Six Critical Costs of External Data Integration
External data integration is critical in giving your organization the leading edge in making business decisions
Topic: Big Data
Big Data technologies and use cases for real-time analytics. Big Data technologies, market insights, and use cases for real-time analytics.
Why Do AI Projects Fail and How Do You Avoid That from Happening?
In this Q&A interview, Aible and Intel provide insights not only on how to avoid AI project failures but how they deliver guaranteed results from AI within 30 …
Real-time Analytics News for Week Ending August 20
In this week's real-time analytics news: Cloudera announced the launch of Cloudera Data Platform (CDP) One, an all-in-one data lakehouse SaaS offering for …
How to Trust Your Location Intelligence: Three Evaluation Criteria
Not all location intelligence data is created equal. There are nuances and, sometimes, inherent biases. Here are three criteria for evaluating the available …
Enhancing Collaborative Application Development For Databases
Managing database access can be a bane of many organizations developing modern applications. RHODA simplifies the process while offering the visibility, …
Real-Time Data Streaming Delivers, and the Data Finally Shows It
Harnessing the full power of real-time data streams delivers great value, but is difficult for many organizations. The top hurdle is integrating multiple data …
Create Control Without Constraint in Distributed Data Management
Most data stacks begin governance at the warehouse, but they don't know where that ELT data came from and what the context and source is. We need to fix
The Unstructured Data Management Maturity Index
As data management matures, unstructured data evolves from being a storage cost center to sitting at the epicenter of value
Location, Location, Location Matters with Data, Too
Data location is increasingly important as it has consequences related to governance, application performance, and
What’s Changed for Data Scientists This Decade?
Many businesses have become over encumbered with data, leading to data silos and inaccurate data processing. This has changed the role of the data scientist …