Apps are the way to democratize AI: to make it accessible to everyone and streamline customers’ experiences with faster time to insights. According to a recent IDC survey, AI applications is currently the largest category of AI software, accounting for roughly one-half of the market’s overall revenue in 2023.

In today’s world, innovation doesn’t happen in a vacuum; collaboration can help technological breakthroughs happen faster. The rise of AI, for example, will depend on the collaboration between data and development. We’re increasingly seeing software engineering workloads that are deeply intertwined with a strong data foundation.

Open source file and table formats have garnered much interest in the data industry because of their potential for interoperability — unlocking the ability for many technologies to safely operate over a single copy of data. Greater interoperability not only reduces the complexity and costs associated with using many tools and processing engines in parallel, but it would also reduce potential risks associated with vendor lock-in.

In Part 1 of “Retail Media’s Business Case for Data Clean Rooms,” we discussed how to (1) assess your data assets and (2) define your data structures and permissions. Once you have a plan on paper, you can begin sizing the data clean room opportunity for your business.

It’s hard to have a conversation in adtech today without hearing the words, “retail media.” The retail media wave is in full force, piquing the interest of any company with a strong, first-party relationship with consumers. Companies are now understanding the value of their data and how that data can power a new, high-margin media business. The two-sided network that exists between retailers and their brands turns into a flywheel for growth.

Today we’re excited to announce an expansion of our partnership with Microsoft to deliver a seamless and efficient interoperability experience between Snowflake and Microsoft Fabric OneLake, in preview later this year. This will enable our joint customers to experience bidirectional data access between Snowflake and Microsoft Fabric, with a single copy of data with OneLake in Fabric.

On April 24, we released Snowflake Arctic with a key goal in mind: to be truly open. In line with that goal, the Snowflake AI Research team is writing a series of cookbooks to describe how to pretrain, fine-tune, evaluate, and serve large-scale mixture-of-experts (MoEs) such as Arctic.

The emergence of generative AI (gen AI) heralds a new, groundbreaking era for advertising, media and entertainment. According to a recent Snowflake report, Advertising, Media and Entertainment Data + AI Predictions 2024, gen AI is going to transform the industry — from content creation to customer experience. The companies that will come out ahead during this time are those that most successfully and quickly supercharge their data strategy.

The pharmaceutical industry generates a great deal of identifiable data (such as clinical trial data, patient engagement data) that has guardrails around “use and access.” Data captured for the intended purpose of use described in a protocol is called “primary use.” However, once anonymized, this data can be used for other inferences in what we can collectively define as secondary analyses.

Time series data is everywhere. It captures how systems, behaviors and processes change over time. Enterprises across industries, such as Internet of Things (IoT), financial services, manufacturing and more, use this data to drive business and operational decisions. When using time series data to perform analytics and drive decisions, it’s often necessary to join several data sets.