Analyst Perspectives

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...

One of the promised benefits of artificial intelligence (AI), Generative AI (GenAI) and agents is that they can make everyone their own financial and business analyst. It’s true that these technologies can make it possible for everyone to access once hard-to-reach data (with suitable permissions), unleash agents to assemble the data into useful tables and charts along with commentary describing results and highlighting underlying drivers of results, propose next best actions and use natural...

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.

Databricks recently announced its Series J funding round, successfully raising $10 billion at a valuation of $62 billion. Led by Thrive Capital alongside high-profile investors such as Andreessen Horowitz and Insight Partners, the company intends to invest this capital towards new artificial intelligence (AI) products, acquisitions and significant expansion of its international operations. In the announcement, Databricks reported that it expects to achieve an annual revenue run rate of $3...

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...

I previously explained that data observability software has become a critical component of data-driven decision-making. Data observability addresses one of the most significant impediments to generating value from data by providing an environment for monitoring the quality and reliability of data on a continual basis. Maintaining quality and trust is a perennial data management challenge, the importance of which has come into sharper focus in recent years thanks to the rise of artificial...

Oracle announced significant updates to its Fusion Cloud Supply Chain & Manufacturing (SCM) software at the recently held Oracle Cloud World. The application suite includes procurement, inventory management, warehouse management, order management and transportation management. SCM tasks are mostly small-scale and repetitive, yet the processes they support are far from simple. They involve the intricate choreography of often complex activities that require the accurate communication and...

In today's rapidly evolving technological landscape, artificial intelligence (AI) governance has emerged as a critical ingredient for successful AI deployments. It helps build trust in the results of AI models, it helps ensure compliance with regulations and it is necessary to meet internal governance requirements. Effective AI governance must encompass various dimensions, including data privacy, model drift, hallucinations, toxicity and perhaps most importantly, bias. Unfortunately, we expect...

As I’ve written recently, artificial intelligence governance is a concern for many enterprises. In our recent ISG Market Lens study on generative AI, 39% of participants cited data privacy and security among the biggest inhibitors to adopting AI. Nearly a third (32%) identified performance and quality (e.g., erroneous results), and an equal amount (32%) mentioned legal risk.