Data Management Glossary
Unstructured Data for AI
Why Does AI Need Unstructured Data?
Unstructured data is the fuel for Artificial intelligence (AI) and there is growing demand to use AI and machine learning techniques to analyze, process, and derive insights from unstructured data. Unstructured data is data that doesn’t have a predefined schema or organized format, such as:
- Text: Emails, social media posts, chat logs, documents.
- Images: Photographs, scanned documents, and graphics.
- Audio: Voice recordings, podcasts, and call recordings.
- Video: Surveillance footage, movies, or user-generated content.
- Sensor Data: Logs from IoT devices without a clear structure.
Most of this unstructured data is storage as files and objects in the enterprise.
Read: Unstructured Data Growth and AI are Changing Executive Decision Making.
What are examples Unstructured Data for AI use cases?
Here are some examples:
Natural Language Processing (NLP):
- Sentiment analysis on social media or reviews.
- Chatbot development for automated customer support.
- Summarizing or translating text content.
Computer Vision:
- Image recognition for tagging photos or medical imaging diagnostics.
- Video analysis for facial recognition or surveillance.
Speech Recognition:
- Transcribing spoken words into text.
- Enhancing virtual assistants like Alexa or Siri.
Predictive Analytics:
- Identifying patterns in unstructured logs or communication data.
- Forecasting trends based on textual or visual insights.
Recommendation Systems:
- Using text reviews and user-generated content to suggest products or services.
Knowledge Extraction:
- Extracting actionable information from documents, reports, or multimedia data.
What are the common AI technologies for Unstructured Data?
- Deep Learning: Particularly neural networks like CNNs for images and RNNs/transformers for text.
- Transformers Models (e.g., BERT, GPT): Used for advanced text generation, classification, or summarization tasks.
- OCR (Optical Character Recognition): Converts images of text into machine-readable formats.
- Audio Processing Models (e.g., WaveNet): Analyze audio signals for transcription or sentiment analysis.
Challenges - Data Cleaning and Preprocessing: Handling noise, inconsistencies, and errors in raw data.
- Scalability: Managing large datasets, e.g., video archives or massive text corpora.
- Interpretability: Making AI outputs understandable and actionable.
- Integration: Combining structured and unstructured data for holistic insights.
AI for unstructured data is becoming increasingly critical, as 80-90% of data generated today is unstructured, according to IDC. Tools like OpenAI’s models, Google Cloud AI, and AWS AI services are instrumental in enabling businesses to leverage unstructured data effectively.
What is the connection between unstructured data management and AI?
At the end of 2024, Komprise CEO and cofounder Kumar Goswami made the following predictions for AI and data:
- IT leaders will get creative to deploy AI on a budget (see the survey)
- Unstructured data governance processes for AI will mature
- Systematic data ingestion for AI will be the first data storage mandate
- Hybrid cloud persists, mandating deep intelligence on data and costs
- Role of storage administrator evolves to embrace security and AI data governance
He noted:
AI mania is overwhelming, but so far, enterprise participation has been largely led by employees who are using GenAI tools to assist with daily tasks such as writing, research and basic analysis. AI model training has been primarily the responsibility of specialists, and storage IT has not been involved with AI. But this will change swiftly in the coming year. Business and public sector leaders know that if they get left behind in the AI Gold Rush, they may lose market share, customers and relevance. Corporate data will be used with AI for retrieval augmented generation (RAG) and inferencing, which will constitute 90% of AI investment over time. Everyone touching data and infrastructure will need to step up to the plate as a broader set of employees start sending company data to AI. Storage IT will need to create systematic ways for users to search across corporate data stores, curate the right data, check for sensitive data and move data to AI with audit reporting. Storage managers will need to get clear on the requirements to support their business, departmental and IT counterparts.
What is unstructured data and why is it critical for AI?
Unstructured data includes documents, PDFs, images, videos, emails, and application files that do not reside in traditional databases. It represents the majority of enterprise data and contains the context AI needs to generate accurate, business-relevant insights.
For AI systems, especially Retrieval-Augmented Generation (RAG), unstructured data provides the “ground truth” used to answer questions. However, this data is typically fragmented across NAS, cloud, and object storage, poorly tagged, and difficult to govern.
AI performance depends directly on how well this data is:
- discovered
- enriched with metadata
- filtered for relevance and sensitivity
- prepared for retrieval
Komprise addresses this by creating what can be consider to be a virtual metadata lakehouse through its Global Metadatabase, enabling organizations to analyze and prepare unstructured data for AI without copying it.
