IBM Journey to AI blog

JANUARY 5, 2023

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Data Quality Data Lakes Data Warehouse Business Intelligence 52

IBM Data Science in Practice

NOVEMBER 28, 2022

How can organizations get a holistic view of data when it’s distributed across data silos? Implementing a data fabric architecture is the answer. What is a data fabric? Ensuring high-quality data A crucial aspect of downstream consumption is data quality.

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Data Pipeline Data Quality Data Preparation Data Governance 130

Precisely

JUNE 2, 2023

As companies strive to leverage AI/ML, location intelligence, and cloud analytics into their portfolio of tools, siloed mainframe data often stands in the way of forward momentum. Insufficient skills, limited budgets, and poor data quality also present significant challenges. To learn more, read our ebook.

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Data Silos ETL Data Quality Data Pipeline 52

Precisely

FEBRUARY 2, 2023

This requires access to data from across business systems when they need it. Data silos and slow batch delivery of data will not do. Stale data and inconsistencies can distort the perception of what is really happening in the business leading to uncertainty and delay.

Data Pipeline 52

Data Pipeline Data Silos Database Data Observability 52

IBM Journey to AI blog

MAY 15, 2024

Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges. This situation will exacerbate data silos, increase costs and complicate the governance of AI and data workloads.

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ODSC - Open Data Science

SEPTEMBER 29, 2023

A 2019 survey by McKinsey on global data transformation revealed that 30 percent of total time spent by enterprise IT teams was spent on non-value-added tasks related to poor data quality and availability. The data lake can then refine, enrich, index, and analyze that data.

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Data Lakes Clustering Big Data Big Data 52

The MLOps Blog

MAY 17, 2023

We also discuss different types of ETL pipelines for ML use cases and provide real-world examples of their use to help data engineers choose the right one. What is an ETL data pipeline in ML? Moreover, ETL pipelines play a crucial role in breaking down data silos and establishing a single source of truth.

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ETL Data Pipeline ML ML 59