Stardog in the News

Speeding Time to Value: The ‘Just in Time’ Data Analytics Stack

The notion of just-in-time (JIT) analytics is one of the latest developments throughout the data landscape. This novel concept not only eliminates many of the conventional limits that have hampered enterprise use of analytics, it also spurs a newfound capability for continuous data intelligence that drastically increases the value derived from analyzing data.

The Turning Point for Enterprise Artificial Intelligence

Instead of different branches of AI competing with each other in vendor solutions, the industry has reached a point of inflection in which there are more offerings “doing new school AI, i.e. statistical learning, machine learning, what we call machine learning and also, at the same time, and we’ve worked on this so it all works together seamlessly, they’re also doing that symbolic or rules-based AI,” Stardog CEO Kendall Clark commented.

Turning data into gold: Knowledge graphs, AI, and machine learning

“There’s an analytics capability on one side, and a data integration or data prep capability on the other side. And you can think about the interaction of them as almost a third capability,” said Kendall Clark, of knowledge graphs.

2022 Trends in Data Strategy: A New Archetype

…as Stardog CEO Kendall Clark termed it, “we’re in a corner; the only way out is alternatives to the physical consolidation of data.”

2022 Predictions Round-up: Data Management & Data Science Analytics

Stardog founder and CEO Kendall Clark has been quoted in various articles predicting trends in data management, data science and analytics.

Data Analytics Stack Goes Multicloud in 2022: Three Trends to Watch

The modern data analytics stack is undergoing shocks as the requirements imposed by a hybrid multicloud world upon data analytics become more apparent. What’s ahead in 2022?

Improving Machine Learning: How Knowledge Graphs Bring Deeper Meaning to Data

Graph models help provide capabilities like improved feature engineering, root cause analysis, and graph analytics. This functionality is key to helping knowledge graphs transition to the dominant data management construct as data management and AI converge.