A Data Engineering Guide reveals that while people often rely on the work of data engineers — depending on Siri for quick solutions or being enchanted by custom recommendations or promos — they often do not realize that these advanced tools can provide accurate results only because of the hard work put in by data […]
Data-Centric Architecture: Find Value with a Data Platform Approach
Applications provide a way to capture raw data in forms and store it in databases, and automated processes make it possible to extract meaning from that data using application programming interfaces (APIs). The current process-centric mindset assumes that the value resides in the automated processing, yet the limitations and costs inherent in its reliance on […]
Graph Databases vs. Key-Value Databases
Graph databases and key-value databases have very different features and are used for accomplishing different tasks. Key-value databases are streamlined and fast, but are limited and not as flexible. Graph databases, on the other hand, are very flexible and great for research, but not terribly fast. Both typically use a non-relational foundation. The two key […]
Scaling the Analytics Team: Developing Key Roles
In an enterprise analytics team, different roles exist to fill different needs, and those needs must be met in order to be successful. Launching an analytics program doesn’t necessarily require a massive influx of personnel before producing usable insights from data, yet it’s important that critical roles are filled, whatever the size of the team. […]
Case Study: Tracking and Tracing Drugs in the Pharmaceutical Supply Chain
Failures or lack of visibility in the many-tiered pharmaceutical supply chain have multiple repercussions. Drug shortages have adverse economic and clinical effects on patients — they are more likely to have increased out-of-pocket costs, rates of drug errors, and, yes, mortality. Hospitals and health systems allocate over 8.6 million hours of additional labor hours to […]
Business Intelligence Meets Metadata Challenges
Many BI managers, CEOs, and CIOs cannot afford to add more staff, so they are seeking technologies that can help their existing teams operate more accurately and efficiently. They “need to change the physics, as we call it. They can’t just add more people to the team,” said Amnon Drori in a recent DATAVERSITY® interview. […]
Optimizing the Data Warehouse
The data warehouse, a relational database technology, makes all enterprise information actionable, and will continue to be prominent as a Data Architecture component. In the 2000s, a typical business would consolidate data from multiple relational databases, centralizing all this information through a data warehouse, and consequently streamlining business tasks. However, the business context has shifted […]
Understanding DataOps
DataOps (data operations) has its roots in the Agile philosophy. It relies heavily on automation, and focuses on improving the speed and accuracy of computer processing, including analytics, data access, integration, and quality control. DataOps started as a system of best practices, but has gradually matured to a fully functional approach for handling data analytics. […]
Data Virtualization Use Cases
Data virtualization, in a nutshell, utilizes data integration without replication. In this process, a single “virtual” data layer is created to provide data services to multiple users and applications at the same time. Why Data Virtualization Is a Necessity for Enterprises explains how data virtualization helps tackle data movement challenges by making a virtual dataset […]
The Future of Data Architecture
Anthony J. Algmin believes Data Architecture is moving from a time of chaos and tangles into something more clean and organized. Speaking at the DATAVERSITY® Data Architecture Online Conference, Algmin looked at past predictions, current hot topics, and predictions for the future. He is the Founder and CEO of Algmin Data Leadership. A Quick Look […]