Analyst Perspectives

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.

The final of the men’s 100 meters at the Paris Olympics this summer was a reminder that being successful requires not just being fast but performing at the right time. Being fast is obviously a prerequisite for participating in an Olympic 100-meter final, and all the competitors finished the race in under 10 seconds, with just 0.12 seconds separating the first man from the last. While all the athletes were fast, what separated the winner of the gold medal—USA’s Noah Lyles—was execution. He was ...

Enterprises face a bewildering level of choice in relation to data platforms, as evidenced by the number of software providers and products assessed in our recent Data Platforms Buyers Guide. There are numerous data platform providers and products to choose from, but also a diverse array of functional and architectural options. Is the workload primarily operational or analytic? Will it be deployed on-premises or in the cloud? Should it be distributed or centralized? Data warehouse or data...