Remove Data Processing Remove Data Quality Remove Metadata Remove Strategy
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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.

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From Data Silos to Data Fabric with Knowledge Graphs

Ontotext

Data Fabric’ has reached where ‘Cloud Computing’ and ‘Grid Computing’ once trod. Data Fabric hit the Gartner top ten in 2019. The Data Fabric paradigm combines design principles and methodologies for building efficient, flexible and reliable data management ecosystems.

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Data Governance Maturity and Tracking Progress

erwin

Data governance is best defined as the strategic, ongoing and collaborative processes involved in managing data’s access, availability, usability, quality and security in line with established internal policies and relevant data regulations. Beginning strategy processes. erwin Data Intelligence.

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KGF 2023: Bikes To The Moon, Datastrophies, Abstract Art And A Knowledge Graph Forum To Embrace Them All

Ontotext

Atanas Kiryakov presenting at KGF 2023 about Where Shall and Enterprise Start their Knowledge Graph Journey Only data integration through semantic metadata can drive business efficiency as “it’s the glue that turns knowledge graphs into hubs of metadata and content”.

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Top 10 Data Lineage Podcasts, Blogs, and Magazines

Octopai

Data lineage is an essential tool that among other benefits, can transform insights, help BI teams understand the root cause of an issue, as well as help achieve and maintain compliance. Through the use of data lineage, companies can better understand their data and its journey. Agile Data. Agile Data. Techcopedia.

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Governing data in relational databases using Amazon DataZone

AWS Big Data

Amazon DataZone allows you to simply and securely govern end-to-end data assets stored in your Amazon Redshift data warehouses or data lakes cataloged with the AWS Glue data catalog. Second, the data producer needs to consolidate the data asset’s metadata in the business catalog and enrich it with business metadata.

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What you need to know about product management for AI

O'Reilly on Data

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. AI product estimation strategies. Imagine you are a data scientist at Disney. Features like geography and job seniority are critical to getting a good match.