Tag: data analysis
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3 Benefits of Adopting ISO 20022 Standard in Your Organization
Learn how adopting the ISO 20022 standard transforms the financial industry by improving analytics, payments processing and time to market for payments. -
LLMs, Large Numbers and Noisy Data: Why Bigger Isn’t Always Better in AI
As we feed more data into LLMs and AI technologies, the noise and data biases will interfere with the final output. Businesses should go deeper than bigger to get the most out of their enterprise data. -
Real-Time Decisioning with Progress Corticon.js: Use Cases and Best Practices
Real-time decisioning is using data and analytics to make decisions in real time. Let’s get into the challenges and best practices you need to know. -
Progress DataDirect Achieves Google Cloud Ready—BigQuery Designation
Progress DataDirect has earned Google Cloud Ready—BigQuery Designation, demonstrating an expert-level ability to connect data analytics platforms and BigQuery. -
Tutorial: Using Apache Drill to Query SQL Server, Salesforce and More Using RDBMS Storage Plugin
Companies have come to depend on open source products like Apache Drill to meet their data storage and analysis needs. As data architectures become more distributed, it can be difficult to access data from a single application. Progress DataDirect offers a simple solution to this very complex business demand. -
Build Vs. Buy—What is the Key to Unlock the Million Dollar Analytics Castle?
It's a common question—how do you unlock your analytics potential? Should you build your own tools in-house, or invest in buying from a vendor? We analyze both options. -
10 Must Read Data Technology Articles from January 2017
We pulled together our 10 favorite articles from data scientists, visualizers, analyzers and developers. Dive in and propel your business forward in 2017! -
We Hope to See You at Strata + Hadoop World
Come see us at Strata + Hadoop World in San Jose on March 28 and learn the latest in big data technology and data science! -
A Deep Dive Into Data Enrichment
In this next installment of the data science series, we tackle data enrichment, the dirty-work of data science.
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