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Why data governance is essential for enterprise AI

IBM Big Data Hub

Because of this, when we look to manage and govern the deployment of AI models, we must first focus on governing the data that the AI models are trained on. This data governance requires us to understand the origin, sensitivity, and lifecycle of all the data that we use. and watsonx.data.

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How to Deliver Data Quality with Data Governance: Ryan Doupe, CDO of American Fidelity, 9-Step Process

Alation

In Ryan’s “9-Step Process for Better Data Quality” he discussed the processes for generating data that business leaders consider trustworthy. To be clear, data quality is one of several types of data governance as defined by Gartner and the Data Governance Institute. Step 4: Data Sources.

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Data Preparation and Data Mapping: The Glue Between Data Management and Data Governance to Accelerate Insights and Reduce Risks

erwin

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. But the attempts to standardize data across the entire enterprise haven’t produced the desired results.

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IBM named a leader in the 2022 Gartner® Magic Quadrant™ for Data Quality Solutions

IBM Big Data Hub

Data observability capability makes data quality checks upstream possible. Data Governance. Ensuring data quality is critical for data governance initiatives.

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What is Data Lineage? Top 5 Benefits of Data Lineage

erwin

Many large organizations, in their desire to modernize with technology, have acquired several different systems with various data entry points and transformation rules for data as it moves into and across the organization. The CEO also makes decisions based on performance and growth statistics. Data Governance.

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The Role of AI and ML in Model Governance

Alation

In part one of this series, I discussed how data management challenges have evolved and how data governance and security have to play in such challenges, with an eye to cloud migration and drift over time. All Machine Learning uses “algorithms,” many of which are no different from those used by statisticians and data scientists.

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Don’t let your data pipeline slow to a trickle of low-quality data

IBM Big Data Hub

To help companies avoid that pitfall, IBM has recently announced the acquisition of Databand.ai, a leading provider of data observability solutions. The data observability difference . Data observability will also integrate with these other use cases for improved results where both are applied.