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Five benefits of a data catalog

IBM Journey to AI blog

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

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Build trust in banking with data lineage

IBM Journey to AI blog

This trust depends on an understanding of the data that inform risk models: where does it come from, where is it being used, and what are the ripple effects of a change? Moreover, banks must stay in compliance with industry regulations like BCBS 239, which focus on improving banks’ risk data aggregation and risk reporting capabilities.

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Data architecture strategy for data quality

IBM Journey to AI blog

Monitor and identify data quality issues closer to the source to mitigate the potential impact on downstream processes or workloads. Efficiently adopt data platforms and new technologies for effective data management. Apply metadata to contextualize existing and new data to make it searchable and discoverable.

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Unfolding the difference between Data Observability and Data Quality

Pickl AI

Data Transparency Data Transparency is the pillar that ensures data is accessible and understandable to all stakeholders within an organization. This involves creating data dictionaries, documentation, and metadata. It provides clear insights into the data’s structure, meaning, and usage.

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Google experts on practical paths to data-centricity in applied AI

Snorkel AI

Generally, data is produced by one team, and then for that to be discoverable and useful for another team, it can be a daunting task for most organizations. Even larger, more established organizations struggle with data discovery and usage. But in other cases, as much as you can automate, the better you are.

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Google experts on practical paths to data-centricity in applied AI

Snorkel AI

Generally, data is produced by one team, and then for that to be discoverable and useful for another team, it can be a daunting task for most organizations. Even larger, more established organizations struggle with data discovery and usage. But in other cases, as much as you can automate, the better you are.