Resource Categories: Analyst Reports

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.

Recent advances in generative AI are actively changing every major sector of the economy. Its potential impacts across the manufacturing industry will be far-reaching, from increasing operational efficiency and productivity to strengthening collaboration across the globe.

Download the report to learn about our most important predictions for the manufacturing industry in 2024:

• Generative AI will overhaul how manufacturers work by driving efficiency, productivity and innovation
• AI and data will inform critical business decisions across the value chain
• A robust data foundation will distinguish leaders in manufacturing

Rapid developments in generative AI have already begun to affect the retail and consumer goods industry. While shoppers and the wider retail industry try to work out exactly what to think of these technologies, the businesses that move quickly to adopt generative AI and new data strategies into their operations will best position themselves for success.

Download the report to learn about our most important predictions for the retail and consumer packaged goods (CPG) industry in 2024:

• Experimenting with generative AI will put retailers and consumer goods companies ahead of the curve on productivity.
• Data monetization will play a massive role in driving revenue.
• A strong data strategy will distinguish industry leaders from followers.

In 2024, rapid developments in generative AI, coupled with changes in policy guidance and public sentiment, will likely compel governments to adopt new technologies more quickly than at any time in recent memory. In this report, our in-house experts weigh in on the impact of AI and other developments on the public sector. 

Read the report to learn key industry predictions for 2024, including:

• How the public sector will embrace AI to solve long-term problems — eventually
• Why agencies will accelerate cloud migration and adoption of data management platforms
• Why governments will organize data marketplaces to grow GDP
• And much more

Generative artificial intelligence (AI) is poised to transform practically every type of work. The potential of generative AI lies in its ability to automate development of every type of knowledge-based output. In the process, generative AI can boost the operational efficiency of many business processes that traditionally have relied on manual human efforts, as well as augmenting the productivity of humans in many knowledge-intensive functions.

This TDWI Insight Accelerator focuses on current enterprise practices and key steps your organization can take to unlock the potential of generative AI.

Cloud data warehouses help organizations modernize their analytical platform. Forrester researched and analyzed the most important providers in the space and named Snowflake one of the leaders.

Download your complimentary copy of the report to learn:

• How easy Snowflake customers say the solution is to use
• How highly automated Snowflake is, “requiring zero administration”
• Why Snowflake is ahead of other vendors when it comes to innovation, including AI/ML

KLAS Research is an industry leader in analyzing IT solutions and services for healthcare organizations. KLAS recently evaluated Snowflake’s Healthcare & Life Sciences Data Cloud based on data integration, cloud capabilities, and performance across six customer experience pillars—culture, loyalty, operations, product, relationship and value.

Report highlights include:

• Why the top customer outcomes are operational efficiency and cost-effectiveness
• In-depth insights from Snowflake healthcare customers about their experience
• Why Snowflake received a 92.2*% performance score
• Customers’ long-term plans for using Snowflake

Download now.

*Some respondents chose not to answer particular questions. As a result, there was limited data available. This statement reflects the impact of non-responses on data availability.*

The pressure to deliver data-driven insights faster across diverse use cases is prompting product leaders to rethink their data application approach. Modern cloud-based data platforms provide the power and flexibility teams need to create highly scalable applications efficiently.

In this report, we explore the synergy between cloud data platforms and data-intensive applications, highlighting key considerations that help accelerate app development, deployment, and adoption.

You’ll learn:

• Why modern cloud data platforms are a great fit for data applications
• Considerations for building data apps at scale, including resource sharing, multi-tenancy, and workload isolation
• Data processing best practices
• Methods to modernize application distribution for providers and consumers