If you are implementing generative AI and it is using poor-quality data, you’re putting your business at risk. At best, you’re in danger of delivering unreliable results and misinformation. And at worst, falling foul of the law and irreparably harming your organization’s reputation.

That’s because generative AI relies on data to create the results it’s famous for. It learns from patterns and structures to generate new data with similar characteristics. So it almost goes without saying that if your data is out-of-date, inaccurate, or biased, your output will be too.

But with the right data, generative AI offers huge opportunities (McKinsey estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across 63 use cases). By taking a data-first approach, with data quality and governance at the heart of your generative AI, your results will be more accurate, more impactful, and more trusted. And you will be in a much better place to use generative AI to strengthen functions across your business - from sales and marketing to customer operations and software development.

I’ve helped organizations big and small to focus on their data and over that time I’ve spotted what can really make a difference when it comes to data quality. Follow these steps to get your data back on track:

Think data governance and standardization

Your data undoubtedly comes from lots of different sources - and that can mean inconsistencies and inaccuracies creep in more easily. Establish a robust data governance practice to ensure data is collected, stored, and managed in the same way across the organization. Simply standardizing data formats, naming conventions, and definitions can prevent data discrepancies and improve the accuracy of AI models.

Implement data profiling and quality metrics

To make sure your generative AI model is learning from accurate data, use data profiling tools to analyze and assess its quality. Define data quality metrics and establish thresholds for acceptable data quality levels. And have a clear process for dealing with inaccuracies.

Cleanse and preprocess data

A bit like painting the Forth Bridge - once you’ve cleansed your data, it will almost immediately need to be cleansed again. Why? Because people make mistakes when they manage data, and because things change and data quickly becomes out of date. So regularly cleaning and preprocessing data to remove duplicates, inconsistencies, and errors is essential. Implement data validation checks and continue to use data profiling techniques to proactively identify and address data quality issues.

Integrate and centralize data

It’s much harder to manage the consistency of your data if it is spread across different networks. To help your AI model access accurate and relevant information, combine data from different sources and store it in a centralized repository. This will ensure a comprehensive and holistic view that is simpler to manage.

Manage Metadata

Proper metadata management is crucial. Using information including where the data has come from, the type of data, and more, helps the AI model to understand the data better and improve its results. It can also help with monitoring the AI’s results and is useful for spotting potential bias.

Address and reduce potential bias

One of the key concerns with AI models, especially generative AI, is bias. And with good reason. Relying on biased data to train your AI model inevitably impacts the accuracy and fairness of the generated content. Addressing bias in your data early on means better results for you, and reduced risk of legal challenge.

The UK government has recognized the risks - and opportunities - around this issue and has established the Fairness Innovation Challenge to fund innovative solutions to tackle bias and discrimination in AI. So it’s definitely an area to watch.

Monitor and audit continuously

Regular data monitoring and auditing processes will help you to track data quality over time, and spot trends and potential issues early. Make sure you have clear steps to take when data quality drops so that it doesn’t impact your results.

Generative AI offers some amazing opportunities, so don’t be put off by the risks. Instead, think data first. Demand unbiased and reliable outcomes. And deliver them by focusing on data quality. Build your AI models on a foundation of clean and accurate data, to know that you can trust the results and open up opportunities for your business.