Snowflake Retail Data Cloud – Snowflake Retail Launches Data Cloud That Enables Collaboration, Delivers Exceptional Customer Experiences, and Optimizes Operations for Retailers (Graphic: Business Wire)
NO-HEADQUARTERS/BOZEMAN, Mont.–( BUSINESS WIRE )–Snowflake (NYSE: SNOW), a data cloud company, today announced the launch of Retail Data Cloud, which combines the Snowflake Data Platform delivered by Snowflake and partners. solutions and industry-specific datasets. The retail data cloud enables retailers, manufacturers, distributors, consumer packaged goods (CPG) vendors and industry technology providers to leverage their own data, access new data and collaborate seamlessly across the retail industry. Together, Snowflake and its ecosystem of partners are able to drive business agility, deliver exceptional, personalized customer experiences and optimize operations for businesses across sectors.
Snowflake Retail Data Cloud
Retail organizations are in the midst of a global shift in the way consumers, retailers and brands communicate. Obtaining and identifying the best data and insights to manage uncertainties in industry is increasingly difficult. The acceleration of digitization and e-commerce, customer expectations and transformation across the supply chain have increased the pressure on businesses to adapt. To meet the demands of this rapidly changing environment, the industry needs a platform that can break down data silos and provide secure and controlled access to data.
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Some of the largest customers in the retail and CPG industries are already using Snowflake’s retail data cloud to unlock business agility and innovation. Examples of customer use include:
“As a global CPG company, the ability to respond quickly to market changes is critical to how we optimize operations in times of uncertainty,” said Jorge Balestra, Global Head of Machine Learning Operations at Kraft Heinz. “Snowflake’s Retail Data Cloud allows us to connect data from multiple sources across purchase orders, inventory and manufacturing, as well as collaborate on data in near real-time with partners like Albertsons, in one place for end-to-end delivery. Chain visibility that leaves zero ambiguity in our business. Snowflake Kraft is critical to Heinz’s digital transformation and allows us to focus resources on innovation for our customers.
Within the Retail Data Cloud, customers can access industry-specific solutions to leverage best practices, reduce time to value and increase overall impact. Partners announcing the new out-of-the-box solution include:
“Snowflake’s retail data cloud suite of integrated solutions for the retail and CPG industry can fuel the next wave of transformation by enabling data access, management and sharing,” said Rosemary Hua, Retail & CPG. Snowflake is the leading industry GTM. “Retailers and CPGs can now partner in the data cloud and work with each other to take data-driven action and better serve their customers in a rapidly changing environment.”
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** Until January 31, 2022. Based on the 2021 Fortune 500 list. The number of our Fortune 500 customers is subject to annual updates to the Fortune 500 list as well as acquisitions, mergers, spin-offs and other market adjustments. Activities with respect to these customers.
This press release contains express and implied forward-looking statements, including Snowflake products, services and technology offerings that are under development or not generally available, and statements regarding the integration and/or interoperability of such products, services and offerings with third parties. Products and services. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under “Risk Factors” and elsewhere in Snowflake’s quarterly reports on Form 10-Q and annual reports on Form 10-K filed with the Securities and Exchange. commission. In light of these risks, uncertainties and assumptions, actual results could differ materially and adversely from those anticipated or anticipated statements. Consequently, you should not rely on any forward-looking statements as predictions of future events.
Snowflake enables any organization to collect its data using Snowflake’s data cloud. Customers use the data cloud to aggregate disparate data, discover and securely share data, and perform a variety of analytical tasks. No matter where the data or users reside, Snowflake provides a unified data experience that spans multiple clouds and geographies. Thousands of customers across multiple industries use Snowflake Data Cloud to power their businesses, including 241 of the 2021 Fortune 500 and 488 of the 2021 Forbes Global 2000 (G2K) as of January 31, 2022. For more information, visit snowflake.com. We recently announced our native integration with the zero-management Snowflake cloud data warehouse. Part 1 of this blog series provided an overview of immediate value companies can focus on when they are snowflakes. In Part 2 of this blog series, we’ll focus on one of the three main use cases we’ve highlighted where it can speed up time to value – insights and analytics.
All of the above use cases have kernel preparation in common. In most instances we see from our customers, a certain amount of initial structuring, cleaning and mixing is done beforehand.
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In reporting and analytics use cases, there is an additional stage of filtering, aggregating and subsetting this data to make it suitable for downstream consumption in visualization platforms, compliance and regulatory reporting and other analytical processes. We see these use cases across all industries, but are especially prevalent in financial services, insurance, marketing and retail. This work is often performed by data analysts and includes assistance from data engineers. Data quality is essential for accurate reporting and analysis. Additionally, central management and collaboration are critical in that access to data is tightly managed, a complete audit trail is created through line-of-sight documentation, and redundant work is eliminated through sharing and collaboration. Features that simplify this process for data analysts include transform by instance, scoring functions, pivot, unpivot, and group by.
The biggest hurdle during reporting and analytics insights is that data is typically delivered by a centralized IT team performing ETL processes that are overwhelmed by the needs of different business units and data teams. Data consumers have certain characteristics that require the data to be in a specific format with specific attributes. If IT teams provide access to less refined data, data consumers will have to do a lot of manual work in tools like Excel or Python. In addition, many organizations are stuck with legacy reporting processes and desktop business tools that support reporting, leaving the potential for data quality issues, redundant work and lack of compliance. Poor governance of these platforms results in multiple sources of truth, lack of repeatability and ultimately lack of confidence in data quality. Automating tasks in Excel is also difficult, and if users ever change, integrating tasks with others in Python is painful.
Empowers business teams to automate technical platforms with easy-to-use tools like Excel The platform is centrally managed and access and permissions are configurable. Data never leaves the source system, ensuring security. Businesses can be more agile, gain insights faster with better data, and collaborate with other teams. Snowflake Infrastructure as a Service frees up technical resources for business teams to focus on improving processes, access and compliance, and enables business teams to take the data they need and refine it for their purposes.
Let’s look at marketing analytics use cases to demonstrate the value of reporting and analytics.
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In this example, we will play the role of a marketing analytics team within a broader marketing organization. It is important to provide marketing and sales teams with the information they need to effectively sell our products. As our business grows, we must adapt to new reporting requirements and quickly prepare known and unknown data for use in our analytics platform. Our IT team has set up the integration process to bring all the necessary raw data into Snowflake We need to join this data, clean some columns, blend the data together, extract important information, and finally set up an automated schedule so that the cleaned data consistently feeds our dashboards. This is an essential part of preparing our workflow. Many people on the team will automatically take the data and perform further aggregations and subsets before analysis. It is aggregation, filtering, and subsets that are key to self-service analytics. Let’s see how it feels.
Our IT team has already set up our Snowflake connection so we can go ahead and explore our available data and connect it to the datasets we need for reporting purposes. This makes it easy to explore datasets and find what we need for our work
Next, we’ll start editing our recipe and cleaning up some of the data we have. Immediately highlights data quality issues such as lack of consistent data formats and other inconsistencies such as error messages in the data. These issues are easy to fix by filtering lines with error messages and fixing incompatible data formats with the transform, for example by specifying my preferred output format, and I’ll work behind the scenes to get my data, regardless.
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