Matt Aslett's Analyst Perspectives

Domo is best known as a business intelligence (BI) and analytics software provider, thanks to its functionality for visualization, reporting, data science and embedded analytics. Additionally, as I recently explained, the company’s platform addresses a broad range of capabilities that includes data governance and security, data integration and application development, as well as the automation and incorporation of artificial intelligence (AI) and machine learning (ML) models into BI and...

Data governance has always been a critical part of the data and analytics landscape. However, for many years, it was seen as a preventive function to limit access to data and ensure compliance with security and data privacy requirements. To fulfill today’s data-driven agendas, many enterprises need an evolved perspective on data governance. The development of new applications driven by artificial intelligence requires a more agile and collaborative approach to data governance—one that automates...

Natural language interfaces for business intelligence products existed long before the emergence of generative artificial intelligence. Large language models have allowed BI providers to accelerate the delivery of functionality to convert natural language questions into analytic queries and generate summarizations and recommendations from data and charts. Features that enable natural language query and natural language generation are now ubiquitous.

In an earlier Analyst Perspective, I discussed data democratization’s role in creating a data-driven enterprise agenda. Building a foundation of self-service data discovery, data-driven organizations provide more workers with the ability to analyze and use data. I’ve also examined how generative artificial intelligence (GenAI) could revolutionize business intelligence software by using natural language interfaces to lower the barriers to working with analytics software. Today, however, data...

As enterprises embrace the potential opportunities presented by artificial intelligence (AI), they are quickly finding that good data management is a prerequisite. As was explained in ISG’s State of Generative AI Market Report, AI requires data that is clean, well-organized and compliant with regulatory standards. There are multiple challenges to delivering AI-ready data, including combining structured and unstructured data, ensuring that the combined data can be trusted, and validating that...

Increased enterprise focus on artificial intelligence (AI) and generative AI (GenAI) has served to sharpen the focus on the need for trusted data and reliable analytics and data operations. The ISG State of Generative AI Market Report highlighted that elevated expectations and demands associated with AI are a forcing function for enterprises to take long-overdue steps to improve data and analytics processes to ensure that data that is clean, well-organized and compliant with regulatory...

Data catalogs provide an inventory of data assets that surface metadata from data platforms, analytics tools and applications that can be used to facilitate data discovery and data usage across an enterprise. As I recently explained, however, there are actually multiple types of data catalogs that offer functionality to address specific use cases and user roles, including data inventory, data discovery and data governance. The data intelligence catalog is an emerging category that combines...

It is now more than two years since the launch of ChatGPT introduced the world to generative AI (GenAI) and large language models (LLMs). GenAI-based assistants and co-pilots are now widely adopted, with individuals and enterprises adopting GenAI models to automate the generation of text, digital images, audio, video and code, amongst other things.

Despite all the interest in artificial intelligence (AI) and generative AI (GenAI), ISG’s Buyers Guide for Data Platforms serves as a reminder of the ongoing importance of product experience functionality to address adaptability, manageability, reliability and usability. While new and emerging capabilities might catch the eye, features that address data platform security, performance and availability remain some of the most significant deal-breakers when enterprises are considering potential...

Too often, enterprises find that data is distributed across multiple silos on-premises and in the cloud. More than two-thirds of participants in ISG’s Market Lens Cloud Study are using a hybrid architecture involving both on-premises and cloud infrastructure for analytics and artificial intelligence deployments. Unifying data to achieve operational and analytic objectives requires complex data integration and management processes. Fulfilling these processes requires a smorgasbord of tools aimed...