IBM’s data strategy for generative AI

The world of data is on the brink of a revolution, driven by the potential of generative AI. However, the reality is that many organizations are struggling to keep pace. Fragmented data stacks, data silos, and the pressure to deliver on AI's potential are exposing a fundamental flaw: a lack of enterprise data readiness.

The effectiveness and trustworthiness of AI are inextricably linked to the quality and governance of its underlying data. Yet, building AI models without high-quality data is akin to driving a car while looking at the rearview mirror, it's a futile endeavor.

Furthermore, the success of generative AI hinges on the ability to harness both structured and unstructured data. The exponential growth of unstructured data is creating a new set of challenges for data leaders. Managing unstructured data poses significant challenges, including the tedious process of making it usable, coupled with difficulties in identifying, classifying and organizing it. The sheer variety of formats and volumes of unstructured data result in complex data management environments and can create security risks, cost overheads and issues with storage, interpretation and compliance. Ensuring quality, accuracy, and authorized access further complicates the data management landscape. The need to efficiently harness this data for generative AI models has led to a quest for next-generation data strategies.

In this blog, we'll explore the challenges and opportunities ushered in by the data revolution. We'll explore strategies and solutions aimed at helping organizations break down data silos, harness the potential of unstructured data, and lay a solid foundation for building high-quality generative AI models. Whether you're a data scientist, business leader, or IT professional, this blog will provide valuable insights into overcoming common hurdles and unlocking the true power of your data.

To harness a broad information base for building AI models and AI-driven decision making, organizations must find ways to address data silos and enable unified access to data, independent of where it resides. Unstructured data, in particular, poses a unique set of challenges. While it is critical to AI success, it remains difficult to ingest, store, process and govern. Overcoming these challenges, requires scalable data stores capable of handling the volume and velocity of unstructured data. Additionally, there is a need for efficient methods to unify, curate, and prepare data efficiently across hybrid cloud environments.

IBM watsonx.data is an open, hybrid data lakehouse to power your AI journey. It enables data users to access and share a single secure copy of enterprise data through a unified entry point, wherever the data resides—across clouds and on-premises environments. You can connect to different data sources and formats without having to move or transform any data to get started. This approach avoids data-duplication and accelerates time-to-value. For example, you can seamlessly integrate with various databases and data warehouses, including Db2, Db2 Warehouse, Netezza, and Informix across hybrid cloud environments. This opens the door to leverage petabytes of data for use in AI applications that lives in databases, data lakes, document management systems, applications, and many other data systems.

IBM watsonx.data is also multi-modal and multi-engine, which enables the ingestion, cataloging and processing of structured and unstructured data in a variety of formats. Vector embedding and retrieval provides the foundation for conversational experiences based on Retrieval Augmented Generation (RAG) patterns grounded in enterprise data and documents. IBM watsonx.data provides this essential capability based on open-source Milvus, a highly scalable and flexible vector store. Additionally, extracting entities, values and relationships from documents and storing that information in IBM watsonx.data generates richer and more accurate responses to RAG-based solutions.

Once data is unified in a data lakehouse, it must be transformed, cleansed, and cataloged to be effectively utilized in AI applications. This requires robust capabilities to inventory data, assess its quality, safeguard it from unauthorized usage, and track its lineage to ensure AI applications rely on trusted and governed data. However, given today’s complex data landscapes, data practitioners often struggle to govern and curate high-quality data necessary for AI use cases due to data silos, inaccessibility and interpretability issues.

Generative AI requires organizations to ingest and prepare unstructured data alongside structured data. Consequently, data engineering teams need the flexibility to mix and match various integration styles including real-time streaming for analytical and operational use cases.

To address these challenges, IBM Data Fabric is specifically designed for the world of generative AI. It offers a set of data integration and data intelligence tools for preparing data for generative AI—ensuring structured and unstructured data readiness for your generative AI initiatives. It helps improve the accuracy, relevancy, and privacy of AI outcomes. With data intelligence tools, organizations gain comprehensive AI-powered capabilities for discovering, governing and sharing trusted data products across the organization to accelerate AI-driven outcomes. Knowledge Accelerators help organizations accelerate data governance initiatives for AI by helping align multiple regulations and industry standards with data. The upcoming new data integration features enable ingestion of unstructured data alongside structured data so there is a single control plane instead of managing and orchestrating multiple tools.

IBM Data Fabric is hybrid-ready by design. It can run anywhere—on-premises, or on any cloud. It also integrates across hybrid data planes, with any style of data integration. Data Fabric understands the hybrid realities of today’s data architectures. It is a ready-made solution to help you deliver on the next generation of your data strategy.

With IBM watsonx.data, users get a suite of modern dataops tools and extensible governance with the Common Policy Gateway, which integrates seamlessly with IBM and third-party policy engines like Apache Ranger, to respect existing user and data authorization policies and governance decisions, allowing for granular access control through row-level filtering and column masking. Real-time enforcement of governance policies ensures alignment with organizational frameworks and enhances data security. This will be critically important to reconcile the coarse-grained document and folder-level governance models of unstructured data and document repositories with the more fine-grained models in place for structured data. With intelligent search capabilities, users can easily discover relevant data tables and views using natural language queries for enhanced productivity.

Overcoming data silos and unlocking unified data access is critical for organizations looking to harness the power of generative AI. By using IBM watsonx.data and IBM Data Fabric, organizations can improve data quality and governance, enhance data integration and analytics capabilities, and build more accurate and trustworthy AI models.

Get your data ready for AI with a unified solution that is hybrid by design and enables data intelligence to drive unprecedented productivity and efficiency gains for data teams. Ensure data readiness across both structured and unstructured data and scale the delivery of trustworthy data products to accelerate AI-driven innovation.

Learn more about IBM’s data strategy for generative AI.