AI-powered vector databases and retrieval-augmented generation
Unstructured data is everything that doesn’t fit neatly into columns and rows, videos, images, audio, web pages, you name it. Managing this mess isn’t just hard. It’s evolving rapidly with AI used not just to process it, but to truly understand it. The smartest way forward right now? Use vector databases and retrieval-augmented generation (RAG).
Here’s the simple explanation. Traditional data systems operate on exact matches, searching for exact keywords or tags. With vector databases, you’re not doing that anymore. You’re training AI to grasp context, meaning, semantics. You feed the system a document or media file, and it doesn’t just remember the words, it understands what it means. Retrieval-augmented generation then pulls relevant data into generative language models for output that’s context-aware and incredibly useful. This setup is already powering intelligent search engines, responsive chatbots, and next-gen recommendation systems.
Anbang Xu, founder of AI video startup Jogg.AI and a former senior software engineer at Google, has seen this firsthand. His team has implemented AI-driven indexing and retrieval tools that essentially turn massive, chaotic piles of data into insights you can act on. Not theoretically, right now. If you want your business to extract value from unstructured formats, this kind of AI toolchain is what gets you real outcomes, not just infrastructure talk.
For C-suite leaders, this matters because it raises efficiency. You’re not hiring teams to manually label, sort, and query obscure files. Instead, you’re scaling insights straight from your unstructured data with meaningful accuracy. It makes your tech smarter, your operations faster, and your people more effective. That’s the kind of leverage worth investing in.
Adopt a schema-on-read approach for flexibility
Now let’s talk about how you handle unstructured or semi-structured data without slowing your team down. Traditional data systems require you to define a rigid structure upfront, schema-on-write. That works fine for financial records or CRM tables, but falls short when things get more unpredictable. Think logs, sensor streams, or any machine-generated data. You don’t always know what structure it should have. That’s where schema-on-read comes in.
Schema-on-read flips the script. The structure isn’t set until the data is actually accessed. You store raw data freely, and the schema gets applied only when needed, when you query it. This gives your teams room to explore, iterate, and adapt when data changes or grows in complexity. No more lengthy transformations just to get a glance at your data. It’s instant access to truth in its current state.
Kamal Hathi, Senior Vice President and General Manager at Splunk (a Cisco company), leads a company that deals with machine data at scale. He points out that schema-on-read removes rigidity, making complex data easier to work with on the fly. His context is telemetry logs, but the lesson applies broadly: flexibility in data handling equals speed in decision-making.
This is critical for business. You can’t afford to delay decision cycles because your data team needs weeks to restructure pipelines. Schema-on-read makes your organization agile. No matter the vertical, telecom, energy, mobility, or finance, it’s better to work with what you already have, rather than force data into outdated molds. Use the complexity to your advantage.
Integrate unstructured data with structured data on cloud platforms
Most enterprise data ecosystems are fragmented. Structured data, like customer profiles or transaction records, sits cleanly in relational databases. Unstructured data, emails, documents, logs, media, is scattered, often unmanaged. That creates silos. It limits how much value you can extract. If you’re serious about building data-driven outcomes across the company, you need to unify both data types. The best place to do that is in the cloud.
Modern cloud platforms are built to handle scale and complexity. You can store massive volumes of unstructured data alongside structured sources in real time, and process them together. Add metadata tagging and AI-driven classification, and suddenly these chaotic datasets become search-ready and usable across teams. This unified approach simplifies access, enhances governance, and enables fast, cross-silo analytics.
Cam Ogden, Senior Vice President at Precisely, makes a clear case for this. He’s seen organizations transform their decision-making capabilities by integrating structured and unstructured datasets in cloud platforms using AI-powered classification. Not only does this make data discoverable, but it also ensures governance standards and security policies remain enforced. That’s essential for compliance-heavy sectors, or any business with sensitive customer data in play.
From a leadership perspective, this isn’t just about smarter analytics. It’s about future-proofing core operations. Unstructured data is growing exponentially. If you’re not managing it in parallel with structured sources, you’re operating with limited intelligence. The cloud isn’t just flexible storage, it’s infrastructure that lets you act in real time with holistic visibility. That means faster strategic moves and a clearer path to automation across business functions.
