Artificial intelligence (AI) has become a critical element in the data strategy of enterprises looking to improve operations, enhance customer experiences, and stay competitive. To enable successful AI adoption and gain valuable insights, organizations must have access to trusted, governed data. An open data lakehouse architecture offers a solution to maximize the value of data and enable AI integration and accelerated insights.

Why an Open Data Lakehouse Architecture is Essential for AI

In a forecast by IDC, it is estimated that global spending on AI will surpass $300 billion by 2026, with a CAGR of 26.5% between 2022 and 2026. However, most organizations struggle with the availability of data for AI-driven analytics. According to IDC, only 47.6% of the data under management was analyzed in 2022. This limitation can hinder the potential impact of AI on businesses.

A data lakehouse architecture combines the strengths of data warehouses and data lakes to address the complexities of today’s data landscape and support AI scalability. While data warehouses may have high storage costs, limiting collaboration and AI model deployments, data lakes can pose challenges for data science workloads. By integrating lakes and warehouses into a unified approach, organizations can achieve more reliable execution of analytics and AI projects.

The incorporation of new data from varied sources and the combination of mission-critical data with a lakehouse architecture enable the discovery of new insights and relationships. Additionally, the structured metadata introduced by a lakehouse ensures clarity and consistency, promoting trust and governance in data management. These factors contribute to the effective use of AI, making it possible to unlock valuable big data insights at scale.

How an Open Data Lakehouse Architecture Supports AI

IBM’s watsonx.data is a purpose-built data store that operates on an open data lakehouse architecture, allowing organizations to scale AI workloads across all data sources and locations. As part of the IBM AI and data platform, watsonx, watsonx.data simplifies data accessibility by providing a centralized entry point and deploying a shared metadata layer across clouds and on-premises environments.

With support for open data formats like Parquet, Avro, and Apache ORC, and the utilization of Apache Iceberg for large-scale data sharing, watsonx.data offers flexibility to store vast amounts of data in vendor-agnostic formats. The platform also leverages multiple query engines to optimize costly warehouse workloads, eliminating the need for duplicating data across repositories for different analytics and AI use cases.

Furthermore, watsonx.data enhances collaboration by transforming into a self-service platform, enabling non-technical users to interact with data alongside data scientists and engineers. Future updates will introduce generative AI capabilities powered by natural language interfaces, making it easier for users to discover, augment, refine, and visualize data and metadata.

Next Steps for Your Data and AI Strategy

By adopting an open data lakehouse approach with watsonx.data, enterprises can effectively address the challenges of data scalability and maximize the potential impact of AI. To learn more about the benefits of a data lakehouse architecture and explore watsonx.data, it is recommended to request a live 30-minute demo. Additionally, access the IDC study on the data lakehouse approach to gain a deeper understanding of its implications.

Frequently Asked Questions (FAQs)

1. What is an open data lakehouse architecture?

An open data lakehouse architecture combines the features and capabilities of data warehouses and data lakes into a unified approach. It allows organizations to benefit from the performance and reliability of data warehouses and the flexibility and scalability of data lakes.

2. Why is an open data lakehouse architecture necessary for AI?

An open data lakehouse architecture provides organizations with access to trusted and governed data, which is essential for successful AI adoption. It enables the integration of AI into data strategies and facilitates faster and more accurate insights.

3. How does watsonx.data support AI workloads?

Watsonx.data, built on an open data lakehouse architecture, offers a data store that allows organizations to scale AI workloads across all data sources and locations. It provides a centralized entry point, supports open data and table formats, and employs multiple query engines to optimize warehouse workloads.

4. Can non-technical users leverage watsonx.data for data analysis?

Yes, watsonx.data is designed as a self-service platform that promotes collaboration among both technical and non-technical users. It facilitates data exploration, augmentation, refinement, and visualization, making it accessible to a broader range of users.

5. How can I learn more about watsonx.data and its benefits?

To gain a deeper understanding of watsonx.data and explore its benefits, it is recommended to request a live 30-minute demo. Additionally, accessing the IDC study on the data lakehouse approach can provide additional insights into the significance of this architectural approach.

Summary

An open data lakehouse architecture is essential for organizations looking to leverage AI effectively and gain valuable insights from their data. By combining the strengths of data warehouses and data lakes, an open data lakehouse architecture enables reliable analytics and AI execution. IBM’s watsonx.data offers a purpose-built data store on an open data lakehouse, supporting AI workloads across various data sources and locations. With watsonx.data, organizations can unlock the potential of AI, enhance collaboration, and achieve success in their data and AI strategies.