Business Intelligence Data Architecture Marketing Metadata 
IBM Big Data Hub
APRIL 10, 2023
A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.
erwin
JANUARY 9, 2020
That’s because it’s the best way to visualize metadata , and metadata is now the heart of enterprise data management and data governance/ intelligence efforts. So here’s why data modeling is so critical to data governance. erwin Data Modeler: Where the Magic Happens.
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CIO Business Intelligence
MARCH 6, 2024
The company is expanding its partnership with Collibra to integrate Collibra’s AI Governance platform with SAP data assets to facilitate data governance for non-SAP data assets in customer environments. “We We are also seeing customers bringing in other data assets from other apps or data sources.
AWS Big Data
AUGUST 31, 2023
Amazon Redshift is a fast, fully managed petabyte-scale cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. Iceberg stores the metadata pointer for all the metadata files.
IBM Big Data Hub
JANUARY 18, 2023
Data platform architecture has an interesting history. Towards the turn of millennium, enterprises started to realize that the reporting and business intelligence workload required a new solution rather than the transactional applications. While this is encouraging, it is also creating confusion in the market.
IBM Big Data Hub
JULY 13, 2023
The more complete, accurate and consistent a dataset is, the more informed business intelligence and business processes become. Geocoding Geocoding is the process of adding location metadata to an organization’s datasets. Learn more about designing the right data architecture to elevate your data quality here.
Octopai
APRIL 8, 2020
The worldwide economy was shaken in 2007 when the United States stock market had its largest drop since the Great Depression. While there are many factors that led to this event, one critical dynamic was the inadequacy of the data architectures supporting banks and their risk management systems.
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