Stardog in the News

How Knowledge Graphs Can Deliver Insights When Integrating Decentralised Data

The majority of large data integration projects falter because systems are developed in a bespoke fashion, leaving organisations with critical applications that have been modified and transformed into a hornet’s nest of code that they cannot unravel for fear of breaking something. So, instead of trying to unravel systems, organisations have tended to duplicate processes, leading to more silos and custom solutions, which is an expensive proposition. Similarly, projects that end up as mass data cleansing and manipulation exercises are not sustainable in a world where organisations want insight into their data at speed.

Setting the Record Straight: Knowledge Graphs vs. Graph Databases

Although it might not be immediately discernible with all the marketing hype occluding this space, there are a number of pronounced distinctions between a true knowledge graph and a graph database.

2022 Trends in Data Modeling: The Interoperability Opportunity

The big data ecosystem is constantly expanding, gravitating ever further from the four walls of the traditional centralized enterprise with a burgeoning array of external sources, services, and systems. Capitalizing on this phenomenon requires horizontal visibility into data’s import for singular use cases—whether building predictive models, adhering to regulatory accords, devising comprehensive customer views and more—across a sundry of platforms, tools, and techniques.

Modern Data Integration Requires Hybrid Multicloud Data Fabric

What if data really is the fuel that powers engines of insight? Imagine a world exactly like ours in every way except for one key difference. Instead of the modern fossil fuel supply chain (i.e., Big Oil) being created before (or roughly coincident with) the mass production of automobiles (i.e., Ford, Volkswagen, etc.), the technology underlying actual race cars was perfected first.

The Analytics Magic of a Data Fabric

The data fabric concept has persisted over the past couple years as a means of providing centralized data access in an increasingly decentralized data landscape. Organizations have data scattered across various cloud, on-premise settings, and hybrids of the two. A data fabric, so the story goes, unites this data while granting singular access to it.

Why The Big Three Can't Win the Hybrid Multi-Cloud Game

The point of data is insight, and the point of insight is progress: lower costs, higher margins, more delighted customers, and in some cases, life-saving responses to emergent threats. What distinguishes a good data strategy from a bad data strategy is, to a first approximation, time to insight.

Data Management Strategy Is More Strategic than You Think

Data management strategy is boring, right? More to the point, it’s a solved problem. The relational model, SQL, and data warehouses date to the 1980s. I mean, what could be less strategic than the “what,” “how,” and “why” of an enterprise data management strategy? Build some pipelines and ETL jobs. Define a relational schema to cover big chunks of the business and drive analytics off the data warehouse OLAP cube queries. Easy right?