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 […]
Deep Learning and Analytics: What is the Intersection?
Emergent artificial intelligence (AI) technologies, especially the automated algorithms populating analytics platforms, are impacting and reshaping the world of business analytics. The underlying connections between traditional analytics processes and the disruptive technologies will make you cheer if you happen to be a data scientist or a business analyst — because your redefined role in the […]
Decoded Data Lineage Helps Tackle Bad Data Quality
What are your outcome expectations of data lineage? No one’s just doing it for fun, after all. Generally speaking, data lineage is a major asset for: Regulatory reporting/governance; trust in decision-making; and, on-premise to cloud migrations. Data lineage tools track business data flow from originating source through all the steps in its lifecycle to destination. […]
Hybrid Database Architectures Lead the Way
Hybrid databases have evolved in the last decade, with a focus on cloud environments. In 2013, Gartner created the term “Hybrid Transaction/Analytical Processing” (or HTAP), which is defined by Gartner as: “An emerging application architecture that ‘breaks the wall’ between transaction processing and analytics. It enables more informed and ‘in business real time’ decision making.” […]
Data Intelligence on the Edge
The term “edge intelligence,” also referred to as “intelligence on the edge,” describes a new phase in edge computing. Organizations are using edge intelligence to develop smarter factory floors, retail experiences, workspaces, buildings, and cities. The edge has become “intelligent” by way of analytics that were formerly limited to the cloud or in-house data centers. […]