Resource Categories: Analyst Reports

Machine learning (ML), the most popular type of AI, provides enterprises with a much-needed tool to compete in today’s complex, fast-changing business environment. To make machine learning succeed at scale, data science teams must standardize and streamline the ML workflow – also known as MLOps – that spans data and feature engineering, model development, and model production.

This report examines how a cloud data platform enables teams to standardize and manage the ML lifecycle to help organizations achieve the scalability, reproducibility, and governance they need to succeed with machine learning.

Read this report to learn:

• The team roles and stages of the machine learning lifecycle

• How a cloud data platform helps streamline this lifecycle

• Guiding principles to optimize MLOps on a cloud data platform

Snowflake Ranks as a Leading ADI Technology Provider

For the fifth consecutive year, Snowflake received a perfect recommendation score from its customers in the 2022 Wisdom of CrowdsⓇ Series Analytical Data Infrastructure (ADI) Market Study. 

Download the report to review Snowflake’s ranking as an Overall Leader in both the Customer Experience and Vendor Credibility Models. Snowflake continues to maintain very high scores well above the overall sample and received a perfect recommendation score. You’ll also discover why customers ranked Snowflake best in class for a number of categories, including:  

• Ease of upgrade, installation, customization, and extensibility
• Value for price paid
• Quality and usefulness of the product
• Quality of technical support
• Quality and value of consulting services
• Customer recommendations
• Vendor integrity

“We are a data-driven company,” is a familiar refrain we hear from business leaders and managers. This is evidence of a fundamental shift in mindset, reflecting the fact that leaders have now understood and internalized the concept of the data-driven enterprise. However, when attempting to re- structure and reorganize data flows and processes and bring in new ways of working with data, particularly CDOs, CIOs and data teams often run into what feels like a brick wall. Acceptance and lip service may have been paid to a data strategy, but the application of the strategy is fraught with hurdles. The realization comes soon enough: Data culture and its manifestation in the day-to-day running of the business can make or all too often break any carefully crafted strategies. So, understanding what data culture means within each organization is critical to its success.

This study was conducted due to the steadily growing interest in the topic and to better understand the flavors of data culture within organizations, the hur-dles they are facing, the initiatives that are on underway and of course the wins that have already been achieved with these measures. Together with the lessons learned from this study and the BARC Data Culture Framework, we hope to provide resources that help leaders to ride the wave of this new mindset and begin to shape their data culture.

This study was prepared by BARC, an inde-pendent market analyst firm. It is available free of charge thanks to the generosity of Alation, Board, Collibra, Crate.io, Five1, Snowflake and Tableau.

Data governance must keep pace with increasingly popular data access and usage practices. As new data sources, data sets, SaaS applications, and cloud data platforms are added to today’s complex and hybrid data architectures, data governance must extend its policies to cover these architectures, plus most of the enterprise. Adaptations to these changes, along with process and practice improvements, are leading to modern data governance, which is the next level beyond today’s data governance.

Modernizing data governance must address all of its many components, including business-driven compliance, technology-driven data standards, and people-and-process practices. Users should expect to modernize data governance now and continuously improve it moving forward.

This report canvasses current and future data governance strategies and best practices to help organizations understand the new requirements for data governance, plus how successful organizations are modernizing existing governance programs and toolsets. The intent is to assist with modernization planning so that data governance achieves maximum business impact.

Data is becoming increasingly complex as organizations build out their analytics capabilities and become more competitive. Often organizations are dealing with new data types and moving to multiple platforms to support this data.

One approach gaining popularity is to utilize a cloud data platform—an integrated platform available on the public cloud to house diverse data and provide services such as a data warehouse, data lake, analytics, or data science. The cloud data platform is a single entity that supports multiple workloads and data types. It provides one place to store and access data for analytics and other use cases.

Data governance is an important issue as organizations extend their data infrastructure to the cloud. Core principles—including compliance, control, and protection—remain the same in cloud data platforms but will need to be extended.

This report examines five organizational and technology best practices for governing data specifically in a cloud data platform

Forrester Study reveals ROI of 405% and benefits of over $10 Million over 3 years for a sample composite organization that built their data apps in the Data Cloud and procured Snowflake through AWS Marketplace.

Read this study to learn how several companies leveraged Snowflake and AWS Marketplace to increase profits, save costs, and accelerate time to market for their data applications while improving scalability and performance, simplifying day 2 operations, and streamlining procurement.

Snowflake and AWS commissioned Forrester Consulting to conduct a Total Economic Impact study that included interviews with 8 companies to obtain data on costs, benefits, and risks associated with their investment in Snowflake through AWS Marketplace. Benefits for a composite organization based on participating companies include:

• Cost savings from faster time to market worth $2.3 million
• Increase profit of $2.2 million from faster time to market
• Cost savings on infrastructure and data platform management of $4.8 million
• Total benefits of over $10 million over 3 years