From predictive analytics and machine learning to generative AI, data is the lifeblood that fuels the development and efficacy of AI systems. At the same time, data-related issues remain a key obstacle across the training, deployment, scaling, and return on investment of initiatives at many enterprises. These issues include the availability and quality of data, the volume and speed at which data needs to be processed, as well as the protection of data.
Ultimately, AI depends and thrives on large, diverse data sets. To succeed, enterprises need fast access to data across different data stores, clouds, locations, and vendors. Equally important, enterprises must have effective safeguards in place to ensure that clean, highquality data is being used in a manner that does not create privacy and compliance risks. A strong data foundation for AI must balance these requirements.
It is no secret that enterprises are accelerating their AI plans this year, and modernizing data infrastructure to support new use cases is a top priority. Data and analytics leaders are looking to the cloud with an eye on new databases and data platforms, architecture patterns, and tools to improve their ability to ingest, integrate, govern, and manage data with speed, scale, and flexibility. This also includes adding capabilities in areas such as data preparation, data observability, data products, collaboration, graphs, and vectors.
To help enterprises gain a deeper understanding of the key consideration and best practices for building a strong data foundation for AI, DBTA is publishing a special thought leadership report in April 2024. The report will be marketed to more than 100,000 qualified subscribers at organizations across North America. Sponsors get to promote their solutions and receive all the leads produced from downloads, screened, and cleansed.
Download the prospectus below for further details.