Access to reliable, regulated data is necessary to power and scale AI, and it is the first step toward success

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

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.

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

To expand AI workloads for all of your data, everywhere, enter IBM Watsonx.data, a fit-for-purpose data store based on an open data lakehouse

With a shared metadata layer distributed across clouds and on-premises systems, Watsonx.data enables users to access all data through a single point of entry

Organizations can reduce costs associated with warehouse workloads by utilizing several query engines that are appropriate for the job at hand

Spend some time ensuring that your company data and AI strategy is prepared for the effect of AI and the scale of data

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