O'Reilly Media - Data
JULY 23, 2018
Scalable Machine Learning for Data Cleaning. Over the last few years, many companies have begun rolling out data platforms for business intelligence and business analytics. Case studies. Can decentralization technologies (like blockchains) pave the way for new forms of data exchanges?
Perficient
FEBRUARY 28, 2023
Academic Medical Center Case Study This was an academic medical center that was running into challenges to integrate large data sets securely and quickly to make the data usable and valuable to its providers and researchers.
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Datavail
FEBRUARY 19, 2019
Strong business intelligence and analytics capabilities are essential for the modern business. Despite the many advantages of BI and analytics, businesses continue to run into roadblocks with adoption. Optimizing OBIEE: Case Study. Benefits of OBIEE.
Apps Associates
OCTOBER 8, 2021
They provide content, assessment and digital services to learners, educational institutions, employers, governments and other partners globally – read the full case study here. Timelines continues to accelerate as organizations are realizing that they need the flexibility and scalability of Cloud technology.
Datavail
JUNE 21, 2022
If you are involved with managing data, analytics, or business intelligence, you know that your organization is going to increase its appetite for making decisions based on its large reservoir of data. Your data management strategy will need to be more scalable than ever. Here are the top challenges you’ll need to address.
Datavail
MARCH 12, 2020
Meanwhile, Essbase on-premise came bundled as part of Oracle Business Intelligence Enterprise Edition (OBIEE), up until the Essbase 12c version of the software. The client in our case study was paying hundreds of thousands of dollars every month in maintenance fees while using the on-premise version of Essbase.
Datavail
JULY 14, 2022
It is constantly feeding corporate databanks with up-to-the-minute information, yet because its flow is transitory, the intelligence it offers isn’t available for use until after it’s been captured, cleaned, and integrated into standard data analysis programs. The process is fast, fault-tolerant, and scalable.
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