Matt Aslett's Analyst Perspectives

I have written before about the rising popularity of the data fabric approach for managing and governing data spread across distributed environments comprised of multiple data centers, systems and applications. I assert that by 2025, more than 6 in 10 organizations will adopt data fabric technologies to facilitate the management and processing of data across multiple data platforms and cloud environments. The data fabric approach is also proving attractive to vendors, including Microsoft, as a...

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...

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...

The data and analytics sector rightly places great importance on data quality: Almost two-thirds (64%) of participants in Ventana Research’s Analytics and Data Benchmark Research cite reviewing data for quality and consistency issues as the most time-consuming task in analyzing data. Data and analytics vendors would not recommend that customers use tools known to have data quality problems. It is somewhat surprising, therefore, that data and analytics vendors are rushing to encourage customers...

Master data management may not attract the same level of excitement as fashionable topics such as DataOps or Data Platforms, but it remains one of the most significant aspects of an organization’s strategic approach to data management. Having trust in data is critical to the ability of an organization to make data-driven business decisions. Along with data quality, MDM enables organizations to ensure data is accurate, complete and consistent to fulfill operational business objectives.

Data Operations (DataOps) has been part of the lexicon of the data market for almost a decade, with the term used to describe products, practices and processes designed to support agile and continuous delivery of data analytics. DataOps takes inspiration from DevOps, which describes a set of tools, practices and philosophy used to support the continuous delivery of software applications in the face of constant changes. DataOps describes a set of tools, practices and philosophy used to ensure...

As data continues to grow and evolve, organizations seek better tools and technologies to employ data faster and more efficiently. Finding and managing data remains a perennial challenge for most organizations, and is exacerbated by increasing volumes of data and an expanding array of data formats. At the same time, organizations must comply with a growing list of national and regional rules and regulations, such as General Data Protection Regulation and the California Consumer Privacy Act....

I have previously written about the importance of data democratization as a key element of a data-driven agenda. Removing barriers that prevent or delay users from gaining access to data enables it to be treated as a product that is generated and consumed, either internally by employees or externally by partners and customers. This is particularly important for organizations adopting the data mesh approach to data ownership, access and governance. Data mesh is an organizational and cultural...

The market for data and analytics products is constantly evolving, with the emergence of new approaches to data persistence, data processing and analytics. This enables organizations to constantly adapt data analytics architecture in response to emerging functional capabilities and business requirements. It can, however, also be a challenge. Investments in data platforms cannot be constantly written-off as organizations adopt new products for new approaches. Too little change can lead to...

Organizations across various industries collect multiple types of data from disparate systems to answer key business questions and deliver personalized experiences for customers. The expanding volume of data increases complexity, and data management becomes a challenge if the process is manual and rules-based. There can be numerous siloed, incomplete and outdated data sources that result in inaccurate results. Organizations must also deal with concurrent errors – from customers to products to...