Analyst Perspectives

Although the terms data fabric and data mesh are often used interchangeably, I previously explained that they are distinct but complementary. Data fabric refers to technology products that can be used to integrate, manage and govern data across distributed environments, supporting the cultural and organizational data ownership and access goals of data mesh. Data fabric and data mesh are also both...

As I noted in the 2024 Buyers Guide for Operational Data Platforms, intelligent applications powered by artificial intelligence have impacted the requirements for operational data platforms. These applications, infused with contextually relevant recommendations, predictions and forecasting, are driven by machine learning and generative AI.

As I recently noted, the term “data intelligence” has been used by multiple providers across analytics and data for several years and is becoming more widespread as software providers respond to the need to provide enterprises with a holistic view of data production and consumption. I assert that through 2027, three-quarters of enterprises will be engaged in data intelligence initiatives to...

I previously wrote about data mesh as a cultural and organizational approach to distributed data processing. Data mesh has four key principles—domain-oriented ownership, data as a product, self-serve data infrastructure and federated governance—each of which is being widely adopted. I assert that by 2027, more than 6 in 10 enterprises will adopt technologies to facilitate the delivery of data as...

As I explained in our recent Buyers Guide for Data Platforms, the popularization of generative artificial intelligence (GenAI) has had a significant impact on the requirements for data platforms in the last 18 months. While there is an ongoing need for data platforms to support data warehousing workloads involving analytic reports and dashboards, there is increasing demand for analytic data...

The final of the men’s 100 meters at the Paris Olympics this summer was a reminder that being successful requires not just being fast but performing at the right time. Being fast is obviously a prerequisite for participating in an Olympic 100-meter final, and all the competitors finished the race in under 10 seconds, with just 0.12 seconds separating the first man from the last. While all the...

Enterprises face a bewildering level of choice in relation to data platforms, as evidenced by the number of software providers and products assessed in our recent Data Platforms Buyers Guide. There are numerous data platform providers and products to choose from, but also a diverse array of functional and architectural options. Is the workload primarily operational or analytic? Will it be...

I have written on multiple occasions about the increasing proportion of enterprises embracing the processing of streaming data and events alongside traditional batch-based data processing. I assert that, by 2026, more than three-quarters of enterprises’ standard information architectures will include streaming data and event processing, allowing enterprises to be more responsive and provide...

The artificial intelligence and machine learning landscape was profoundly altered by the emergence of generative AI into the mainstream consciousness during 2023. The widespread availability of GenAI models and cloud services has lowered the barriers to individuals and enterprises engaging with AI for various use cases, including generating content, querying data, writing code, preparing data for...

I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well software providers’ offerings meet buyers’ requirements. The Data Platforms Ventana Research Buyers Guide is the distillation of a year of market and product research by ISG and Ventana Research.

I previously wrote about the ongoing importance of event brokers and event management in enabling enterprises to adopt event-driven architecture and event stream processing. Many enterprises adopt EDA as the design pattern for maximizing events to deliver real-time business processes. There are many advantages to using EDA, including a cultural shift away from batch processing towards real-time...

I previously wrote about the potential for generative artificial intelligence technology to enhance the integration sector by facilitating outcome-driven approaches for automatically generating integration pipelines in response to declared business requirements. The use of GenAI in data and application integration remains nascent, but multiple software providers are embracing the potential for...

I recently wrote about the role data observability plays in generating value from data by providing an environment for monitoring its quality and reliability. Data observability is a critical functional aspect of Data Operations, alongside the development, testing and deployment of data pipelines and data orchestration, as I explained in our Data Observability Buyers Guide. Maintaining data...

Enterprises are embracing the potential for artificial intelligence (AI) to deliver improvements in productivity and efficiency. As they move from initial pilots and trial projects to deployment into production at scale, many are realizing the importance of agile and responsive data processes, as well as tools and platforms that facilitate data management, with the goal of improving trust in the...

The emergence of generative artificial intelligence (GenAI) has significant implications at all levels of the technology stack, not least analytics and data products, which serve to support the development, training and deployment of GenAI models, and also stand to benefit from the advances in automation enabled by GenAI. The intersection of analytics and data and GenAI was a significant focus of...

I recently wrote about the development, testing and deployment of data pipelines as a fundamental accelerator of data-driven strategies as well as the importance of data orchestration to accelerate analytics and artificial intelligence. As I explained in the recent Data Observability Buyers Guide, data observability software is also a critical aspect of data-driven decision-making. Data...

I previously wrote about the potential for rapid adoption of the data lakehouse concept as enterprises combined the benefits of data lakes based on low-cost cloud object storage with the structured data processing functionality normally associated with data warehousing. By layering support for table formats, metadata management and transactional updates and deletes as well as query engine and...

Many organizations have adopted DataOps to apply agile development, DevOps and lean manufacturing processes to the development, testing, deployment and orchestration of data integration and processing pipelines. The most likely ultimate outcome of these pipelines is the analytics reports and dashboards enterprises rely on to make business decisions.

Analytics software is used by business analysts and decision-makers to facilitate the generation of insights from data. It encompasses business intelligence and decision intelligence software, including reports and dashboards as well as embedded analytics and the development of intelligent applications infused with the results of analytic processes. Analytics software enables enterprises to...

I recently wrote about the development, testing and deployment of data pipelines as a fundamental accelerator of data-driven strategies. As I explained in the 2023 Data Orchestration Buyers Guide, today’s analytics environments require agile data pipelines that can traverse multiple data-processing locations and evolve with business needs.