Business Intelligence Data Silos ETL 
The MLOps Blog
MAY 17, 2023
However, efficient use of ETL pipelines in ML can help make their life much easier. This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines.
IBM Journey to AI blog
OCTOBER 22, 2024
The data universe is expected to grow exponentially with data rapidly propagating on-premises and across clouds, applications and locations with compromised quality. This situation will exacerbate data silos, increase pressure to manage cloud costs efficiently and complicate governance of AI and data workloads.
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IBM Journey to AI blog
MARCH 14, 2024
Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.
Pickl AI
NOVEMBER 15, 2023
This centralization streamlines data access, facilitating more efficient analysis and reducing the challenges associated with siloed information. With all data in one place, businesses can break down data silos and gain holistic insights. It often serves as a source for Data Warehouses.
IBM Journey to AI blog
JANUARY 5, 2023
The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases.
IBM Journey to AI blog
JULY 3, 2024
Open is creating a foundation for storing, managing, integrating and accessing data built on open and interoperable capabilities that span hybrid cloud deployments, data storage, data formats, query engines, governance and metadata. With an open data lakehouse, you can access a single copy of data wherever your data resides.
IBM Journey to AI blog
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. A read-optimized platform that can integrate data from multiple applications emerged.
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