Exploring Data Management in the Age of AI

If AI is the engine of the future, data is its fuel. As enterprises move to inferencing, they are relying on having data that is clean, clear, and ready to be deployed in AI applications.

Unfortunately, many enterprises face data that doesn’t meet these requirements. Data may be stored in silos throughout an enterprise and in various far-flung geographic locations. It may exist in formats that are both structured and unstructured: Tabular data from accounting can’t mix with documents, images, and social media messages from marketing. Even data within the same department might feature duplicated, outdated, or erroneous information that must be weeded out before any dataset is ready for processing.

Unless companies can overcome these challenges, efforts to deploy AI can quickly devolve into delay-ridden projects that erase ROI and doom AI's adoption. Data becomes a bottleneck rather than the lifeblood of AI applications.

What’s In This Report

In Data Management in the Age of AI, we take a close look at the specific requirements of AI data pipelines, while outlining how a lot of enterprise AI data today fails to meet them.

Fortunately, the demands of AI have made fixing data problems a matter of urgency, particularly for vendors of storage products, which play a central role in AI infrastructure. These systems are evolving into data management platforms alongside other solutions aimed at preparing data for AI.

We present a data management taxonomy that includes storage platforms alongside HPC platforms, object storage systems, and other solutions. And we explore proposed remedies in depth, citing specific products and services that offer ways to corral data for use in AI.

Here are some highlights from the report:

Good data is essential to enterprise AI. Reaping the rewards of AI requires data that is clean, clear, and ready to be transformed into effective machine-readable format. But many enterprises are struggling to meet these requirements.
HPC storage systems formed the basis for today’s high-end AI data management systems. DDN, VAST Data, and WEKA have extended their products to fit accelerated computing and AI workflows, realizing significant performance improvements.
Enterprise storage products are evolving into data management platforms. Whether on-premises or in the cloud, or both, traditional storage vendors have adopted a range of technologies to prepare data for AI workflows.
Data management platforms are rebranding as AI platforms. Databricks and Snowflake have added vector search, integral models, and other features to support AI development.
No single solution is comprehensive. While many products and services perform a range of data management functions, enterprises will need combinations plus their own efforts to address AI workflows.

Some companies highlighted in this report: Attimis, Backblaze, Cloudian, Confluent, CTERA, DataCore Software, Databricks, Datadobi, dbt Labs, DDN, Dell, Everpure (formerly Pure Storage), F5, Fivetran, Hammerspace, Hitachi Vantara, HPE, IBM, Informatica, Komprise, Matia, Matillion, MinIO, Nasuni, NetApp, NVIDIA, Panzura, PEAK:AIO, Qumulo, Redpanda, Scality, Snowflake, UnifyApps, VAST Data, VDURA, Vultr, Wasabi, WEKA