Generative AI has been evolving at a rapid pace, and businesses are racing to capture its value.
Throught the past year, GenAI has made a remarkable impact across industries and all organizational domains, from sales and marketing to R & D, logistics and finance. According to McKinsey’s State of AI in early 2024 report, GenAI adoption doubled to 65%.
Despite its rapid growth, companies and industries are still only scratching the surface. Many struggle to transform their Generative AI pilots into successful solutions, facing barriers such as data readiness, quality and accuracy issues, limited understanding of GenAI use cases, and concerns around data security and privacy.
However, based on numerous surveys and our own experience collaborating with global companies across various industries, a key roadblock is data readiness.
A survey of 334 CDOs and data leaders (sponsored by AWS and the MIT Chief Data Officer/Information Quality Symposium) revealed that while excitement around generative AI is high, there's still a lot of work to do. Specifically, companies haven’t developed new data strategies or managed their data in ways that would enable generative AI to be effective.
So what does it mean to be data-ready for generative AI?
We spoke with some of our top data experts to identify the key data essentials that can help companies worldwide achieve a solid foundation of data readiness, boosting their GenAI capabilities for future growth and business transformation.
Proprietary Data is the Key Ingredient to Gaining an Edge in Generative AI
For a long time, data has been considered the most valuable asset for businesses, but with the rise of Generative AI (GenAI), its importance has reached an entirely new level.
Today, large language models (LLMs) have become highly advanced systems that fully comprehend publicly available information across the internet. In this landscape, the key differentiator for businesses is their ability to leverage proprietary data to gain a competitive edge. Generative AI unleashes its biggest power when it uses company’s propriatery data.
Foundation models powered by company proprietary data unlock high-value insights about products, customers and operations—boosting decision-making, reducing risks, and driving efficiency. They also open new revenue opportunities. But while proprietary data is a goldmine, many companies struggle to harness it. That’s why investing in it is crucial—whether you’re piloting Gen AI or scaling it up.
“Organizations that pre-train, fine-tune, or enhance models using their own proprietary data through a RAG (Retrieval Augmented Generation) architecture will distinguish themselves from the crowd. Maintaining a competitive advantage now depends on ensuring data security, implementing effective data governance, and utilizing data efficiently within GenAI workflows.”
Tolga Coplu, Head of GenAI at BlueCloud
For a deeper dive, read How to Unleash Unstructured Data with GenAI - BlueCloud.
Data Quality, Governance and Security Drive Innovation in GenAI Era
In the GenAI era, data quality, governance, and security are more critical than ever. Data is essential not only for training AI models according to business needs but also for ensuring that the models generate relevant and high-quality outputs.
As Tolga explains, “data governance, security, and quality should not be treated as separate elements but rather as integral parts of the AI ecosystem. In this new era, businesses that manage their data most effectively will be the ones that extract the highest value from Generative AI, driving innovation and maintaining a strategic edge.
For a deeper dive, read Key data and AI trends for 2025.
Regression Testing and Continuous Monitoring of Data Is Paramount
The power of Generative AI lies in the quality of the data it learns from. If your data isn’t accurate, consistent, and well-managed, your AI models won’t deliver the reliable, context-aware insights your business needs.
“By implementing robust validation and verification processes, you can catch inconsistencies early—before they skew results or introduce bias into your AI-driven decisions. Plus, maintaining strict service-level agreements (SLAs) helps keep data operations efficient, ensuring smooth processing, timely responses, and cost control,” explains Minal Piyush Satpute, Senior Data Engineer at BlueCloud
As AI systems evolve, managing data versions becomes just as important. Without proper tracking, model drift can erode performance over time. “Regular regression testing and continuous data monitoring—including accuracy, completeness, and timeliness—are key to keeping your AI outputs relevant and trustworthy,” says Minal.
At the end of the day, a well-structured data strategy isn’t just a nice-to-have—it’s a necessity. If your goal is to leverage GenAI for meaningful, high-impact results, your data needs to be rock solid.
For a deeper dive read Cloud and data analytics trends for 2025.
Unstructured data is a source of unexplored value
Generative AI (GenAI) fundamentally alters data quality requirements.
Most companies rely on structured data—think rows and tables—which offers a predefined view of information. But unstructured data, like text, images, and video, is packed with rich, real-world context. When combined with structured data, unstructured data enhances generative AI, adding signals like tone, personality, and sentiment for more natural, human-like interactions.
“Unlike traditional systems focused on structured data, GenAI thrives on unstructured data, building context through inferred metadata and continuous interpretation. This requires a shift towards dynamic data curation, emphasizing freshness, relevance, and uniqueness, alongside robust sanitization to protect sensitive information.”
