Resource Categories: Analyst Reports

As competitive pressures increase, many organiza­tions want to leverage higher volumes of diverse data for analytics and new appli­cations, including real-time and streaming data.

Streaming data refers to data that is gener­ated continuously in a stream, typically at high volume, either at regular or irregular intervals. Working with streaming data may involve capturing multiple streams for later analysis or processing, or analyzing one or more streams in real time—for example, to immediately capture data that deviates from the norm.

Successful applications of streaming data can improve operational processes and reduce costs by quickly identifying problems. They can help organizations improve the customer experience and provide new and innovative applications that can add to a company’s bottom line. Yet organizations face difficulties using their current data platforms, integration, and management to handle real-time data streaming.

This TDWI Checklist Report examines six important considerations and best practices for managing streaming data for real-time use.

The data explosion continues to accelerate across distributed landscapes with data on premises and on multiple cloud data platforms. Organizations face challenges as well as tremendous potential for increasing the value of data assets, including through data monetization—potential that can go untapped without good data management and governance.

Data-driven business initiatives depend on scalable, agile, and comprehensive data management and governance. The latest applications embed sophisticated analytics using AI/ML capabilities that must be provisioned with continuous, integrated, curated data to deliver insights to all users. Flexibility is key to keeping pace with business demand and unanticipated events.

This TDWI Best Practices Report focuses on understanding current challenges and providing best practices insights for modernizing processes and deploying technologies to solve them.

Cloud computing is rapidly becoming the center of data management, driven by business objectives. This means that CIOs and CDOs need to ensure that cloud data platform investments achieve desired business objectives while controlling total cost of ownership. This TDWI Checklist Report outlines five steps to accomplish these goals:

• Take advantage of fully managed capabilities to accelerate value and minimize TCO
• Increase flexibility with cloud elasticity to handle a variety of workloads
• Maximize the benefits of pay-as-you-go, usage-based pricing models
• Improve usage visibility and control through cost optimization
• Evaluate cloud data platforms for continuous performance improvement

Supply chain resiliency is critically important in life sciences. Components flowing through the supply chain are needed for medical devices, drugs, and other lifesaving items. For instance, sensors are required for medical devices; certain ingredients are needed for active pharmaceutical ingredients (APIs). These essentials can all be impacted by supply chain disruptions.

How does the life sciences industry utilize data and analytics to improve the resiliency of its supply chain? This TDWI Insight Accelerator discusses key challenges and recommendations for overcoming them, including how cloud solutions can help.

Modern analytics can provide a significant path to value for organizations. Although many companies are still analyzing structured data, newer data sources such as machine data or text or image data, along with newer analytics approaches, are becoming part of an evolving data and analytics landscape.

TDWI research finds that organizations are embracing modern technologies—including cloud platforms, automated tools, data fabrics, data pipelines, and new data governance and quality tools—in their efforts to support a modern data infrastructure for data and analytics. Despite some organizations’ success, many businesses still struggle to implement or see benefits from modern analytics.

This Best Practices Report examines the drivers for modern analytics and the current state of analytics adoption (including tools and platforms) and explores the differences between those who are using modern analytics successfully and those who are not.

Across the healthcare industry, interoperability, or the ability to effectively share data, impacts everything from effective patient care and supply chain accuracy to regulatory compliance and workflow optimization. To better understand the state of interoperability today, Snowflake partnered with Fierce Healthcare in late 2022 to survey a segment of healthcare providers, systems, regulators, and payers to learn about the present-day challenges and expectations associated with interoperability. The survey revealed surprising and interesting insights, especially when comparing differences by job title, organization type, and role within the organization.

Check out this report to learn:

• How healthcare professionals rank interoperability’s impacts
• The common challenges of achieving effective data collaboration today
• Perspectives on the future impacts of interoperability in healthcare

See the survey findings now.

Legacy on-premises data warehouses (DWs) are increasingly showing their age. Over the past decade, enterprises everywhere have been migrating to cloud-based DWs that offer greater scalability, performance, flexibility, and cost-effectiveness.

This TDWI Checklist shares five best practices for migrating your enterprise DW to the cloud and presents recommendations for aligning your DW migrations with your modernization strategies and business imperatives.

To compete in today’s dynamic environment, organizations need to modernize their data and analytics environments.

This modernization includes implementing new technologies such as scalable cloud platforms and unified approaches. It includes more advanced analytics such as geospatial analytics and machine learning. It also includes new paradigms such as the data fabric and the data mesh.

Furthermore, modernization may include new organizational constructs such as the data office and new teams such as DataOps, MLOps, and data literacy enablement teams.

This TDWI Best Practices Report discusses the current state of DataOps and MLOps practices, platforms, and pipelines in modern organizations. It describes key business drivers, implementation challenges, and key use cases for both DataOps and MLOps pipelines in their respective domains, and for unification of these pipelines within enterprise IT infrastructures.

Read the report to learn how the unification of DataOps and MLOps platforms, workflows, and methodologies is dovetailing with enterprise data modernization initiatives.