From business-wide objectives to long-term targets, every organization has specific metrics they need to track. One way to communicate those insights is a KPI dashboard. Key performance indicator (KPI) dashboards are a great way for executives to improve their management of strategic goals, and keep on top of changes, issues and trends in performance at a high-level, with many useful applications when used correctly, alongside other modern analytics tools.

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.

BigQuery numeric search indexes enable optimized lookups on INT64 and TIMESTAMP data types.

As I have explored in this “Sliding Doors” blog series, identifying the right door to create value with data can prove quite challenging - and once that door is opened, the journey ahead can seem daunting. But what if there was a way to make that journey a bit easier? Maybe it’s time to get back to basics… Who needs to hear this?

You’ve heard of throwing ideas at a wall until something sticks–as a product manager, you may find you’re doing the same with application features. For application teams, creating sticky applications that customers can rely on and continue using for years to come is key to maximizing revenue. Elements like intuitive interfaces, personalized experiences, seamless integrations, and valuable core functionalities all contribute to this stickiness.

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.

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.

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.

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.

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.