Resource Categories: Analyst Reports

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.

Today’s organizations want to build analytics-based applications and use data and analytics to become more competitive.

They are modernizing their data environments to support these advanced analytics and looking for a scalable environment to meet their data needs. Many organizations are dealing with hybrid environments as part of this modernization effort.

Recently, the data mesh paradigm has emerged. The data mesh is an organizational and architectural approach for sharing, accessing, and managing analytics data in complex and large-scale environments within or across organizations. Data mesh architecture and processes can be accomplished in different ways and new products and organizational constructs will emerge to support some of the principles of the data mesh.

What are the implications for the data mesh and the cloud? How can the cloud and the data mesh work together? This report examines these questions and offers five essential actions your enterprise should take to ensure a successful data mesh adoption.

Robust data engineering processes ensure that analytics are always accurate, relevant, and fit for purpose.

Making the most of your enterprise data requires a high-performance pipeline that transforms it all into ready-to-use business assets.

Essentially, a data pipeline is a chain of connected processes that takes data from sources and prepares it for downstream data and analytics applications to consume (by transforming, integrating, cleansing, augmenting, and enriching the data). How can you simplify the deployment and management of your data pipelines, even as they span the most complex, distributed cloud environments?

This TDWI Checklist discusses key steps for deploying and operating cloud-based data pipelines.

The data lake is an important piece of a modern data strategy—especially when it comes to supporting a wide variety of data types and more advanced analytics.

Today’s data lakes are often built on cloud-based object stores that run on public, private, hybrid, and other cloud architectures to provide scalability and extensibility. These modern data lakes support SQL, artificial intelligence (AI), machine learning (ML), and other advanced analytics.

Modern data lakes also provide an environment with capabilities to load, integrate, and analyze data to derive business value. Some incorporate data catalogs or implement augmented intelligence features such as automatic classification of sensitive data. They automate infrastructure management and provide automated services such as shutting off a job when it is complete. Modern data lakes are often unified or converged with the cloud data warehouse to form a unified platform.

This TDWI Checklist examines important characteristics of the modern data lake and factors to consider to ensure your data lake is future-proof.

Security used to be the biggest barrier to adoption of cloud services and transitioning and migrating applications and data to a cloud infrastructure. However, concerns about cloud service provider security, at least from the system security perspective, have been largely addressed.

Now, as cloud service providers have hardened system security measures, other types of information risks increasingly need to be acknowledged and addressed. This Pulse Report reflects upon six key dimensions of information risk.