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NOVEMBER 18, 2024

Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and data governance are the top data integrity challenges, and priorities. Leverage AI to enhance governance. Take a proactive approach.

Analytics 59

Analytics Analytics AI AI 59

ODSC - Open Data Science

JANUARY 9, 2024

So let’s dive in and explore 10 data engineering topics that are expected to shape the industry in 2024 and beyond. Data Engineering for Large Language Models LLMs are artificial intelligence models that are trained on massive datasets of text and code.

Data Engineering 97

Data Engineering Data Engineer Data Engineering Data Engineering 97

IBM Journey to AI blog

APRIL 10, 2023

To further the above, organizations should have the right foundation that consists of a modern data governance approach and data architecture. It’s becoming critical that organizations should adopt a data architecture that supports AI governance.

AI 103

AI AI Data Governance Data Pipeline 103

IBM Journey to AI blog

FEBRUARY 26, 2024

The financial services industry has been in the process of modernizing its data governance for more than a decade. But as we inch closer to global economic downturn, the need for top-notch governance has become increasingly urgent. That’s why data pipeline observability is so important.

Data Pipeline 112

Data Pipeline Data Engineering Data Engineering Data Engineer 112

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MARCH 7, 2024

Yet experts warn that without proactive attention to data quality and data governance, AI projects could face considerable roadblocks. Data Quality and Data Governance Insurance carriers cannot effectively leverage artificial intelligence without first having a clear data strategy in place.

Data Governance 72

Data Governance AI AI Data Quality 72

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FEBRUARY 9, 2024

In 2023, organizations dealt with more data than ever and witnessed a surge in demand for artificial intelligence use cases – particularly driven by generative AI. They relied on their data as a critical factor to guide their businesses to agility and success.

Data Quality 69

Data Quality DataOps Data Silos Artificial Intelligence 69

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MAY 31, 2023

Data Integrity Processes Run Where Data Lives Traditional data management solutions have required that data be brought to where the tools run. As a result, organizations have had to bring copies of their data to the tools – one copy for data quality, one copy for data governance, and so on.

Data Quality 52

Data Quality Data Governance Data Observability ETL 52

IBM Journey to AI blog

JULY 13, 2023

This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches.

Data Quality 74

Data Quality Data Profiling Data Governance Analytics 74

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SEPTEMBER 11, 2023

Artificial intelligence (AI) has many applications, ranging from software products to appliances to cars and everything in between. This includes automatically detecting over 300 semantic types, personally identifiable information, data patterns, data completion, and anomalies.

Data Quality 52

Data Quality AI AI Data Profiling 52

IBM Journey to AI blog

JULY 3, 2024

Multiple data applications and formats make it harder for organizations to access, govern, manage and use all their data for AI effectively. Scaling data and AI with technology, people and processes Enabling data as a differentiator for AI requires a balance of technology, people and processes.

AI 45

AI AI Data Quality Database 45

ODSC - Open Data Science

DECEMBER 3, 2024

In its essence, data mesh helps with data observability  — another important element every organization should consider. With granular access controls, data lineage, and domain-specific audit logs, data catalogs allow engineers and developers to have a better view of their systems than before.

Data Pipeline 40

Data Pipeline Data Governance Data Analyst Analytics 40

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APRIL 15, 2025

Leaders must act now Addressing skills gaps, investing in dedicated tools, and aligning governance practices are critical steps to ensure AI success and mitigate risk. Artificial intelligence (AI) and machine learning (ML) are transforming businesses at an unprecedented pace. Are You Ready for the Future of AI Observability?

Data Observability 69

Data Observability Data Quality Data Pipeline ML 69

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FEBRUARY 28, 2025

Data quality issues often present a significant challenge to data integrity. Inaccurate, non-standardized, and incomplete data diminishes the potential of business analytics, artificial intelligence, and machine learning, even in a best-case scenario. Next, well take a closer look at your datas role in AI success.

Data Quality 69

Data Quality Data Governance Data Observability Analytics 69