Resource Categories: Analyst Reports

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.

Data-intensive applications help customers, employees, and other users do business and make the best decisions in various real-world contexts.

These applications—which embed sophisticated analytics and business logic that feed on fast-changing volumes and varieties of data—deliver guidance that can adapt in real time to user circumstances.

To tailor these applications for their intended uses, application providers require a robust development and operationalization workflow that accelerates and automates the sourcing and preparation of data, the building and training of analytics, and the management of assets (plus the necessary programming code) in cloud-based production environments.

This TDWI Checklist Report presents best practices for application providers to accelerate this workflow in the cloud. It addresses such imperatives as user journey alignment, scalable resource provisioning, rapid application deployment, unified pipeline workflows, comprehensive observability, and maximum application reusability.

Modern enterprises are expanding their analytics programs to improve their ability to make fact-based decisions, plan for an uncertain future, compete on analytics, and grow customer accounts. These high-value business goals require advanced forms of analytics, which in turn demand use-case-appropriate data integration, data platforms, and other data management. Without the right data in the right format on the right platform, critical and expensive
efforts in advanced analytics have little or no business value.

This report defines data management for advanced analytics (DM for AA), which tailors established and emerging data management best practices and techniques to specific forms of advanced analytics, thereby raising the precision, productivity, and business value of analytics.

This TDWI Best Practices Report explores data management strategies and best practices, then links combinations of these to the leading forms of advanced analytics to help data management and advanced analytics professionals and their business counterparts achieve greater success and business impact.

Data monetization opportunities are increasingly within reach of businesses everywhere. Recent innovations in converged data analytics platforms can accelerate an enterprise’s ability to productize their data and other assets, such as trained machine learning models.

This Best Practices Report examines whether enterprise democratization of data and analytics is making a contribution to increased revenues, cost reductions, process efficiencies, returns on investment, and other quantifiable aspects of financial performance.

Excellence in personalization is a competitive differentiator. Customer retention and loyalty—critical to higher margins and profitability—depend on personalization.

Customers enjoy having the power to select features and tailor products. Many are willing to pay higher prices for products they can customize. Organizations need timely, quality information to ensure that personalization options are appropriate for each customer.

Organizations have long used characteristics such as gender, household size, education, occupation, and income to segment customers for marketing. Smarter segmentation based on analytics, including artificial intelligence and machine learning (AI/ML), allows you to move beyond standard segmentation and one-size-fits-all marketing. Powered by data, you can develop micro-segments, explore data relationships across segments to tailor offerings, and move toward personalized, one-to-one marketing.

This TDWI Insight Accelerator focuses on data-driven personalization strategies for retail and consumer packaged goods (CPG) organizations. Many of the issues are also of interest to marketing functions in other industries.

What is the data economy? How can you leverage it? And, why care–what’s possible once you’re successful?

We conducted global research to find out. We learned that organizations exploiting near-boundless access to data, data services, business insights and data collaboration are solving some of the most complex business problems while also creating new market opportunities.

These Data Economy Leaders accelerate time to market for new products, best know and serve customers, and outthink bad actors to minimize fraud. Those pushing the boundaries of the data economy have built new revenue streams with data products and services made available to their customers, partners, and any other organization.

Data Economy Leaders outperform all others in revenue growth, customer satisfaction, market leadership, and other business metrics. Download the report to find out:

• What characterizes Data Economy Leaders (and those lagging behind)
• How your organization can become a leader and the business benefits that result
• Which industries are winning (or lagging) in the data economy and how
• What advice chief data officers (CDOs) from leading brands have for aspiring organizations
• What Snowflake recommends for how to get started and which efforts to prioritize

Maximizing the value of data platforms with data integration and data management capabilities is essential for organizations that want to become data- and analytics-driven. Increasingly—but not entirely—based in the cloud, these systems and services are the engine of modern applications and smarter processes for higher business efficiency and innovation.

Research indicates the need for tighter integration between two traditionally separate worlds: one devoted to analytics generation and the data integration, platforms, and management that support analytics, and the other devoted to mission-critical business applications and processes essential to operations across enterprises.