Analyst Perspectives

Organizations today have an ever-increasing appetite for platforms that improve the speed and efficiency of data analytics and business intelligence (BI). The ability to quickly process data enables organizations to make well-informed decisions in real time. This agile approach to data processing is crucial for staying ahead in today's competitive landscape. With the rising need for data-driven insights, organizations face the difficulty of dealing with massive volumes of distributed business...

I recently wrote about the various technologies used by organizations to process and analyze data in real time. I explained that while the terms streaming data and events and streaming analytics are often used interchangeably, they are separate disciplines that make use of common underlying concepts and technologies such as events, event brokers and event-driven architecture. Confluent’s acquisition of Immerok earlier this year provided a reminder of this fact. Confluent is one of the most...

Real-time business is a modern phenomenon, and business transformation has accelerated many business events in recent years. However, the execution of business events has always occurred in real time. Rather, it is the processing of the data related to business events that has accelerated instead of the event itself.

It is a mark of the rapid, current pace of development in artificial intelligence (AI) that machine learning (ML) models, until recently considered state of the art, are now routinely being referred to by developers and vendors as “traditional.” Generative AI, and large language models (LLMs) in particular, have taken the AI world by storm in the past year, automating and accelerating the development of content, including text, digital images, audio and video, as well as computer programs and...

As I have previously explained, we expect an increased demand for intelligent operational applications infused with the results of analytic processes, such as personalization and artificial intelligence-driven recommendations. These systems rely on the analysis of data in the operational data platform to accelerate worker decision-making or improve customer experience.

I have previously written about the functional evolution and emerging use cases for NoSQL databases, a category of non-relational databases that first emerged 15 or so years ago and are now well established as potential alternatives to relational databases. NoSQL is a term used to describe a variety of databases that fall into four primary functional categories: key-value stores, wide-column stores, document-oriented databases and graph databases. Each is worthy of further exploration, which is...

The publication of Ventana Research’s 2023 Operational Data Platforms Value Index earlier this year highlighted the importance of incorporating analytic processing into operational applications to deliver personalization and recommendations for workers, partners and customers. This importance is being accelerated by interest in generative AI, especially large language models. The emergence of intelligent applications has impacted the requirements for operational data platforms with the need to...

Despite best intentions, many organizations still struggle with some fundamental aspects of data processing and analytics. Taking data from operational applications and making it available for analysis is a first step, but data management remains a perennial challenge. Data movement and transformation difficulties can lead to delays and data quality problems that prevent organizations from generating value from data. The inability to govern and integrate data from multiple data sources prevents...

Maintaining data quality and trust is a perennial data management challenge, often preventing organizations from operating at the speed of business. Recent years have seen the emergence of data observability as a category of DataOps focused on monitoring the quality and reliability of data used for analytics and governance projects and associated data pipelines. There is clear overlap with data quality, which is more established as both a discipline and product category for improving trust in...

Organizations increasingly rely on real-time analytics to make informed decisions and stay competitive in today’s data-driven business landscape. As the complexity of data grows with the continuous addition of diverse sources, customers and workers alike expect real-time responsiveness. Accelerated query performance is crucial to process and extract valuable insights from data in a timely manner. Traditional analytics applications are often insufficient for managing the scale, velocity and...