship between Data Governance and AI.

The interrelationship between Data Governance and AI

• Data Governance is an intrinsic part of the Data Lifecycle and AI is a part of it too.  The entire data lifecycle from sourcing to deletion of data is overseen by Governance practices. Since AI is now a critical art of this lifecycle, it is essential to govern the AI models (and not just the data that is fed to them), from the development of the AI system to the documentation of the system, the deployment, monitoring, enhancement and retirement of the AI system. The EU has taken the first step towards this by drafting the EU AI Act which deals with these very aspects. The Act on implementation will require EU nations to adhere to the governance standards as laid out in the Act.
• Data Governance plays a pivotal role in ensuring Data Quality standards. AI systems thrive on good quality data. The effectiveness of the output of AI depends on the quality of the data. Data Governance helps ensure the quality of the data by maintaining the accuracy and integrity of the data. Hence, Data Quality is an outcome of Data Governance. And AI is interwoven with Data Governance. 
• Ensuring Data Availability – AI systems need data, and data governance make data available in a responsible and compliant manner. Data access rules create the boundaries for the type of data that is made available to business users, including standards and policies for data democratization and data exchange.
• Creating Data Ownership – owning the data is a must in any data lifecycle, and appointing the right owners, and equipping them with the KPIs to monitor data is an aspect of Data Governance that should not be ignored in the AI world. Ownership of data results in clean, fit-for-purpose data, that can be leveraged by the AI models for data based business decisions and impact.
• Data Alerts – setting up processes for Data alerts to business stakeholders is an important part of Data Governance. Alerts and notifications for drastic changes in data will ensure the quality of the data and thus the output from AI Models. Additionally, setting up alerts for data leaks and breaches will equip organisations to quickly address and stop such issues and ensure the AI models are not privy to such data.
• Adhering to Regulatory Requirements- data governance plays a critical role in ensuring adherence to Regulatory requirements, including data privacy and security, data protection and data deletion. By considering regulations on GDPR and PI data and its usage through customer consent, data governance ensures that AI systems are not accessing any data that they should not and mitigates any risks related to data privacy and security regulations.
• Dispute Management for AI related decisions – the algorithms that make the decisions for AI impact not just businesses, but also individuals and the society at large. There are bound to be disputes on the decisions that arise through AI. And this is where Data Governance steps in, with well detailed and documented processes for dispute resolution. The EU AI Act covers this aspect and thus substantiates that Data Governance is the backbone for AI.
• Reporting – data governance lays the standards for reporting requirements from regulators on AI models and their output. (The EU AI Act covers reporting in detail)

The relationship between Data Governance and AI is intricately woven together. AI cannot be effective without good governance, as its very source – good quality data is dependent on good governance standards and processes. However, the reality is different. The failure to implement Data Governance has left several organisations to struggle with setting the foundation and the pressures of increasing revenues and margins have led them to rush into the adoption of AI, thereby exposing organisations to the risk of regulatory penalties and increasing costs, with a negligible ROI.

Hence, organisations need to hit the reset button, pause and evaluate their Data Strategies. Data Ownership should reside with the business users. Data Councils can be set up initially for a pre-determined duration to handhold the Data Stewards and other stakeholders in the management of Data. AI related Governance strategies and requirements must be incorporated in the Data Strategy, and new roles designed to supplement the new-age Data Lifecycle Management. Ongoing Data Literacy initiatives will help in a smooth adoption of Data Governance practices and standards. All these initiatives will strengthen the Data Governance Framework and help accrue visible business benefits through AI. Organisations will reap the benefits of data, adhere to evolving regulatory requirements and create a safe and ethical environment for the business, customers and the society to thrive in.