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Get the reportAI is already transforming how teams write code, generate legal documents, and summarize complex information. But in data, the transition has been slower. Tools like ChatGPT can interpret static files or simple questions, but they fall short when it comes to producing reliable, useful outputs from raw or even modelled data.
That’s because business data isn’t just about rows and columns. It comes with complex relationships, institutional knowledge, evolving schemas, access controls, and metric definitions that vary from one organization to the next. Without that surrounding context, AI can’t produce results that are accurate or meaningful. Outputs risk being wrong, inconsistent, or irrelevant, which undermines trust and slows adoption.
This guide outlines the current options for AI tooling for data teams, why context is the differentiator, and how your team can move from experimentation to real outcomes.
Over the last few years, the role of data teams has expanded well beyond reporting and dashboarding. They’re now expected to support decisions across every department, ensure data quality, manage tooling, and maintain governance. At the same time, the number of requests coming from the business continues to grow, often with increasing complexity and urgency.
Large language models (LLMs) are becoming a practical response to this shift. Rather than scaling support tickets or increasing headcount, many teams are exploring how AI can meet users where they are, provide faster answers, and reduce routine workloads.
LLMs offer several advantages that make them especially appealing in a modern data environment:
This shift is not just about speed or convenience. It reflects a growing need to scale data access without burning out the team responsible for maintaining it. LLMs offer a path toward more distributed, scalable data support where users feel empowered to explore and data teams can focus on what matters most.
Of course, the success of this approach depends on more than just having a chatbot. Without data context, these systems often return inconsistent, shallow, or misleading answers. For LLMs to work reliably, they need to be grounded in the same context data teams rely on every day.
Teams are eager to use LLMs for analytics, but most tools available today fall short of supporting the complexity of real data environments. The difficulty goes beyond missing metadata or limited schema visibility. What makes this problem harder is the way context is spread across disconnected systems.
Important information lives in different layers of the stack. A column might be stored in Snowflake, defined in dbt, and visualized in Looker. Each tool describes the data differently, applies its own logic, and has its own naming conventions. Without a shared foundation, AI tools have little to work with when it comes to reasoning across systems.
Most models also struggle to keep up with how quickly data changes. Pipelines evolve, metrics are redefined, and ownership shifts over time. When tools operate in isolation, there is no way for them to interpret dependencies, assess impact, or adjust for changes. Even basic differences in how BI tools label charts or how transformation tools organize models can create confusion for AI systems.
Security is another barrier. Access controls vary across tools, and many LLMs have no awareness of who should be allowed to see what. Without visibility into those controls, outputs risk surfacing sensitive data to the wrong people or skipping over relevant content entirely.
These challenges are why no general-purpose LLM has emerged as the go-to solution for data teams. Without a layer that brings together lineage, definitions, usage, and governance, AI cannot produce results that teams can rely on consistently.
As interest in AI-powered insights grows, more tools are entering the market to help organizations generate answers from data. Many promise faster access to insights and reduced pressure on data teams. But despite similar goals, their effectiveness often depends on a single factor: how well the system understands your data environment.
The following sections break down common categories of AI output tools, giving you a full view into your current options in the market.
Solutions from providers like OpenAI, Anthropic, Gemini, and Grok are often the first stop for teams exploring generative AI. These models offer intuitive, chat-based interfaces and strong general reasoning abilities. While some now support integrations with tools like Google Drive, GitHub, and Intercom through Model Context Protocol (MCP), they still lack native understanding of the structured and governed data systems most organizations rely on, such as data warehouses, transformation pipelines, BI dashboards, and governance tools.
They don’t inherently understand your table relationships, dashboard logic, or the access rules tied to your data warehouse. As a result, teams often need to build additional layers just to make commercial LLMs usable in a data context. These include ways to retrieve metadata, inject context into prompts, and enforce access controls. Even with these efforts, hallucinated SQL, inconsistent responses, and permission gaps remain common, especially without native support for RBAC or lineage context.
One way to mitigate these issues is to start with metadata, lineage, and governance, rather than bolt them on afterward. That’s the approach Secoda takes. Rather than treating AI as an external tool that must be integrated, Secoda embeds it within the existing data catalog and governance layer. This gives the AI access to ownership metadata, schema changes, data quality scores, and usage patterns from the start.
Instead of requiring custom infrastructure to enforce access and surface the right context, Secoda provides built-in support for safe query generation, permission-aware responses, and metadata filtering. For organizations using tools like Snowflake, this means AI can reason over masking policies, tags, and freshness signals without additional engineering effort.
In short, where commercial LLMs require complex infrastructure to be useful in data, Secoda AI is designed to work out of the box with your stack, your access controls, and your existing metadata. That makes the difference between experimentation and dependable results.
