Stardog in the news

Stardog Press Kit

pr@stardog.com

Press Releases

Jun 2, 2021

Stardog Appoints Data Management Expert Yancy Oshita as CMO

ARLINGTON, Virginia (June 2, 2021) – Stardog, the leading Enterprise Knowledge Graph platform provider, today announced the appointment of Yancy Oshita as Chief Marketing Officer (CMO). With over 25 years experience building and scaling enterprise SaaS marketing teams, including as CMO at Wimba (acquired by BlackBoard), Catchpoint, and Zags, he will be responsible for driving Stardog’s marketing strategy and helping customers to take advantage of data fabric as the foundation for their data management strategy.

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Articles

May 7, 2021

Philosopher turned data scientist: Stardog CEO Kendall Clark is shattering data silos

“The spark that birthed Stardog came about easy-rider style. One cold winter night in 2003, data scientist Kendall Clark was “freezing on a motorcycle ride” when he came up with an idea to future-proof data management. At the time Clark was working at an artificial intelligence research lab in Arlington, VA, exploring whether the World Wide Web could be the basis for a new kind of data integration system. Now the co-founder and CEO of Stardog, Clark has entrusted a philosophical approach to cloud-native computing.

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Articles

May 7, 2021

Stardog CEO Kendall Clark featured on DM Radio

Listen to Stardog CEO on the DM Radio show episode “What About Our (Data) Relationship?” and learn more about the episode, below! “We all have a relationship with data these days. We either use it strategically via some analytical tool; or we create the data for others to leverage. Increasingly, we’re realizing that data is not just powerful, it’s personal. There are conversations about data ownership, data stewardship. And what about using data for AI?

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Articles

Apr 23, 2021

The Intersection of Data Democratization and Security

“Ways to improve data use are also considered from a range of perspectives by additional industry leaders. FICO’s Tim VanTassel looks at the challenges posed by the democratization of analytics and then provides a practitioner’s guide to analytic model development. Machine learning is useful, he emphasizes, but a combination of information, explainability, and wisdom is critical. In addition, data integration systems leverage semantic graphs and data virtualization to represent connectedness and unlock business value, observes Stardog’s Kendall Clark in an article on the new integration landscape.

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Articles

Apr 23, 2021

Semantic Graphs and the New Data Integration Landscape

“Conventional data management systems are fundamentally ill-suited for the world of data as it exists today. These systems, based with few exceptions on the relational data model, are broken because they integrate based on data location at the storage layer. While this approach worked reasonably well for the past 25 years, the world today has far too much data to use data location in storage as the basic lever. The ill-suitedness of traditional, relational data model-based data integration tools reveals itself in several ways.

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Articles

Apr 22, 2021

Why Inference is Key to Realizing Data’s Full Potential

“Leveraging a knowledge graph’s inference capabilities, organizations can extrapolate new data connections and explain any new connection they create. Digital transformation is all the rage, and in most cases, the goal of digital transformation is to treat data like an asset. In some instances, that means monetizing data, and in others, the goal is to leverage data more efficiently to derive insight to make better decisions. However, in reality, both are hard to achieve.

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Articles

Apr 7, 2021

Data Modeling Mastery for AI and Beyond

“An inordinate amount of some of the most vital aspects of Artificial Intelligence—from data engineering to data science, data preparation to machine learning—rely on one indispensable prerequisite: data modeling. Without effective data modeling, organizations can’t integrate data across sources to build advanced analytics models. Data modeling is foundational to assembling training datasets, utilizing specific data for end user applications, and scaffolding predictive cognitive computing models. Consequently, it behooves companies to make the modeling process as efficient as possible to achieve the following three benefits that optimize their modeling endeavors—and the advanced analytics applications and use cases they support.

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