Stardog in the News

Overcoming Data Integration Challenges to Unleash Massive Potential: Knowledge Graphs 101

Data is full of subjective human choices and human values. An enterprise data set might seem like objective fact–like a hard, fixed, immutable thing that represents the world exactly as it is. In reality, it’s more instrumental than all that. Enterprise data represents something; it re-presents some part of the world and we manipulate the data largely to manipulate the world.

Enabling Federated Querying & Analytics While Accelerating Machine Learning Projects

By logically connecting enterprise data, knowledge graphs identify key data elements that make sense to the business. In fact, analytics applications that point to an enterprise knowledge graph powered semantic data layer offer end users better and faster services by reaching directly into the data lake. Knowledge graphs can also make it simpler to feed sound and rich data into ML algorithms.

Key Enterprise Knowledge Graph Examples for Data Quality Success

Enterprise knowledge graphs can help enforce data integrity, improve data correctness, and ensure data consistency. With these safeguards in place, organizations and end-users can clean up their data and rest easy knowing they are receiving complete and accurate results that they can use to solve their business needs.

How Semantic-Based Knowledge Graphs Accelerate the Value of Data Lakes

Whether performing advanced analytics to drive decision-making or modeling complex relationships against data that is both too wide and big to describe people, places, things, and how they relate, knowledge graphs are making a difference in how information is found, used, and leveraged.

PODCAST: Knowledge Graphs 101 with Kendall Clark, Founder & CEO of Stardog

What happens when an analyst has a new idea about how to organize and understand data? They have to start from scratch. They have to recreate a new version of the data, a new data set. And then, they have to go through the process of remodeling, retransforming and resummarizing the data all over again. But what if they didn’t have to? What if we could make data modeling far more flexible?

5 Steps to Implementing a Modern Data Fabric Framework

If your organization wants a modern data management strategy, consider following the five fundamental steps to implementing a data fabric framework described here.

Leveraging Knowledge Graph Technology to Fuel Advanced Analytics

Since the early 1990s, organizations have been collecting, storing, analyzing and reconfiguring a plethora of information, only to find 20 years later that they are still struggling to maximize their big data investments. The challenge remains clear for organizations of all sizes; they must have the ability to connect data and deliver analytics solutions to help solve urgent business problems.