Generative AI

Your Company is Ready for Gen AI. But is Your Data?

Dee Radh

June 5, 2024

your company and data ready for gen AI

The buzz around Generative AI (Gen AI) is palpable, and for good reason. This powerful technology promises to revolutionize how businesses like yours operate, innovate, and engage with customers. From creating compelling marketing content to developing new product designs, the potential applications of Gen AI are vast and transformative. But here’s the kicker: to unlock these benefits, your data needs to be in tip-top shape. Yes, your company might be ready for Gen AI, but the real question is—are your data and data preparation up to the mark? Let’s delve into why data preparation and quality are the linchpins for Gen AI success.

The Foundation: Data Preparation

Think of Gen AI as a master chef. No matter how skilled the chef is, the quality of the dish hinges on the ingredients. In the realm of Gen AI, data is the primary ingredient. Just as a chef needs fresh, high-quality ingredients to create a gourmet meal, Gen AI needs well-prepared, high-quality data to generate meaningful and accurate outputs.

Garbage In, Garbage Out

There’s a well-known adage in the data world: “Garbage in, garbage out.” This means that if your Gen AI models are fed poor-quality data, the insights and outputs they generate will be equally flawed. Data preparation involves cleaning, transforming, and organizing raw data into a format suitable for analysis. This step is crucial for several reasons:


Ensuring data is accurate prevents AI models from learning incorrect patterns or making erroneous predictions.


Standardizing data formats and removing duplicates ensure that the AI model’s learning process is not disrupted by inconsistencies.


Filling in missing values and ensuring comprehensive data coverage allows AI to make more informed and holistic predictions.

The Keystone: Data Quality

Imagine you’ve meticulously prepared your ingredients, but they’re of subpar quality. The dish, despite all your efforts, will be a disappointment. Similarly, even with excellent data preparation, the quality of your data is paramount. High-quality data is relevant, timely, and trustworthy. Here’s why data quality is non-negotiable for Gen AI success:


Your Gen AI models need data that is pertinent to the task at hand. Irrelevant data can lead to noise and outliers, causing the model to learn patterns that are not useful or, worse, misleading. For example, if you’re developing a Gen AI model to create personalized marketing campaigns, data on customer purchase history, preferences, and behavior is crucial. Data on their shoe size? Not so much.


Gen AI thrives on the latest data. Outdated information can result in models that are out of sync with current trends and realities. For instance, using last year’s market data to generate this year’s marketing strategies can lead to significant misalignment with the current market demands and changing consumer behavior.


Trustworthy data is free from errors and biases. It’s about having confidence that your data reflects the true state of affairs. Biases in data can lead to biased AI models, which can have far-reaching negative consequences. For example, if historical hiring data used to train an AI model contains gender bias, the model might perpetuate these biases in future hiring recommendations.

Real-World Implications

Let’s put this into perspective with some real-world scenarios:

Marketing and Personalization

A retail company leveraging Gen AI to create personalized marketing campaigns can see a substantial boost in customer engagement and sales. However, if the customer data is riddled with inaccuracies—wrong contact details, outdated purchase history, or incorrect preferences—the generated content will miss the mark, leading to disengagement and potentially damaging the brand’s reputation.

Product Development

In product development, Gen AI can accelerate the creation of innovative designs and prototypes. But if the input data regarding customer needs, market trends, and existing product performance is incomplete or outdated, the resulting designs may not meet current market demands or customer needs, leading to wasted resources and missed opportunities.

Healthcare and Diagnostics

In healthcare, Gen AI has the potential to revolutionize diagnostics and personalized treatment plans. However, this requires precise, up-to-date, and comprehensive patient data. Inaccurate or incomplete medical records can lead to incorrect diagnoses and treatment recommendations, posing significant risks to patient health.

The Path Forward: Investing in Data Readiness

To truly harness the power of Gen AI, you must prioritize data readiness. Here’s how to get started:

Data Audits

Conduct regular data audits to assess the current state of your data. Identify gaps, inconsistencies, and areas for improvement. This process should be ongoing to ensure continuous data quality and relevance.

Data Governance

Implement robust data governance frameworks that define data standards, policies, and procedures. This ensures that data is managed consistently and remains high-quality across the organization.

Advanced Data Preparation Tools

Leverage advanced data preparation tools that automate the cleaning, transformation, and integration of data. These tools can significantly reduce the time and effort required to prepare data, allowing your team to focus on strategic analysis and decision-making.

Training and Culture

Foster a culture that values data quality and literacy. Train employees on the importance of data integrity and equip them with the skills to handle data effectively. This cultural shift ensures that everyone in the organization understands and contributes to maintaining high data standards.

The Symbiosis of Data and Gen AI

Gen AI holds immense potential to drive innovation and efficiency across various business domains. However, the success of these initiatives hinges on the quality and preparation of the underlying data. As the saying goes, “A chain is only as strong as its weakest link.” In the context of Gen AI, the weakest link is often poor data quality and preparation.

By investing in robust data preparation processes and ensuring high data quality, you can unlock the full potential of Gen AI. This symbiosis between data and AI will not only lead to more accurate and meaningful insights but also drive sustainable competitive advantage in the rapidly evolving digital landscape.

So, your company is ready for Gen AI. But the million-dollar question remains—is your data?

Download our free Gen AI Data Readiness Checklist shared at the Gartner Data & Analytics Summit.

Dee Hadh headshot

About Dee Radh

As Director of Product Marketing, Dee Radh heads product marketing for the Actian Data Platform. Prior to that, she held senior PMM roles at Talend and Formstack. Dee has spent 100% of her career bringing technology products to market. Her expertise lies in developing strategic narratives and differentiated positioning for GTM effectiveness. In addition to a post-graduate diploma from the University of Toronto, Dee has obtained certifications from Pragmatic Institute, Product Marketing Alliance, and Reforge. Dee is based out of Toronto, Canada.