AI Modeling Data Quality ETL 
IBM Journey to AI blog
MAY 15, 2024
Business leaders risk compromising their competitive edge if they do not proactively implement generative AI (gen AI). However, businesses scaling AI face entry barriers. The explosion of data volume in different formats and locations and the pressure to scale AI looms as a daunting task for those responsible for deploying AI.
IBM Journey to AI blog
OCTOBER 22, 2024
The ability to effectively deploy AI into production rests upon the strength of an organization’s data strategy because AI is only as strong as the data that underpins it. Data must be combined and harmonized from multiple sources into a unified, coherent format before being used with AI models.
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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.
Towards AI
FEBRUARY 11, 2025
Author(s): Richie Bachala Originally published on Towards AI. Beyond Scale: Data Quality for AI Infrastructure The trajectory of AI over the past decade has been driven largely by the scale of data available for training and the ability to process it with increasingly powerful compute & experimental models.
IBM Journey to AI blog
JULY 3, 2024
By 2026, over 80% of enterprises will deploy AI APIs or generative AI applications. AI models and the data on which they’re trained and fine-tuned can elevate applications from generic to impactful, offering tangible value to customers and businesses.
Unite.AI
MAY 21, 2024
An additional 79% claim new business analysis requirements take too long to be implemented by their data teams. Other factors hindering widespread AI adoption include the lack of an implementation strategy, poor data quality, insufficient data volumes and integration with existing systems.
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