Tools like Notion AI and Glean are designed to surface internal knowledge across documents, wikis, and files. They excel at navigating company policies, product specs, and other unstructured content. But they are not built to work with structured data. Understanding SQL, parsing warehouse schemas, or interpreting dbt logic falls outside their scope.
These tools treat data references the same way they treat text in a slide deck or a PDF. They can find where something was mentioned, but they don’t understand how tables join, how metrics are calculated, or whether a source is still actively used. They are optimized for search, not for data reasoning.
Secoda takes a fundamentally different approach. It treats data not as unstructured text, but as structured entities connected by lineage, ownership, transformations, and usage. This makes it possible for AI to generate more accurate outputs, because it understands relationships between tables, how definitions flow across layers of the stack, and where documentation lives.
Without this context, even strong search tools can surface misleading information. They can’t verify joins, check freshness, or apply governance rules. Secoda mitigates these risks by embedding AI directly within the structured metadata layer, enabling context-aware answers that reflect the real shape of your data environment.
Some organizations explore building internal AI tooling to gain more control over data, user experience, or security. In practice, this usually means building on top of foundation models from providers like OpenAI or Anthropic, not training models from scratch. While this path can offer flexibility, it also comes with familiar tradeoffs.
Building in-house requires engineers to develop and maintain custom infrastructure. That includes integrating metadata, enforcing access controls, designing prompt flows, and connecting AI outputs to existing systems. These tools must be tuned over time, monitored for quality, and adapted as the data environment evolves. Every layer introduces additional costs, from platform engineering to support, and adds long-term maintenance responsibilities.
More importantly, this effort pulls engineering time away from core initiatives. Most organizations are not in the business of building and maintaining AI tooling. Their goal is to improve data access, accelerate insights, and maintain governance. Recreating what integrated platforms already provide often results in higher costs and slower outcomes.
This is where buying can offer a strategic advantage. Platforms like Secoda embed AI directly into the data catalog, using existing metadata, RBAC policies, and documentation to generate accurate, secure, and context-aware outputs. There is no need to manage embedding infrastructure, stitch together retrieval logic, or duplicate access control systems.
Integrating AI into data workflows is already complex. Choosing a platform that comes with governance and context built in allows teams to move faster, reduce total cost of ownership, and focus on outcomes instead of internal tooling.
Many tools in the modern data stack, including dbt and Snowflake, now offer AI-powered features directly within their platforms. These capabilities can be helpful for localized tasks, such as writing SQL for a known schema, documenting a model, or detecting anomalies in a specific table. Within their own environments, they are well-tuned for narrow use cases.
However, their scope is limited to the data and logic each tool directly controls. These features do not understand how upstream transformations impact downstream dashboards, how definitions differ across teams, or how access should be enforced across systems. Without this broader context, they are not well-suited for questions that require reasoning across the full data workflow.
They also lack a unified interface for collaboration. Each tool manages its own insights in isolation, which forces users to switch between interfaces and rely on tribal knowledge to connect the dots. There is no shared space for capturing institutional knowledge or surfacing consistent guidance across tools.
Secoda helps fill this gap by offering a centralized workspace that is purpose-built for collaboration and context sharing. It includes built-in wikis to document how data is defined and used, connects information from across tools into a single view, and presents that context through a user-friendly interface. AI-generated suggestions and summaries appear alongside documentation, making it easier for users to interpret what they’re seeing without needing deep technical knowledge.
Secoda also brings this functionality into the tools teams already use, such as Slack and Chrome. Users can search documentation, ask questions, and get relevant results without switching contexts. By meeting users where they already work, Secoda makes data knowledge more accessible and easier to act on.
While native AI features in individual tools are useful for task-specific support, Secoda helps teams move beyond isolated actions to deliver complete, reliable answers grounded in shared context and organizational knowledge.
Secoda’s AI features are built on a foundation of business context: lineage, documentation, metadata, and governance. This foundation enables Secoda AI to deliver more accurate and context-aware outputs, minimizing many of the risks associated with general-purpose AI systems.
Secoda AI is designed to reflect how real data teams operate. Its architecture is powered by a multi-agent system, where each agent specializes in tasks like lineage parsing, query synthesis, or semantic search. These agents collaborate to interpret user questions, retrieve context from across the metadata graph, and reason through ambiguous or incomplete inputs before generating a response.
When a user asks a question, Secoda AI interprets the query using existing metadata. It references lineage paths, ownership information, access policies, and historical documentation to inform its response. For example, if asked about a column, it can identify how that column was created, how it is used downstream, who owns it, and whether its definition aligns with business logic. This context helps prevent hallucinated SQL and irrelevant results.
Secoda AI uses different query strategies depending on the nature of the request. For unstructured questions like “what does ARR mean,” it applies vector search to identify glossary terms or relevant documentation. When the question is more structured, such as “find tables without owners,” it uses catalog search with filters applied across the metadata graph. For complex queries like “list all finance dashboards using PII columns with missing documentation,” Secoda combines approaches to support multi-parameter filtering, sorting, and ranking.
