Data Modeling Methodology for Data Fabric Success
The key to success with a data fabric is to approach it with an MVP mindset and do strictly the minimal work to accomplish the first significant tranche of business value. Read on to learn about a methodology that will ensure success.
Upgrade your Analytics with a Knowledge Graph
Are you currently managing a digital product or product line? If so, you may need a knowledge graph to improve your customer experience and retention.
How Data Fabrics Modernize Existing Data Management Investments
While previous technologies such as data lakes, data catalogs, and data integration platforms have promised to end data silos, the truth is, data silos are inevitable! They exist for very good reasons. They allow for local control and governance when it is important to a particular part of your business. Some data must be stored apart from other data to comply with legal regulation or simply for legacy business reasons.
How to Build a Semantic Search Engine Using a Knowledge Graph
In contrast to typical enterprise search solutions, knowledge graph-powered search returns fewer, more relevant results, reducing time spent searching up to 90%. A knowledge graph improves search by capturing the meaning of the search terms. Read on to learn how to build a semantic search application using a knowledge graph.
5 Steps to Building a Data Fabric
We hear from many of our customers that they’re interested in building a data fabric, but they’re not sure how to get started. Luckily, with a knowledge graph-based approach, you can start small and grow your data fabric over time. In this post, we’ll share an easy, approachable 5-step process for getting started with a data fabric.
Video interview: 2 Experts Discuss Graph and NLP
Two experts from Stardog and Stardog partner Lymba sat down to discuss trends in graph and NLP. Read on to find a transcript of some highlights from their conversation and a full video of the interview!
Quick Visual Analysis using Charts in Stardog Studio
Visualizing your data is a great way to eyeball your analysis and share the results with colleagues. Stardog is making this easier with a new feature in Studio: Stardog Charts.
Discover Connections in your Data Fabric with Stardog Explorer
With our new tool Stardog Explorer, it’s easier than ever to search, browse, and understand connected data. No querying, no code — just an intuitive interface that anyone can use. Read on to learn more.
How the Inter-American Development Bank Built FindIt, a Semantic Search Platform
Learn how IDB built FindIt, a semantic search platform that brings knowledge generated by the IDB Group to its staff and external audiences.
How ITV Improves Customer Experience through Optimizing Rights Management
Learn how ITV leverages Stardog to transform its rights service, maximizing exploitation and distribution opportunities and increasing viewer engagement.
Data Fabric 101: The Next Generation of Enterprise IT
Data fabric is ushering in a new era of enterprise IT. Data fabric offers a different way forward and one that’s a significant departure from the world of rows and columns in a table that we’re used to. But what outcomes does a data fabric really promise? Read on to learn more.
Stardog Explorer: Early Access Release
Announcing Stardog Explorer, a brand new way to easily explore the connections in your data. Powerful, intuitive new visualization and search capabilities make it easier for more types of users to benefit from your connected data in Stardog.
Analytics Modernization Success Stories
Contemplating upgrading your product portfolio with a knowledge graph? Read on to hear how Stardog customers have delivered delightful user experiences and new features to their customer bases.
Tracing a Solution to Enterprise Data Problems
It is important to be able to not only discover connections but explain them to your stakeholders. The ability to identify all links between seemingly unrelated data is called traceability. Learn how graph makes this easier than the relational model.
Conventional Data Integration is No Longer Sufficient
The original premise of data integration was focused on the correct goal of unifying data, but its execution was fundamentally flawed in that it sought to consolidate all enterprise data into one physical place. After fifty years of data warehouses and other ETL-based solutions, it’s clear that their promise for fast, complete analysis has fallen short.