Ventana Research Analyst Perspectives

There is always space for innovation in the data platforms sector, and new vendors continue to emerge at regular intervals with new approaches designed to serve specialist data storage and processing requirements. Factors including performance, reliability, security and scalability provide a focal point for new vendors to differentiate from established vendors, especially for the most demanding operational or analytic data platform requirements. It is never easy, however, for developers of new...

In previous perspectives in this series, I’ve discussed some of the realities of cloud computing including costs, hybrid and multi-cloud configurations and business continuity. This perspective examines the realities of security and regulatory concerns associated with cloud computing. These issues are often cited by our research participants as reasons they are not embracing the cloud. To be fair, the majority of our research participants are embracing the cloud. However, among those that have...

Earlier this year, I wrote about the increasing importance of data observability, an emerging product category that takes advantage of machine learning (ML) and Data Operations (DataOps) to automate the monitoring of data used for analytics projects to ensure its quality and lineage. Monitoring the quality and lineage of data is nothing new. Manual tools exist to ensure that it is complete, valid and consistent, as well as relevant and free from duplication. Data observability vendors,...

In my previous perspectives on cloud computing, I addressed some of the realities of cloud costs as well as hybrid and multi-cloud architectures. In the midst of the pandemic, my colleague, Mark Smith, authored a series of perspectives on considerations for business continuity in general, beginning with this look at some of the investments organizations must make to mitigate the risk of business disruptions. In this perspective, I’d like to address some of the realities of business continuity...

I have written recently about the similarities and differences between data mesh and data fabric. The two are potentially complementary. Data mesh is an organizational and cultural approach to data ownership, access and governance. Data fabric is a technical approach to automating data management and data governance in a distributed architecture. There are various definitions of data fabric, but key elements include a data catalog for metadata-driven data governance and self-service, agile data...

I have written before about the continued use of specialist operational and analytic data platforms. Most database products can be used for operational or analytic workloads, and the number of use cases for hybrid data processing is growing. However, a general-purpose database is unlikely to meet the most demanding operational or analytic data platform requirements. Factors including performance, reliability, security and scalability necessitate the use of specialist data platforms. I assert...

Earlier this year I described the growing use-cases for hybrid data processing. Although it is anticipated that the majority of database workloads will continue to be served by specialist data platforms targeting operational and analytic workloads respectively, there is increased demand for intelligent operational applications infused with the results of analytic processes, such as personalization and artificial intelligence-driven recommendations. There are multiple data platform approaches to...

I have recently written about the organizational and cultural aspects of being data-driven, and the potential advantages data-driven organizations stand to gain by responding faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. I have also explained that data-driven processes require more agile, continuous data processing, with an increased focus on extract, load and transform processes — as well as change data capture and automation...

In my first perspective on cloud computing realities, I covered some of the cost considerations associated with cloud computing and how the cloud costing model may be different enough from on-premises models that some organizations are taken by surprise. In this perspective. I’d like to focus on realities of hybrid and multi-cloud deployments.

I recently wrote about the need for organizations to take a holistic approach to the management and governance of data in motion alongside data at rest. As adoption of streaming data and event processing increases, it is no longer sufficient for streaming data projects to exist in isolation. Data needs to be managed and governed regardless of whether it is processed in batch or as a stream of events. This requirement has resulted in established data management vendors increasing their focus on...