Struggling to get your data AI-ready?

Data as the key to competitive advantage

Your competitive advantage lies in your unique data. Unstructured data, which is data hidden in emails, PDFs, images and videos, is particularly valuable but very difficult to use. In order to leverage the full potential of their enterprise data for AI, companies need an intelligent data architecture to retrieve, prepare, and deliver both structured and unstructured data.

Although data is the fuel for AI, it is estimated that less than 1% of enterprise data is currently used by AI models. A full 90% of enterprise data is unstructured. A hybrid, open data lakehouse can help you optimize your AI and integrate your data into a variety of modern use cases. It combines all data types, simplifies integration and makes managing and controlling both structured and unstructured data easier than ever. It is therefore no surprise that it is becoming the preferred long-term data architecture for next-generation analytics and AI workloads.

Simplifying the data lifecycle for better AI

As the importance of enterprise data has grown, so have the challenges associated with it. The sheer volume of data can be overwhelming, and it is often stored in silos within organizations. Furthermore, new data variants have complicated integration, while poor-quality data has reduced the effectiveness of AI.

Generative AI (Gen-AI) can help solve these problems, but it requires robust and flexible data architecture. The limitations of retrieval-augmented generation (RAG) currently prevent companies from realizing the value of unstructured data for Gen-AI. So, how can unstructured data be integrated into Gen-AI and traditional analytical workloads? The answer lies in using a hybrid, open data lakehouse.

A data lakehouse is an emerging architectural concept that combines the flexibility of a data lake with the performance and structure of a data warehouse. Most lakehouse solutions offer a lightweight query engine combined with a metadata governance layer over low-cost storage. These intelligent metadata layers facilitate the categorization and classification of unstructured data, such as video and voice data, as well as semi-structured data, including XML, JSON and emails.

watsonx.data: Your data redefined

IBM watsonx.data is the only hybrid, open data lakehouse designed for enterprise AI and analytics. It enables you to manage the entire lifecycle of enterprise data for AI within your data lakehouse, supporting the next generation of AI and AI-powered BI applications. Watsonx.data simplifies and scales the integration, management and governance of structured and unstructured data across on-premises, cloud and multi-cloud environments. It forms part of an open, modern data stack that leverages innovative open-source technologies and integrates with your existing data environment, without locking you into a specific vendor.

IBM watsonx.data allows you to access, prepare and deploy your company’s unstructured data, achieving an AI that is 40% more accurate than traditional RAG. Watsonx.data is special because:

1. Hybrid and open for accessing data, regardless of where it is stored, and for provisioning in local, cloud, and multi-cloud environments with interoperability with your existing ecosystem and data investments.
2. Workload-optimized with multiple purpose-built query engines, including the new open-source product Apache Gluten Enhanced Spark, to optimize workloads for cost and performance.
3. Gen AI-enabled with integrated data fabric capabilities – watsonx.data Integration and watsonx.data intelligence – all within the data lakehouse to prevent the creation of additional data silos.

Now you can scale and automate:

1. Capture your structured and unstructured data from a variety of new source systems, including FileNet, Box, Google Docs, and more.
2. Enrich your data semantically by creating vectorized embeddings and structured derivatives from extracted and normalized entities in your documents. This supports AI applications that understand positional context, relationships and calculations, delivering more accurate and complete results.
3. Manage your data with access controls inherited from the document source systems, right through to retrieval of your data for AI purposes, with PII annotations to prevent the disclosure of sensitive information.
4. Retrieve this data for a wide range of workloads, from BI to generic AI applications and agents.

Conclusion

The challenges businesses face in leveraging their data for generative AI are significant. The key to competitive advantage lies in the ability to effectively integrate and manage both structured and unstructured data. IBM watsonx.data offers a hybrid, open data architecture that enables companies to unlock the full potential of their data. By combining the flexibility and performance of a data lake with the structure of a data warehouse, organizations can utilize their data more efficiently and significantly enhance the accuracy of their AI applications. Watsonx.data ensures that businesses can capture, enrich, and manage their data to meet the challenges of the modern data landscape.

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