Industrial environments are rich data sources, from equipment pressure and temperature readings to real-time inventory levels. This ocean of data provides organizations with valuable insights when (and if) effectively harnessed. By transforming raw data generated across the floor 24/7 into actionable intelligence, industrial plants are equipped with data-informed insights necessary to create operational strategies that […]
Generative AI and Data: Using Knowledge Graphs and RAG Together
Generative AI has huge potential, but it also faces problems. If generative AI creates information that is not factually accurate in response to a user request – resulting in so-called hallucinations – it can have a big impact on users. Relying on large language model (LLM) training data on its own is not enough to prevent […]
Cloud Computing vs. Data Security
Cloud computing has, in recent years, become both an essential service used in many industries and a ubiquitous part of the daily lives of consumers. By offering remote access to computing services that can be rented out on a flexible, efficient, as-needed basis, it gives companies access to greater computer power and storage capabilities than […]
Data Modeling and Data Models: Not Just for Database Design
“The main purpose of a data model is actually not to design a database – it’s to describe a business,” said Christopher Bradley, information strategist at DMA Advisors. Bradley spoke at a recent Data Architecture Online conference about the purpose of Data Modeling and its role in Data Governance and the modern successful business. Are […]
ADV Webinar: Every Database Is Multi-Modal – What Does This Mean to an Enterprise?
Download the slides here>> About the Webinar Today’s enterprises have an unprecedented variety of data store choices to meet the needs of their varied workloads. Because there is no one size fits all when it comes to data stores, this can lead to confusion and chaos. Enterprises have many needs for databases, including cache, operational, data […]
Knowledge Graphs: Context, Compliance, and Connections
“Graph is leaving a larger and larger footprint. And that is good,” said Thomas Frisendal in Knowledge Graphs and Data Modeling. Gartner named knowledge graphs as part of an emerging trend toward digital ecosystems, showing relationships among enterprises, people, and things, and enabling seamless, dynamic connections across geographies and industries. Elisa Kendall and Deborah McGuinness, […]
What Is a Graph Database? Definition, Types, Uses
A graph database (GDB) models data as a combination of nodes (vertices) and edges (relationships) with equal importance. Businesspeople query these structures to reveal patterns and insights within the data and their associations. These would be difficult to discern from other data visualizations, such as tables, charts, and documents. Since humans naturally think by associating one concept with another, people […]
Fundamentals of Dimensional Data Modeling
In today’s data-driven business environment, organizations demand reliable and stable business insights to make informed decisions. To cater to this demand, over 60% of companies turn to data warehouses (DWs) to store, manage, and analyze their data efficiently. The success of these DW implementations depends on dimensional data modeling – an analytical approach that organizes and categorizes data for efficient analysis and […]
How a Neuro-Symbolic AI Approach Can Improve Trust in AI Apps
As a cognitive scientist, I’ve been immersed in AI for more than 30 years – specifically in speech and natural language understanding, as well as machine-based learning and rule-based decision-making. Progress in our field is always uneven, unfolding in fits and starts. Those of us in the AI field have witnessed multiple “AI winters” over the […]
Running Generative AI in Production – What Issues Will You Find?
As your data projects evolve, you will face new challenges. For new technology like generative AI, some challenges may just be variations on traditional IT projects like considering availability or distributed computing deployment problems. However, generative AI projects are also going through what Donald Rumsfeld once called the “unknown unknowns” phase, where we are discovering […]