AI-powered classification and indexing for data retrieval and compliance
If you don’t know where your data is or what it contains, you can’t use it. That’s a fundamental issue with unstructured data. Traditional methods of organizing it are outdated. Manual tagging doesn’t scale. Sorting through vast file systems wastes time, drains teams, and increases the risk of compliance failures. AI addresses this problem head-on through intelligent classification and indexing.
By training machine learning models and applying natural language processing (NLP), unstructured data can be automatically categorized, tagged, and indexed based on both content and context. Meaning: the system understands what the data is about without needing a human to label it. You extend your storage, whether it’s a data lake or object store, with searchable intelligence. That changes how fast your teams find what they need, and ensures critical information is handled correctly.
Adhiran Thirmal, Senior Solutions Engineer at Security Compass, says this automated classification drastically reduces human error while improving operational efficiency. He also points out that AI is especially effective at flagging and protecting sensitive data like personal identifiers or financial records, helping organizations stay compliant without constant manual oversight.
For the executive team, this is directly tied to risk management and scale. With regulatory pressure increasing around data privacy, being able to automatically surface, isolate, and govern sensitive information is not optional. It’s a strategic requirement. AI lowers the cost of compliance and increases your pace of execution. That’s the kind of advantage you need in fast-moving industries, where mistakes around data governance can cost millions.
Create a unified data platform to consolidate data types
The typical enterprise still runs on disconnected systems, structured data here, unstructured data there, with semi-structured formats scattered across different teams and tools. Each of these systems often comes with its own storage, security framework, and governance protocol. That division creates friction. It increases operational overhead and slows down data-driven initiatives. A unified, sovereign data platform addresses this by bringing all forms of data under one control plane.
With a single platform managing structured, semi-structured, and unstructured data simultaneously, organizations eliminate the need to juggle multiple databases or migrate data repeatedly between systems. You reduce data sprawl and duplication. The result is a consolidated, high-performance environment that simplifies access and oversight. This is especially crucial when workloads span analytical, transactional, and AI pipelines. Efficient integration across data types reduces latency in execution and boosts throughput across your technology stack.
At the infrastructure level, hybrid control planes give centralized visibility and governance, whether your data is stored on-premises or in multiple cloud environments. That’s not just a technical benefit, it’s a strategic move. You retain full control over where data resides, how it’s secured, and who can access it. It’s essential in today’s geopolitical and regulatory landscape, where data sovereignty and compliance requirements are increasingly complex.
Benjamin Anderson, Senior Vice President of Technology at EnterpriseDB, emphasized that unifying structured and unstructured data within a sovereign platform improves performance and reduces risk. He noted this model enables the quality-of-service needed to support demanding AI workloads and critical business operations, without adding layers of complexity.
For C-suite leaders, this move reduces both short-term operational friction and long-term compliance costs. It enables scale without fragmentation. You’re not left patching together point solutions as your data needs evolve. Instead, you’re investing in infrastructure that supports continuous innovation, across departments, markets, and use cases.
Key highlights
- Use AI and RAG for smarter data retrieval: Leaders should implement AI-powered vector databases with retrieval-augmented generation to make unstructured data searchable by meaning, enabling faster, more intuitive insights across vast content types.
- Prioritize schema-on-read for agility: Executives managing growing machine-generated data should shift to a schema-on-read model to reduce ETL workloads and support real-time, flexible data analysis without structural constraints.
- Unify data in the cloud for deeper insights: Organizations should integrate structured and unstructured data on cloud platforms to eliminate silos, enhance governance, and enable real-time analytics driven by AI classification and metadata tagging.
- Automate classification to reduce compliance risk: Adopting AI-driven classification and indexing lowers the cost of manual sorting, minimizes human error, and strengthens data privacy controls, crucial for regulatory compliance.
- Consolidate platforms to reduce complexity: Building a unified, sovereign data platform allows CIOs and CTOs to manage all data types centrally, decrease infrastructure friction, and streamline support for AI, analytics, and transactional workloads.
