Artificial intelligence (AI) is now at the forefront of how enterprises work with data to help reinvent operations, improve customer experiences, and maintain a competitive advantage. It’s no longer a nice-to-have, but an integral part of a successful data strategy. The first step for successful AI is access to trusted, governed data to fuel and scale the AI. With an open data lakehouse architecture approach, your teams can maximize value from their data to successfully adopt AI and enable better, faster insights.
Why does AI need an open data lakehouse architecture?
Consider this, a forecast by IDC shows that global spending on AI will surpass $300 billion in 2026, resulting in a compound annual growth rate (CAGR) of 26.5% from 2022 to 2026. Another IDC study showed that while 2/3 of respondents reported using AI-driven data analytics, most reported that less than half of the data under management is available for this type of analytics. In fact, according in an IDC DataSphere study, IDC estimated that 10,628 exabytes (EB) of data was determined to be useful if analyzed, while only 5,063 exabytes (EB) of data (47.6%) was analyzed in 2022.
A data lakehouse architecture combines the performance of data warehouses with the flexibility of data lakes, to address the challenges of today’s complex data landscape and scale AI. Typically, on their own, data warehouses can be restricted by high storage costs that limit AI and ML model collaboration and deployments, while data lakes can result in low-performing data science workloads.
However, when bringing together the power of lakes and warehouses in one approach — the data lakehouse — organizations can see the benefits of more reliable execution of analytics and AI projects.
A lakehouse should make it easy to combine new data from a variety of different sources, with mission critical data about customers and transactions that reside in existing repositories. New insights and relationships are found in this combination. Also, a lakehouse can introduce definitional metadata to ensure clarity and consistency, which enables more trustworthy, governed data.
All of this supports the use of AI. And AI, both supervised and unsupervised machine learning, is often the best or sometimes only way to unlock these new big data insights at scale.
How does an open data lakehouse architecture support AI?
Enter IBM watsonx.data, a fit-for-purpose data store built on an open data lakehouse, to scale AI workloads, for all your data, anywhere. Watsonx.data is part of IBM’s AI and data platform, watsonx, that empowers enterprises to scale and accelerate the impact of AI across the business.
Watsonx.data enables users to access all data through a single point of entry, with a shared metadata layer deployed across clouds and on-premises environments. It supports open data and open table formats, enabling enterprises to store vast amounts of data in vendor-agnostic formats, such as Parquet, Avro, and Apache ORC, while leveraging Apache Iceberg to share large volumes of data through an open table format built for high-performance analytics.
By leveraging multiple fit-for-purpose query engines, organizations can optimize costly warehouse workloads, and will no longer need to keep multiple copies of data for various workloads or across repositories for analytics and AI use cases.
Finally, as a self-service, collaborative platform, your teams are no longer limited to only data scientists and engineers working with data, but now can extend the work to non-technical users. Later this year, watsonx.data will infuse watsonx.ai generative AI capabilities to simplify and accelerate the way users interact with data, with the ability to use natural language to discover, augment, refine and visualize data and metadata powered by a conversational, natural language interface.
Next steps for your data and AI strategy
Take the time to make sure your enterprise data and AI strategy is ready for the scale of data and impact of AI with an open data lakehouse approach. With watsonx.data, you can experience the benefits of a data lakehouse to help scale AI workloads for all your data, anywhere.
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