Busra Uslu, Data Analytics Leader at BlueCloud
Given GenAI's large-scale, inline processing demands, data quality must be integrated into the pipeline, ensuring real-time validation and bias mitigation. But to truly unlock its value, companies need to make it more accessible—expanding data architectures, strengthening security, and improving governance. The future of AI-driven insights depends on it.
Unlocking Unstructured Data with Snowflake’s Document AI
Unstructured data holds immense untapped potential, but extracting meaningful insights from it has always been a challenge. Snowflake’s Document AI changes that by enabling organizations to efficiently process and analyze unstructured data with ease.
Powered by Arctic-TILT, Snowflake’s proprietary large language model, Document AI intelligently processes various document formats, automating data extraction and enhancing decision-making.
Operating entirely within the Snowflake platform, it simplifies Intelligent Document Processing (IDP), allowing businesses to transform unstructured data into structured formats—without requiring deep machine learning expertise.
Real-World Impact of Document AI
From financial services to regulatory compliance and operational efficiency, Document AI is already transforming industries.
Learn more in Harnessing the Power of Snowflake Document AI: Top 3 Use Cases for Unlocking Value from Unstructured Data with BlueCloud.
Building a Trustworthy Data Foundation for GenAI Success
One of the biggest challenges in AI is fully understanding what could be causing bias in your models. As Abirami Karthikeyan, Data Analytics Manager at BlueCloud puts it:
"The model ran fine, but when I tried to interpret the results, I found myself at a loss—and that’s a real challenge, right? Making sure that you're understanding the parameters within which you're working so that you can interpret your AI results very well—I feel that's a challenge too."
Without high-quality, trustable data, AI can quickly become a black box, creating uncertainty and skepticism in business decision-making.
"Two problems—number one, trust won’t be there. If you have poor quality data, the business will not trust it. Even with basic analytics and visualizations, there’s no trust, and AI even more so because you are absolutely leaning on data quality to make decisions. AI becomes a black box, so you need an extra level of trust," explains Abirami.
But beyond trust, bad data can lead to costly business mistakes.
" If you don’t catch a quality issue in your data before feeding it into your model, the results could mislead the business entirely. Imagine a sales forecast predicts strong performance in Arizona, so you shift all your inventory there—only to end up with poor sales and major losses."
This is why high-quality, well-governed data is critical for AI adoption. Without it, businesses may hesitate to invest in AI—or worse, make decisions that negatively impact their bottom line.
For a deeper dive read Challenges of AI Adoption and How to Overcome Them.
GenAI Unlocks the Puzzle of Its Own Data
The smart move is to harness GenAI to build a strong, future-ready data foundation.
At BlueCloud, we’re exploring innovative ways to integrate GenAI into the data lifecycle, making it more efficient, scalable, and insightful. We’re leveraging GenAI to simplify complex processes like NLP-driven interactions for data insights, ML model development, and inference. Additionally, we see immense potential in using GenAI for large-scale migrations, particularly in accelerating and optimizing statistical code conversion.
“Inspired by partners like Snowflake—who are leading the charge with innovations like Cortex Analyst—we’re pushing the boundaries of what’s possible. Beyond technology, we’re building a new generation of data analytics professionals—experts who not only master the latest GenAI tools but also have a strong foundation in data fundamentals,” explains Abirami.
Snowflake's Cortex Analyst is an AI-driven tool designed to democratize data access by enabling users to interact with data through natural language queries. It allows individuals without extensive SQL knowledge to ask complex questions and receive accurate, context-aware responses, thereby simplifying the data exploration process. By leveraging advanced AI capabilities, Cortex Analyst interprets user intent, generates precise SQL queries, and retrieves relevant data, making data analysis more accessible across organizations.
For a deeper dive read “Elevating data analytics: How BlueInsights and Cortex AI empower organizations to talk to their data.”
How Can We Help You Improve Your Data Readiness at the Age of Generative AI?
The six new data fundamentals are just the beginning for companies on their journey to data readiness. With this information in hand, how will you take the next step to get your data ready?
At BlueCloud, we’re here to help you transform and optimize your data, empowering you to move forward with confidence.
Why BlueCloud?
We are flexible.
AI evolves daily. You need a deep understanding of trends and the ability to learn and unlearn quickly. That flexibility is critical, both at an individual level and as an organization.
We are quick learners.
We’re not just ML engineers—we quickly understand the business context. Our engineers learn the sales parameters, business goals, and industry context to build effective solutions.
We are committed to continuous success.
We don’t just build a model and walk away. We always ensure our models are sustainable, self-driven, and easy to maintain.
We are Snowflake partners
The partnership allows BlueCloud clients to leverage Snowflake’s flexibility, performance, and ease of use to deliver more meaningful data insights.
Explore our generative AI and ML capabilities to learn how we can help you ready your data for GenAI.