NewYork-Presbyterian Achieves 96% Savings and 10x Faster AI Data Ingestion with Komprise
How are enterprises feeding unstructured data to AI?
Most enterprises use multi-stage ingestion pipelines to prepare unstructured data for AI, especially for RAG systems. These pipelines typically involve:
- collecting data from file shares, cloud, and applications
- parsing and extracting content from formats like PDFs and images
- chunking and structuring content
- tagging metadata (owner, date, sensitivity, type)
- generating embeddings and indexing in vector databases
However, these pipelines often fail due to poor data quality, lack of metadata, and lack of governance. The ingestion stage is widely considered the most critical part of AI pipelines because it determines the quality and trustworthiness of AI outputs.
Komprise simplifies and improves this process with:
- Smart Data Workflows: Automates sensitive data management and ingestion pipelines, including tagging, filtering, and routing subsets of unstructured data to AI systems
- Intelligent AI Ingest: Identifies and delivers only relevant, high-value file and object data for AI pipelines instead of bulk ingesting everything
- Global Metadatabase: Provides a unified metadata layer across all storage to drive intelligent selection, unstructured data classification and filtering
- KAPPA Data Services: Serverless infrastructure that allows for the development of custom data functions by automating infrastructure and execution across large unstructured datasets.
This approach reduces cost, improves accuracy, and accelerates successful AI deployment by ensuring only the right data is fed into AI systems.
Why do most AI and RAG projects struggle with unstructured data?
Most AI initiatives fail not because of the models, but because of poor data preparation. Common challenges include:
- lack of visibility into enterprise data
- inconsistent or missing metadata
- ingestion of too much irrelevant or outdated content (see ROT data)
- inability to preserve context from complex file formats
- fragmented governance across systems
- sensitive data exposure risks (see sensitive data detection)
RAG pipelines are particularly sensitive to these issues because retrieval quality directly impacts AI output quality. Poor ingestion leads to hallucinations, irrelevant answers, and low trust.
Komprise Intelligent Data Management addresses these challenges by:
- indexing all unstructured data via the Global Metadatabase
- enriching metadata to improve filtering and retrieval
- using Smart Data Workflows to curate and prepare datasets
- applying Sensitive Data Management to detect and control PII/PHI
- enabling Intelligent AI Ingest to reduce noise and improve relevance
This ensures AI systems are built on trusted, curated, and governed data rather than raw, unfiltered content.
How does metadata improve AI accuracy and reduce hallucinations?
Metadata is the control layer for AI retrieval. It allows systems to filter and prioritize the most relevant and trustworthy content before it is used in AI responses.
In RAG pipelines, metadata enables:
- filtering by date, department, owner, or document type
- excluding outdated or duplicate content (learn more about Komprise potential duplicate reporting)
- prioritizing authoritative sources
- enforcing access controls and governance policies
Without metadata, AI systems rely purely on semantic similarity, which can surface incorrect or irrelevant content.
Komprise enhances AI accuracy by:
- building a Global Metadatabase across all storage systems
- enriching metadata with business context and usage patterns
- enabling metadata-driven filtering before ingestion
- orchestrating AI data pipelines using Smart Data Workflows
This results in higher-quality retrieval, improved answer accuracy, and more reliable AI outcomes. See Why Komprise?
How does Komprise prepare and deliver trusted unstructured data for AI?
Komprise provides an end-to-end platform for transforming unstructured data into AI-ready assets through a metadata-driven approach. Read the solution brief: Smart Data Workflows for AI.
Key capabilities include:
Smart Data Workflows
Automates data preparation tasks such as tagging, classification, movement, and AI pipeline integration.
Global Metadatabase
Creates a unified, continuously updated metadata layer across file and object storage, enabling discovery, filtering, and governance at scale.
Sensitive Data Management
Detects and governs sensitive data (PII, PHI, financial data) before it is exposed to AI systems.
Intelligent AI Ingest
Selects and delivers only relevant, high-value data to AI systems, reducing cost and improving signal-to-noise ratio.
KAPPA Data Services
Rapidly deliver custom data services, such as industry-specific metadata enrichment, without having to provision or manage the infrastructure to process the operation across large datasets.
Read: KAPPA: A Serverless Approach to Metadata Enrichment and Unstructured Data Management
Together, these capabilities enable a virtual metadata lakehouse approach to unstructured data management where enterprises:
- analyze data globally (across data silos)
- enrich and govern it
- move only what is needed
- deliver trusted unstructured data to AI
This reduces infrastructure cost, accelerates AI adoption, and ensures compliance while improving AI accuracy.