In modern enterprises, the exponential growth of data means organizational knowledge is distributed across multiple formats, ranging from structured data stores such as data warehouses to multi-format data stores like data lakes. Information is often redundant and analyzing data requires combining across multiple formats, including written documents, streamed data feeds, audio and video. This makes gathering information for decision making a challenge.

Data is one of the most valuable commodities an organization has. Every company stores and manages a substantial amount of information. But how do you gain revenue from it? Here, we discuss three ways you can monetize data with an embedded analytics investment.

For data engineers, the Generative AI (Gen AI) era is a transformative shift in how we approach data architecture and analytics. Professionals at the forefront of this shift will be gathering in San Francisco, at the Data+AI Summit June 10-13. Attendees will be exploring tools that integrate with Databricks Intelligent Data Platform that decrease data management costs and improve data's impact on business outcomes.

A look at how to augment LLM results with retrieval augemented generation (RAG) and vector search, directly from BigQuery.

Making operational decisions in a tight timeframe is critical to the success of an organization. Real-time data ingestion enables quicker data availability, in turn enabling timely decision-making. Real-time ingestion is foundational to our digital transformation at Discover Financial Services. As a senior manager leading the streaming and real-time data platforms at Discover, I don’t want to be in the data replication business manually.

Despite the transformative potential of AI, many financial planning and analysis (FP&A) teams are hesitating, waiting for this emerging technology to mature before investing. According to a recent Gartner report, a staggering 61% of finance organizations haven’t yet adopted AI. Finance has always been considered risk averse, so it is perhaps unsurprising to see that AI adoption in finance significantly lags other departments.

Imagine transforming raw business data into actionable insights with minimal effort. This is the value proposition of automated analytics, a form of data analytics fast becoming more accessible among modern business intelligence (BI) and analytics software solution vendors. Independent software vendors (ISVs) and enterprise organizations at the cusp of investing in analytics struggle with manual data processes that are time-consuming and prone to errors.

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.

With integration to Vertex AI, you can use multimodal LLMs like Gemini Pro 1.0 and Gemini Vision Pro 1.0 in BigQuery to create marketing campaigns.

For any product team with a Software as a Service (SaaS) application, the significance of embedding capable, reliable analytics is well understood. But it’s important to remember how valuable these tools are for your users’ experience, too. Having fast access to data and easy ways to explore it is, as you know, key to monitoring business performance, finding trends, sharing insights, and improving your core product. Today’s business user also wants more when they use your product.