Analyst Perspectives

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...

The degree to which data platforms are critical to efficient business operations cannot be overstated. Without data platforms, enterprises would be reliant on a combination of paper records, time-consuming manual processes and huge libraries of physical files to record, process and store business information. The extent to which that is unthinkable highlights the level at which today’s enterprises and society as a whole rely on data platforms. The core persistence, management, processing and...

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...

The adoption of cloud environments for analytic workloads has been a key feature of the data platforms sector in recent years. For two-thirds (66%) of participants in ISG’s Data Lake Dynamic Insights Research, the primary data platform used for analytics is cloud based. Many enterprises adopted cloud-based analytic data platforms with a view to improving operational efficiencies by reducing the need for upfront investment in physical infrastructure as well as the ability to scale cloud services...

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 recently noted, the term “data intelligence” has been used by multiple providers across analytics and data for several years and is becoming more widespread as software providers respond to the need to provide enterprises with a holistic view of data production and consumption. I assert that through 2027, three-quarters of enterprises will be engaged in data intelligence initiatives to increase trust in their data by leveraging metadata to understand how, when and where data is used in...

I previously wrote about data mesh as a cultural and organizational approach to distributed data processing. Data mesh has four key principles—domain-oriented ownership, data as a product, self-serve data infrastructure and federated governance—each of which is being widely adopted. I assert that by 2027, more than 6 in 10 enterprises will adopt technologies to facilitate the delivery of data as a product as they adapt their cultural and organizational approaches to data ownership in the...

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.