Importantly, Secoda AI adapts as the data environment changes. It handles schema evolution and permission-aware access by validating results against the most current metadata and RBAC configuration. This ensures users only receive information they are authorized to access, and that outputs reflect the latest data definitions and lineage.
Every response is validated before execution. The system applies parameter checks, enforces access policies, and maps results back to metadata sources to ensure transparency and traceability. This is particularly important for organizations in regulated industries, where auditability is essential.
Secoda AI also learns over time. When a data team validates an answer, the system can store that interaction as a memory, reinforcing correct reasoning paths for similar future queries. This reinforcement loop allows the system to improve with use, reducing friction for recurring questions.
AI is not treated as a standalone assistant in Secoda. It is embedded directly into core platform features including documentation, search, catalog browsing, and monitoring. This means users can ask questions, explore assets, and take action all in the same environment, without switching tools or breaking context.
When questions involve metrics or trends, Secoda AI can also generate charts, allowing users to quickly explore patterns without writing SQL, exporting data, or switching into a BI tool. This eliminates the need to spin up dashboards for one-off questions, keeping exploration fast and lightweight within the same environment.
For organizations that prioritize governance, accuracy, and operational trust, adopting a metadata-aware platform like Secoda provides a more reliable and scalable path to automation. Rather than replacing data teams, Secoda AI enhances their workflows by mirroring how they operate through context-driven reasoning, structured problem solving, and collaborative context sharing.
✅ Fully supported
🔶 Partially supported / tool-dependent / with MCP
❌ Not supported
Once teams have explored the available options for AI tooling, the next step is deciding which approach can actually work in their environment. This isn’t just a question of which model performs best in a demo. It’s a decision about reliability, long-term scalability, and whether the system can operate effectively within the real-world constraints of your data stack.
Below are the core dimensions to evaluate, each closely tied to whether an AI assistant can deliver accurate, safe, and repeatable results.
AI tools must respect existing permission structures. If a user cannot access a table directly, they should not be able to query it through AI. Any system you consider should enforce role-based access control (RBAC), log usage activity, and provide an auditable history of how data is accessed. This is especially important in environments where data sensitivity varies across teams, and where trust depends on clear visibility into who sees what.
Many AI tools fail because they operate without the metadata needed to interpret a question properly. Without knowledge of schema, lineage, dbt models, tests, glossary terms, or transformation logic, answers often sound plausible but miss critical nuance. Effective systems need a unified context layer that brings together definitions, usage patterns, table popularity, and ownership. This context is essential not only for accuracy, but for making the AI system adaptable as your data changes.
Some tools require multiple infrastructure layers like vector databases, retrieval APIs, embedding services, and custom-built connectors just to surface results. Others may need manual configuration to adapt to the semantics of each tool in your stack. Teams should assess the ongoing operational burden, not just the initial setup. Systems that require extensive maintenance to keep metadata aligned with schema changes or access rules often lose value over time.
The total cost of running AI in production includes more than LLM usage. API calls, compute resources for retrieval pipelines, secure networking, and monitoring systems all add overhead. Especially at scale, the real cost comes from managing and maintaining these layers. Look beyond model licensing or token pricing and evaluate the full cost of ownership across your architecture.
LLMs can return SQL that appears correct but fails on execution, or worse, executes and returns misleading results. These issues often stem from incomplete metadata, outdated documentation, or schema drift. Business users may not know what was missed, and data teams may not be looped in to validate the response. This increases the risk of silent failure, especially in environments where data is used to guide high-impact decisions.
Creating value from AI is not just about the model itself. It depends on the environment the model operates in and the context it has access to.
Structured context allows AI to reason through questions in a way that reflects how data teams already work. When the system can reference lineage, schema details, usage patterns, ownership, and definitions, it becomes easier to generate results that are consistent and aligned with how your organization uses data.
Integrating AI into the tools and workflows your team already relies on helps maintain accuracy over time. When context is embedded from the beginning, AI systems can adapt to changes in pipelines, access controls, and documentation without losing relevance.
Scale alone will not define the next generation of AI in data. Teams that see lasting results will be the ones that prioritize integration with their existing governance model, access policies, and metadata systems. This foundation makes it possible to reduce risk while expanding access to insights.
Secoda follows this approach by embedding AI within the structure of your data environment. Instead of treating AI as a separate tool, it becomes part of how users search, document, and monitor data. The result is a system that produces reliable outputs grounded in the reality of your stack.
For teams focused on making AI practical and dependable, building with context is the first step toward outcomes they can trust. To learn how Secoda can support your team’s goals, reach out to our team to start a conversation.
Discover how data teams can move from AI experimentation to reliable outcomes. This guide explores the challenges of using LLMs with business data, why context matters, and how tools like Secoda embed governance, metadata, and lineage to deliver trustworthy AI outputs.
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