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Stardog Press Kit

pr@stardog.com

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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Articles

Apr 5, 2021

Is Your Data Ready for a Multicloud Strategy? Four Steps to Get You on the Right Track

“Every few weeks for the past year, there’s another “are you ready for multicloud?” checklist. These specs are very thorough and often include support, devops, policy, governance, and risk considerations. Good and necessary stuff, not to mention a great management tool for scalability. But when they’re missing key strategic considerations, they may well lead to a false sense of security. After all, if we’ve attended to everything on the checklist, one could assume they must be ready, right?

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Articles

Apr 5, 2021

Why the Next Generation of Data Management Begins with Data Fabrics

“The mandate for IT to deliver business value has never been stronger. In fact, 76% of executives believe IT must be an active partner in developing business strategy. Agility is key to success here. However, most enterprises are hampered by data strategies that leave teams flat-footed when the market shifts or new challenges arise. Take structured Data Management systems, for instance. This option worked well when the enterprise data landscape was itself predominantly structured.

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Articles

Mar 24, 2021

Knowledge graphs provide smart database management across hybrid computing environment

“The next challenge in data management is accessing data resources that are dispersed across a hybrid computing environment. Companies have invested in master data management solutions, breaking down silos and centralizing resources to simplify access. But moving data is costly, and silos serve a purpose by isolating secure data and allowing local control and governance. Gaining a comprehensive view of data across locations from on-premises out to the edge requires the merger of human and machine intelligence in solutions that connect data and leverage it to provide context in situ.

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