Overcoming persistent business challenges can seem like a task that’s just not humanly possible. It, therefore, stands to reason that industry leaders have turned to non-human intelligence for solutions.

A recent McKinsey report identified at least 63 possible use cases for generative AI (GenAI) across 16 business functions, including customer operations, software engineering, research and development, and marketing and sales. Ignoring these opportunities would be shortsighted. If fully implemented, these use cases have an estimated potential impact of hundreds of billions of dollars.

As the demand for GenAI grows and AI tools proliferate, there is an urgent need to address bad enterprise data. Great AI outcomes start with well-governed data—and we’ll show you the how and why.

The AI-data governance link

Any enterprise AI or large language model (LLM) can only be as good as the data it is trained with. Data governance frameworks guide best practices for collecting, storing, and managing the immense volumes of data that flow into an organization at any given time.

Low-quality data—which has not been cleaned, verified, or organized—can affect the quality of AI outputs and business outcomes in several ways:

• Inaccurate or biased outputs
• Contradictory insights and unreliable predictions
• Compliance issues arising from references to classified or restricted data
• Decreased confidence in business decisions
• Erosion of trust from internal users and external customers/clients
• Reduced productivity and efficiency

Inaccurate risk identification, assessment, and mitigation - even minor discrepancies can escalate when GenAI is trained on its own outputs, amplifying mistakes and biases. It is essential to focus on getting things right with AI, not just getting things done.

Here, data governance provides structure, not stricture. Properly cleaned, labeled, and structured datasets lay a fair, unbiased foundation for your AI model. Even simple steps like standardizing data formats, naming conventions, and definitions can prevent discrepancies and improve the accuracy of AI model outputs.

It would be ideal to establish a comprehensive data governance framework before integrating GenAI into your operations. This ensures your next big initiative is well-supported to make a global impact.

Three steps today for smarter AI tomorrow

Laying the groundwork for your next GenAI project is crucial for obtaining accurate, reliable outputs and insights, freeing your team to act decisively and confidently in the face of business challenges. Here’s how to launch a framework that solidifies your data foundation:

Define data ownership and accountability

Data governance is more than just rules and standards; it also includes organizing workstreams that balance speed and security. Data stewards must underline the importance of properly organized and secured data.

Your teams should know what datasets they are responsible for, how to maintain their quality, and how to access other datasets they may work with. In this way, all functions are assured swift, constant, and secure access to the data they need for key decisions.

Have rigorous standards for data privacy and compliance

Your data is subject to various regulations depending on your industry and location. Upholding these standards is crucial for trust, privacy, and security. Familiarize your teams with the laws and regulations that dictate data access and storage and ensure they are consistently adhered to.

With proper oversight from internal counsel and tech leaders (including a chief privacy officer), AI success can be achieved without sacrificing security, trust, or compliance.

Regular data audits and updates

Now that your data has been cleaned and restructured, keep it in optimal condition with regular checks and audits. This can look like:

• Removing irrelevant or outdated records
• Verifying accuracy and consistency between databases
• Automated processes to identify duplicates or corrupt records

Consistent data management efforts prevent minor issues from compounding into major crises. They also minimize future troubleshooting and prevent systemic data issues from hindering your progress.

Data quality control

Prioritizing your data yields lasting positive effects that boost your organization beyond AI initiatives. Centralizing low-quality data without addressing underlying issues means consolidating all your data errors and discrepancies in one place, posing a greater risk to your strategic and operational efficiency.

For best results, we recommend adopting a Data First approach. This means addressing the quality and integrity of your data as the first step of any AI initiative or major digital transformation. A robust, reliable data foundation lets you make decisions that enhance your business outcomes now and in the future. This approach has helped industry leaders streamline operations, encourage innovation, and continue to lead in an ever-evolving market.

Set Up for Success

Done right, data governance sets up your enterprise for success with GenAI today and in the future. Emphasizing data quality ensures access to the right data at the right time. This leads to superior AI outputs, better decision-making, and improved business results.

Learn more about how Syniti can help you forge ahead with high-quality, trustworthy data in this research report from HFS.