Analyst Perspectives

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 data used to fuel analytics and AI. This has led to increased attention on the role of data...

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 observability addresses one of the most significant impediments to generating value from data by providing an...

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 data orchestration functionality on top of low-cost storage of both structured and unstructured data,...

The artificial intelligence (AI) market is exploding with activity, which is part of the reason we recently announced that we have dedicated an entire practice at Ventana Research to the topic. Large language models (LLMs) and generative AI (GenAI) have taken the AI world by storm. In fact, we assert that through 2026, one-half of all AI investments will be based on generative rather than predictive AI. My colleague Rob Kugel has written about how AI can improve productivity and benefit the...

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 improve business outcomes by operating more efficiently, accelerating product development and enhancing...

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.

I previously explained how master data management helps provide trust in data, making it one of the most significant aspects of an enterprise’s strategic approach to data management. More recently, I discussed how it has a role to play in accelerating data democratization as part of data intelligence initiatives. Along with data quality, MDM enables organizations to ensure data is accurate, complete and consistent to fulfill operational business objectives. While it is an established and mature...

I wrote recently about the role that data intelligence has in enabling enterprises to facilitate data democratization and the delivery of data as a product. Data intelligence provides a holistic view of how, when, and why data is produced and consumed across an enterprise, and by whom. This information can be used by data teams toensure business users and data analysts are provided with self-service access to data that is pertinent to their roles and requirements. Delivering data as a product...

Data and analytics have become increasingly important to all aspects of business. The modern data and analytics stack includes many components, which creates challenges for enterprises and software providers alike. As my colleague Matt Aslett points out, a better term might be modern data and analytics smorgasbord. There are arguments for and against using an assortment of tools versus a consolidated platform. For example, purchasing, integrating and deploying a variety of tools can be complex....