The Forrester Wave™: Data Management For Analytics, Q1 2020

While traditional data warehouses often took years to build, deploy, and reap benefits from, today's organizations want simple, agile, integrated, cost-effective, and highly automated solutions to support insights. In addition, traditional architectures are failing to meet new business requirements, especially around high-speed data streaming, real-time analytics, large volumes of messy and complex data sets, and self-service. As a result, firms are revisiting their data architectures, looking for ways to modernize to support new requirements.
DMA is a modern architecture that minimizes the complexity of messy data and hides heterogeneity by embodying a trusted model and integrated policies and by adapting to changing business requirements. It leverages metadata, in-memory, and distributed data repositories, running on-premises or in the cloud, to deliver scalable and integrated analytics. Adoption of DMA will grow further as enterprise architects look at overcoming data challenges and use existing investments and technologies, along with modern technologies, to support the next generation of analytics.
As a result of these trends, DMA customers should look for providers that:

• Deliver self-service capabilities to enable data intelligence. An important differentiator in DMA is the ability to support platforms that automate loading, unloading, tuning, integration, transformation, provisioning, high availability, security, scale, and patching. It can help ingest, process, transform, and curate data using ML and adaptive intelligence quickly. DMA also enables self-service capabilities for business users to ask complex and new questions so they can make more accurate decisions.
• Support real-time analytics for quicker and ad hoc requirements. DMA enables real-time analytics by streaming various sources, whether on-premises or in the cloud. Memory is a critical component of data management: It can store, access, and process large amounts of data to deliver real-time analytics. Look for vendors that offer deep integration with the new Intel Optane (persistent memory) with multiple modes, including app mode. The solution should leverage dynamic random-access memory, persistent memory, and flash/solid state drive with intelligent tiering and should deliver optimal price/performance for large-scale deployments.
• Integrate analytics to deal with diverse data sources and types. DMA can perform integrated analytics across various data repositories by leveraging data platforms to store and process large sets of semistructured and unstructured data, log files, and streaming data with ease. For example, health research often requires looking at complex patient data and determining the efficacy of a treatment based on factors like age, sex, and health status. DMA enables EA pros to gather, store, and process various data points to support complex navigation and modeling. 

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