Analyst Perspectives

I have previously explained how increased enterprise focus on artificial intelligence (AI) and agentic AI is a forcing function for enterprises to take long-overdue steps to improve data management and data governance. Data is integral to AI: large volumes of data are required to train models, while data freshness is important for inferencing in interactive applications and data quality is fundamental to ensuring that the output of agentic and generative AI (GenAI) initiatives can be relied...

A little under a year ago, I explained how Google was positioning its BigQuery product as a unified data platform for processing data in multiple formats, across multiple locations, for multiple use cases—including business intelligence (BI) and artificial intelligence (AI)—using a combination of multiple data engines, including SQL, Spark and Python. The evolution of BigQuery as the focus of Google’s analytics and AI offerings continued at the recent Google Cloud Next ‘25 event, with Google...

The artificial intelligence (AI) landscape is undergoing dramatic transformation, with enterprises rapidly adapting to a world where AI is no longer just a possibility but a necessity. ISG Market Lensresearch of 300 enterprises shows that AI initiatives were the second largest category of IT spending for 2024, behind customer experience initiatives. Furthermore, enterprises plan to increase 2025 IT spending on AI initiatives by 5.7%, which is more than any other category and nearly three times...

The six costliest words in managing a finance department are, “We’ve always done it this way.” The record-to-report (R2R) cycle describes the process of finalizing and summarizing the financial activities of a business for a specific accounting period—typically a month, quarter or fiscal year. It is important to note that R2R exclusively covers the activities between recording (keeping the books) and reporting (publishing financial statements and management accounts). It involves completing...

I recently described how business data catalogs are evolving into data intelligence catalogs. These catalogs combine technical and business metadata and data governance capabilities with knowledge graph functionality to deliver a holistic, business-level view of data production and consumption. The concept of the knowledge graph has been part of the data sector for decades, but adoption has typically been limited to industries and enterprises focused on the Semantic Web, such as media,...

We live in a time of uncertainty, not unpredictability. Especially when a business finds itself on an undefined journey with an unclear destination—whether caused by internal events or the world at large—having plans to deal with a range of outcomes increases the odds of success. Or, at least enduring the least amount of damage. Managing an organization in uncertain times is always hard, but tools are available to improve the odds of success by making it easier and faster to plan for...

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...

Founded as Software Development Laboratories in 1977, Oracle is a behemoth in the software industry, generating more than $50 billion in revenue in its fiscal year 2024. Originally focused solely on the relational database market, the software provider operated as Relational Systems, Inc. for several years before adopting the name Oracle in 1982. The company went public in 1986 and became one of the largest software providers in the world, eventually amassing a large portfolio of business...

It has been a little over a decade since the term data operations entered the analytics and data lexicon. It describes the application of agile development, DevOps and lean manufacturing by data engineering professionals in support of data production. DataOps was initially seen as antithetical to traditional data management approaches, which typically included batch-based and manual tools and practices. The term was embraced by emerging software providers as a means of differentiating from...

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 a defining moment for the artificial intelligence (AI) sector, the recent breach involving Chinese company Liang Wenfeng (makers of DeepSeek AI) has shed light on the emerging cyber threats that challenge the industry. The reported incident revealed that DeepSeek AI had left its ClickHouse database accessible to the public, exposing over one million lines of log entry data. This data included chat histories, secret keys and other sensitive information. Further complicating matters, Microsoft...