The enterprise is on the brink of a great transformation. It is about to become, in a sense, self-aware.

What do I mean by that? At the moment, enterprises learn about themselves by analyzing data they have captured, transformed and stored. Selecting this data defines what can be known and what cannot. The enterprise can only understand what it decides to understand in advance. It is not what I would call self-aware.

A truly self-aware enterprise, in my definition, is one that has access to any organizational data an ad hoc analysis might require. It’s one that can answer any question about itself — even questions no one had previously thought to ask. No detail of its operations should be beyond its reach.

This scenario is closer to reality than anyone would have thought just a few years ago, thanks to two crucial innovations. The first and foremost is the mass commercialization of generative AI (GenAI) — specifically the large language model (LLM), which has made it trivial to analyze data using natural language prompts. The second is the scalable vector data store. This not only holds enterprise data, but makes it available for semantic search in GenAI applications. Vector data stores are not new, but highly scalable stores are. And scale is important when you’re saving the amount of data a self-aware enterprise requires.

The ideal end state is to have all your enterprise data accessible to your AI.

This is the approach, generally known as retrieval-augmented generation (RAG), developers use to create AI-powered chatbots or expert question-answering systems. In this case, the enterprise itself is the subject: its regional sales, hiring records, expenses, correspondence, quarterly plans and product development timelines. Everything, in theory, can be queried through an LLM. Any question that could be asked of anyone in an enterprise can be answered through this interface.

Transitioning to the Self-Aware Enterprise

The basic components of a self-aware transformation are available today. Putting them in place requires care, preparation and the input of a cross-functional team. As you work together, here are a few principles to keep in mind.

Avoid Creating New Silos

Fragmentation has no place in the self-aware enterprise. Simplicity and elegance should be your watchwords. Your RAG application will probably need to work across a combination of vectorized data, which supports semantic search, as well as structured enterprise data, which will require some form of keyword search.

Ideally, you would deploy an AI agent that could analyze a prompt, decide “This part needs a semantic search, and that part is a SQL lookup,” retrieve the appropriate data, and stitch it together with an LLM.

Semantic search makes the biggest impact with unstructured data like text documents and multimedia files. Those are ideal candidates for vectorization. But a lot of useful material lives in traditional structured data. Think transactional records and time series data: orders, users, logfiles. You could deploy a dedicated vector database to store your embeddings, but maintaining data on separate systems tends to create friction and blind spots.

For most enterprises, a more efficient approach is to consolidate data onto a unified platform that can store data in vector and relational formats, and support semantic search along with keyword search and SQL queries.

Prioritize Security and Privacy

You’ll need a way to ensure different users have different levels of access to sensitive information. Unfortunately, there’s no silver bullet for this problem. The eventual solution will probably depend on AI. At the moment, most enterprises rely on a mix of role-based and attribute-based access control (RBAC/ABAC) to manage permissions. As much as possible, you want to unify identity, enforce permissions at the source and log everything. Prefer data systems that provide fine-grained controls for these functions and let you “bring your own cloud” (BYOC) to maximize consistency.

Pre-Process Unstructured Data Through LLMs Before Vectorization

For PDFs, slide decks and other unstructured formats, don’t just vectorize raw content. Use LLMs to extract and structure key information — such as summaries, tables and entities — to improve semantic quality and ensure more meaningful retrieval.

For instance, you might use a SQL-compatible database as the persistence and indexing hub for raw data, and Kafka and Flink to stream and process the data on its way to the LLM, which then extracts summaries and entities and stores them back in the SQL data store.

Build a Queryable Knowledge Graph

Once you’ve integrated your structured and unstructured data, consider building a knowledge graph that encodes semantic relationships and acts as a supplementary context source for LLMs. This graph can also reside in the unified data platform and expose SQL plus graph interfaces, enriching the quality of retrieval and reasoning during generation. In other words, process your structured data in advance to enrich semantic search on the unstructured data in your vector data store.

Modern SQL-compatible databases like TiDB can directly store graph data structures to support hybrid queries, which helps maintain strong consistency and high availability.

Invest in Observability for Data Quality and Query Behavior

During development, it’s critical to build observability into the data pipelines, including monitoring data cleanliness, the structure and evolution of the knowledge graph, and the effectiveness and performance of queries. This ensures the system remains trustworthy, explainable and easy to iterate on as enterprise usage scales.

Have a Plan for Data Quality

All data is not equal. Enterprise data stores inevitably contain large amounts of outdated, abandoned or contradictory documents, including wikis, PDFs, slide decks and prefinal drafts. Including them in the knowledge base without careful curation is a sure way to introduce noise, misinformation and confusion for downstream LLM applications. You’ll need a strategy to privilege canonical data sources and resolve conflicts.

Conclusion

The self-aware enterprise is within reach, but realizing it takes more than technology. It calls for architectural clarity, disciplined data hygiene and a commitment to breaking down silos — both technical and organizational. Enterprises that embrace this shift will gain something powerful: the ability to ask better questions, make faster decisions and operate with a level of introspective intelligence that was once unimaginable. In a world where advantage flows to the most informed, self-awareness isn’t just a virtue. It’s a competitive edge.

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