Resource Categories: Analyst Reports

Cost Savings And Business Benefits Enabled By The Snowflake AI Data Cloud

Generative AI is changing the demands on our data platforms. Enterprises need a platform that is easy to use and scalable, with automated infrastructure management, maintenance and performance improvements that help them launch projects and products faster.

In 2024, Snowflake commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study to find the potential ROI enterprises may get by deploying Snowflake. They spoke with four Snowflake customers and found:

• What 3-year ROI they saw with the AI Data Cloud
• How many months it took them to see a return on their investment
• The cost savings gained from improved decision-making and time to innovation
• How much more productive their data engineers, data scientists and data analysts said they became with Snowflake

Forrester Consulting study commissioned by Snowflake

As generative AI (gen AI) adoption transforms the competitive landscape, businesses without a strong data strategy will find their ambitions limited, according to a new report by MIT Technology Review Insights, in partnership with Snowflake.

The report surveyed more than 275 global business leaders, from a broad range of industries, about their hopes for gen AI. It found that four of five businesses aren’t ready to capitalize on the technology’s benefits because of poor data foundations. But executives still have lofty ambitions for gen AI’s potential.

Download your copy of the report to learn:

• How AI is opening the door for organizations to gain new insights from their unstructured data
• Why organizations can no longer “limp along” with poor data foundations
• About the top challenges organizations are facing in deploying AI at scale and how they can overcome them

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To harness data’s full potential, organizations must ensure the integrity, availability, and efficiency of their data processes.

They need to be able to track and observe the data flowing through their data environments. When problems occur, they need to be alerted in real time to be able to address them. Organizations are using a range of different tools and tool types to perform different aspects of data monitoring and observability. These include core data management tools as well as newer automated and observability tools.

Organizations with healthy data derive more value from their data. This TDWI Best Practices Report examines the processes and best practices organizations are putting in place to track data flows, detect and respond to issues in real time, and maintain overall data health.

Serving today’s dynamic marketplace requires that retailers and their supply chain partners move at the speed of the consumer.

To stay competitive, today’s retailers must provide differentiated customer experiences, achieve sustained operational efficiencies, and deliver innovative products and services. This requires extensive coordination among people and systems. Central to this coordination is customer data and advanced analytics, especially artificial intelligence.

This TDWI Insight Accelerator focuses on how artificial intelligence (AI) can help retailers and CPG companies to stay competitive, delight customers, and accelerate their delivery of innovative new products and services.

A data lakehouse provides a unified enterprise platform for data-driven decision support, business intelligence, advanced analytics, machine learning, and artificial intelligence.

The data lakehouse architecture includes functions for data storage, data pipelines, AI and machine learning, data governance, data protection, and more. Unification of these disparate capabilities is achievable with an open lakehouse architecture.

Enterprises are adopting data lakehouse architectures because they want unified views of enterprise data assets on interoperable storage. This setup allows them to use more diverse, more flexible tools for analyzing, manipulating, and otherwise consuming their data.

This TDWI Checklist discusses four best practices that will help ensure that enterprises maximize the usefulness, flexibility, and interoperability of the cloud storage layer in their open data lakehouses.

In today’s competitive landscape, data is the key to unlocking startup success. Implementing a well-crafted data strategy lays the groundwork for machine learning and generative AI, driving innovation at its core. Snowflake and Amazon Web Services (AWS) offer a robust, scalable and secure platform tailored for startups to empower innovation, efficiency and elevate your startup to new levels of achievement.

Read more about how Snowflake + AWS are enabling Startups in their growth journey

AI is a key contributor to success in modern organizations.

For years, TDWI research has focused on AI as a core enabler for business automation, decision support, and operational efficiency. Today, AI-powered intelligent applications are being used to transform business processes and thereby improve their performance, efficiency, effectiveness, and agility.

Enterprises all over the world, in all industries, and of all sizes are implementing digital business transformation to varying degrees. Advances in cloud computing, real-time stream and event processing, low-latency data fabrics, and more—all accelerated by AI—are driving a continual feed of real-time data updates, contextual insights, optimized experiences, and fast results into all business processes.

This TDWI Best Practices Report explores trends, considerations, and opportunities associated with successful implementation of AI in enterprise digital business transformation initiatives.

Organizations are exploring generative AI to unlock new levels of productivity using large language models (LLMs) alongside related tools. It’s important to consider use cases that target all types of data, including internal company data; with all enterprises having access to the same models, company data is what provides competitive advantage.

Of course, generative AI isn’t necessarily the best option for all AI use cases. In this ever-evolving environment, it is important to have well-defined business objectives and then evaluate which technology will help your organization successfully achieve them.

This TDWI Checklist Report explores several popular generative AI use cases as well as how to get started with generative AI and put it into production with company data.

In today’s rapidly changing digital environment, data governance is essential for organizations seeking to manage their data more effectively.

There are numerous reasons why data governance has become so critical. Organizations are now grappling with numerous data types, ranging from structured data to newer data types such as text data, machine data, image data, and audio data. AI/ML models also need to be governed to ensure that the input to and the output from these models is trustworthy.

Traditional data governance objectives (e.g., ensuring data accuracy, consistency, and compliance) remain foundational. However, they now must be integrated with new concerns, including the management of diverse and voluminous data, ethical data use, and the governance of AI/ML assets—all of which involve technology considerations.

This TDWI Checklist Report examines five important pillars and best practices for the comprehensive governance of modern data- and AI-related assets.