The True Cost of DataOps for GenAI

Imagine your GenAI giving business-critical answers based on faulty assumptions. 

A supply chain disruption caused by mismatched inventory data, a safety protocol overlooked due to inconsistent reporting, or a compliance violation slipping through the cracks. 

That’s what happens without proper DataOps. And no shiny GenAI tool can save you from it.

DataOps is the backbone of enterprise sanity, especially with GenAI in the picture. It’s about creating a system where data moves smoothly, has a clear purpose, and helps everyone work together. Without it, enterprises teeter on the edge of chaos. Data silos. Untrustworthy GenAI outputs. Spiraling operational costs.

GenAI raises the stakes even further. Because it thrives on context and meaning. If the underlying data you have isn’t semantically rich or well-governed, your GenAI results are unreliable. Think hallucinations, mismatched metrics, and analytics that make no business sense. 

In a recent conversation with Kent Graziano on the TrueDataOps podcast, Inna Tokarev Sela, Founder & CEO of illumex, talked about DataOps and why skipping it is like skipping leg day (sure, you can try, but you’ll stumble when it matters most). For the video version of their conversation, please see here:

Q: How have you seen the space of DataOps evolve over the last few years?

Inna: DataOps has always been fascinating to me. Especially in enterprise settings where monolithic systems dominate, and teams structure their workflows around CI/CD practices.

The past five years have seen DataOps evolve from data stack orchestration to handling the challenges of generative AI in 2022-2023. 

Generative AI applications introduced new demands, and this year, cost has become a major focus. In fact, the cost of implementing DataOps often proves lower than the cost of neglecting it. A stance that may seem surprising but has become more evident.

Gartner predicts that 2025 will be the year of AI for data. This brings focus to the role of DataOps in creating scalable, collaborative, and well-governed data environments that are so important for GenAI.

Q: With GenAI becoming more prominent, is it fair to say that unprepared data leads to poor results? 

Inna: Exactly. At illumex, we believe it takes generative AI to prepare for generative AI. 

That’s why, from day one, we embedded graphs and semantic models in our architecture. We offer data reconciliation, self-service data access, and analytics, adapted to each customer’s unique landscape.

Through metadata, we even integrate with traditional on-premise systems like Oracle and modern platforms like Snowflake. This metadata-driven approach allows us to mimic enterprise data flows and bring tailored experiences across varying infrastructures.

Q: Why do you think DataOps is so critical in the GenAI space?

Inna: Without DataOps, the foundation for GenAI falls apart. GenAI needs semantic coherence to interpret data meaningfully. If your data isn’t well-prepared, that is to say, if it’s not semantically aligned—your GenAI will not perform well. You’ll get unreliable responses. 

Beyond data preparation, governance is crucial. You need to govern every generative AI interaction to build trust and stay compliance. The role of DataOps is to tie all these threads together—data preparation, governance, monitoring—to enable intelligent, scalable decision-making.

Q: And what if we skip governance, especially with GenAI?

Inna: Governance is non-negotiable. And it’s not simply about who has access to what data. It’s about cultivating collaboration and trust. 

Governance ensures that generative AI operates transparently, with all processes documented and traceable. 

For example, at illumex, we combine semantic models with business glossaries. This means that when GenAI provides an answer, users can see exactly how it was derived—what definitions were used and what logic was applied. 

Q: Transparency is often overlooked as a key part of governance. How important would you say it is?

Inna: Transparency is key, especially with GenAI. Business users need to trust that the AI outputs they’re working with are accurate and meaningful. 

It essentially eliminates the ‘black box’ problem, making it clear how results are calculated. This ties directly into the user experience. If users can’t trust the data, they won’t use it. 

DataOps ensures this transparency, which builds confidence and drives adoption. And governance is all about promoting a collaborative, transparent environment. 

So, GenAI models must be designed with domain expertise and user context in mind to maintain accuracy and trustworthiness.

Q: Ontology also seems vital for making data accessible to business users. How would you explain its role in how non-technical teams use data?

Inna: That’s exactly why we coined the term “Generative Semantic Fabric.” It’s “generative” because we utilize generative AI, and “semantic” because we prioritize the needs of business users. Our approach allows the integration of GenAI agents and analytics in a way that includes domain experts and business-oriented users.

Domain-specific ontologies and semantic models are crucial for capturing business context and making data easy to understand across different departments and geographies. Instead of keeping semantics in a single tool, we created a flexible repository to align data across different teams and tools within an organization.

Yes, we used to dream of having a “single source of truth” in data warehousing. But in reality, semantic coherence is much more vital. Especially now, as we use multiple tools for AI and analytics.

I think the biggest promise of generative AI in enterprises is allowing intelligent, scalable decision-making. This is only possible when data models build context as well as meaning, making sure that enterprise GenAI operates on a trusted, transparent footing.

Q: With AI, hallucinations and reliability are significant concerns, aren’t they?

Correct. Hallucinations stem from mismatched terminology or inconsistencies in data labeling. Semantic coherence is essential. We achieve this by implementing a business glossary and certified definitions, which eliminate hallucinations and allow for full traceability.

Q: And with the right user experience, business users feel confident that they can use AI to make reliable decisions.

Absolutely. When we talk about labeling structured data, giving tables and columns semantic names is just the first step. The main work lies in aligning the data structure with meaningful business terms that people recognize and trust.

We automate this process to make sure that when users engage with data, they’re interacting with accurate, certified definitions, not abstractions. This is essential for enterprise contexts where governance and regulatory requirements demand full accountability.

Q: Brilliant. This approach minimizes the risk of AI misunderstandings. Now, as we wrap up, what’s next for illumex?

2025 will be an exciting year as we continue developing self-service data governance and generative analytics tools. 

We’re focusing on delivering data and GenAI solutions that are customizable and scalable for various industries. That’s why we’ve built our Generative Semantic Fabric–to address DataOps’ most critical challenges. 

From metadata-driven augmented governance to automating semantic alignment, we’ve reimagined how enterprises prepare their data for GenAI. And it’s all about creating trust, transparency, and collaboration at every step.