Remove Data Collection Remove Data Governance Remove Metadata
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SAP Datasphere Powers Business at the Speed of Data

Rocket-Powered Data Science

We live in a data-rich, insights-rich, and content-rich world. Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. As you would guess, maintaining context relies on metadata.

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What is data governance? Best practices for managing data assets

CIO Business Intelligence

Data governance definition Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. It encompasses the people, processes, and technologies required to manage and protect data assets.

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The What & Why of Data Governance

erwin

Modern data governance is a strategic, ongoing and collaborative practice that enables organizations to discover and track their data, understand what it means within a business context, and maximize its security, quality and value. The What: Data Governance Defined. Data governance has no standard definition.

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AI adoption in the enterprise 2020

O'Reilly on Data

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.

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Don’t Fear Artificial Intelligence; Embrace it Through Data Governance

CIO Business Intelligence

In this new era the role of humans in the development process also changes as they morph from being software programmers to becoming ‘data producers’ and ‘data curators’ – tasked with ensuring the quality of the input. Further, data management activities don’t end once the AI model has been developed. Addressing the Challenge.

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When is data too clean to be useful for enterprise AI?

CIO Business Intelligence

Good data governance has always involved dealing with errors and inconsistencies in datasets, as well as indexing and classifying that structured data by removing duplicates, correcting typos, standardizing and validating the format and type of data, and augmenting incomplete information or detecting unusual and impossible variations in the data.

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Pillars of Knowledge, Best Practices for Data Governance

Cloudera

And if data security tops IT concerns, data governance should be their second priority. Not only is it critical to protect data, but data governance is also the foundation for data-driven businesses and maximizing value from data analytics. But it’s still not easy.