Saturday, July 20, 2024

Impact of Data Lakehouses on AI Goal Outcomes

In order to help businesses reimagine processes, enhance customer experiences, and preserve a competitive advantage, artificial intelligence (AI) is increasingly at the forefront of how they use data. It is now a crucial component of a successful data strategy rather than just a nice-to-have. Access to reliable, regulated data is necessary to power and scale AI, and it is the first step toward success. Your teams can get the most out of your data with an open data lakehouse design strategy, enabling better, quicker insights and a successful adoption of AI.

Why does AI require a lakehouse architecture for open data?

Take this into consideration: According to an IDC prediction, global spending on AI will reach $300 billion in 2026, with a 26.5% compound annual growth rate (CAGR) from 2022 to 2026. Even though two thirds of respondents indicated they used AI-driven data analytics, the majority of them claimed that less than half of the managed data was accessible for these analyses. In fact, according to an IDC DataSphere study, only 5,063 exabytes (EB) of data (47.6%) will be examined in 2022 out of the 10,628 exabytes (EB) of data that IDC predicted would be beneficial if analyzed.

In order to grow AI and meet the issues presented by the complex data landscape of today, a data lakehouse architecture combines the performance of data warehouses with the flexibility of data lakes. Data lakes can produce low-performing data science workloads, whereas data warehouses are typically constrained by high storage costs that hinder AI and ML model collaboration and deployments.

However, enterprises can benefit from more dependable analytics and AI project execution by combining the power of lakes and warehouses in one strategy the data lakehouse.

It should be simple to merge mission-critical data about customers and transactions that are stored in existing repositories with fresh data from a range of sources using a lakehouse. This combo reveals new relationships and insights. Additionally, a lakehouse can provide definitional metadata to guarantee clarity and consistency, allowing for more reliable, controlled data.

All of this is in favor of using AI. And to access these fresh big data insights at scale, AI both supervised and unsupervised machine learning is frequently the best or even the only option.

How does AI assist an open data lakehouse architecture?

To expand AI workloads for all of your data, everywhere, enter IBM, a fit-for-purpose data store based on an open data lakehouse. is a component of IBM’s Watsonx AI and data platform, which enables businesses to grow and accelerate the effect of AI throughout the organization.

With a shared metadata layer distributed across clouds and on-premises systems, enables users to access all data through a single point of entry. It supports open data and open table formats, allowing businesses to store enormous amounts of data in vendor-neutral formats like Parquet, Avro, and Apache ORC and communicate massive amounts of data using Apache Iceberg’s open table format designed for high-performance analytics.

Organizations can reduce costs associated with warehouse workloads by utilizing several query engines that are appropriate for the job at hand. They will also no longer need to maintain multiple copies of the same data across repositories for analytics and AI use cases.

As a self-service, collaborative platform, your teams’ access to non-technical users means that they are no longer restricted to just data scientists and engineers working with data. Later this year, will integrate generative AI capabilities to simplify and expedite the way people engage with data, with the ability to utilize natural language to discover, augment, improve and visualize data and metadata powered by a conversational, natural language interface.

Your data and AI strategy’s next points

Spend some time ensuring that your company data and AI strategy is prepared for the effect of AI and the scale of data. You may benefit from a data lakehouse with to scale AI workloads for all of your data, everywhere.

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