A modern data infrastructure is essential for retailers looking to stay competitive today. Companies are abandoning more traditional, on-premises IT infrastructures and moving to more centralized “as a service” (XaaS) models of delivery enabled by cloud technologies, according to McKinsey. Aging on-premises infrastructures are unable to meet demands for agility and innovation, eating up too much time and too many resources for teams trying to maintain them.

The role of the CDO will not suffer the slow rise to prominence other emerging C-level roles have seen in previous years. Modern technologies designed to easily centralize, access, analyze, share, and monetize data have arrived. As a result, data has become the most powerful resource to drive an organization’s transformation. That puts CDOs at the wheel, their foot on the pedal, and their fellow execs holding on tight for the ride of their lives.

The lingering effects of the global pandemic are merging with inflation to create a perfect storm for retailers looking to find the right inventory stature for the seasons ahead. Companies are getting squeezed between rising supply chain costs and falling consumer confidence. To succeed in this volatile market, McKinsey suggests that retailers “accelerate decision-making tenfold.”

Accessing data from the manufacturing shop floor is one of the key topics of interest with the majority of cloud platform vendors due to the pace of Industry 4.0 adoption. Industry 4.0, also known as the Fourth Industrial Revolution, refers to the emerging trend of technological transformation in manufacturing and related industries.

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.