Systems | Development | Analytics | API | Testing

It’s 2023 and with the new year comes an opportunity to drive innovation, growth, and digital transformation with data in the face of ongoing economic turbulence. If Snowflake’s report, How to Win in Today’s Data Economy is any indication, data-driven organizations are poised to emerge as the winners of the year with 77% Data Economy Leaders, which is only 6% of those surveyed, experiencing annual revenue growth versus 36% of Data Economy Laggards, the lowest-performing survey group.

Data, data, data. It does seem we are not only surrounded by talk about data, but by the actual data itself. We are collecting data from every nook and cranny of the universe (literally!). IoT devices in every industry; geolocation information on our phones, watches, cars, and every other mobile device; every website or app we access—all are collecting data. In order to derive value from this avalanche of data, we have to get more agile when it comes to preparing the data for consumption.

Scania is at the forefront of a more autonomous, connected, electric future for the transportation industry. Find out why its Head of Data and Information Management uses data mesh—and Snowflake—to make it a reality. Scania is a global truck, bus, and industrial engine manufacturer and offers an extensive range of related services so its customers can focus on their core business.

Marketing data integration is the process of combining marketing data from different sources to create a unified and consistent view. If you’re running marketing campaigns on multiple platforms—Facebook, Instagram, TikTok, email—you need marketing data integration. Why? Because being able to assimilate data from different channels and across multiple marketing touchpoints gives you visibility into the overall impact of a campaign, event, or another marketing effort.

The Chief Data Officer is arguably one of the most important roles at a company, particularly those that aspire to be data-driven. CDO appointments and the elevation of data leaders have accelerated in recent years, and the role has morphed as perceptions of data have evolved. Responsibilities span strategy and execution, people and processes, and the technology needed to deliver on the promise of data.

Snowflake customers leverage the Data Cloud to bring all their data together and capitalize on the near-infinite resources of the cloud. But how can this data be used to look ahead? How can we use yesterday’s evidence to plan for tomorrow? The answer—time series forecasting. Time series forecasting is one of the most applied data science techniques in business. It is used extensively in supply chain management, inventory planning, and finance.

Since Snowflake’s inception, we’ve had the needs of our customers as our North Star, with a clear focus on security and governance of data. Early on we also committed to continuous innovations to improve performance and reduce latencies, and by virtue of our business model continuously improve the economics for our customers.

As data science and machine learning adoption has grown over the last few years, Python is catching up to SQL in popularity within the world of data processing. SQL and Python are both powerful on their own, but their value in modern analytics is highest when they work together.

Snowflake is committed to helping developers focus on building their apps and businesses rather than on infrastructure management. At this year’s Snowday, Snowflake announced a series of advancements that empower developers to do more with their data, enhancing productivity and unlocking new ways to develop applications, pipelines, and machine learning (ML) models with Snowflake’s unified data platform.

There’s a lot of talk in the market these days about data clean rooms, along with some confusion about what exactly a data clean room is and how it differs from data sharing methods. In this blog post, I’d like to shed some light on this topic.