How to Strengthen Enterprise Data Governance with Data Quality

DATAVERSITY

If your organization is in a highly-regulated industry  or relies on data for competitive advantage  data governance is undoubtedly a top priority.  Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
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OTHER ON-DEMAND WEBINARS

Experience limitless analytics with Azure Synapse Analytics

View this webinar where our experts discussed the new era of analytics with the Microsoft Azure Synapse Analytics platform. It is a limitless analytics service with unmatched time to insight that bring together data integration, enterprise data warehousing and big data analytics – all into a single service.
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Gain Context, Insight and Value from Big Content

OpenText

The Internet of Things, social media and enterprise content all have one thing in common: they generate vast quantities of data — ever-faster. So much so that picking out relevant information and assembling it into meaningful patterns is a tall order. These new data sources also present another challenge. They generate unstructured data - such as text and emojis - that don’t fit neatly into traditional database structures. This data can however hold valuable insights on important and hard-to-quantify concepts such as social sentiment.
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Modernize Your Data Architecture to Deliver Oracle as a Service

robin.io

The migration to cloud-based data architectures continues at a rapid pace, including databases and data management. Oracle databases are part of this trend, and during this webinar you will learn how to automate the provisioning and management of Oracle databases so that you can deliver an “as-a-service” experience with 1-click simplicity. Experts will walk you through the process of
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Moving from centralized data platform to a federated data mesh

In this webinar, we will cover the pros and cons of building a centralized data lake vs federated data mesh. Traditionally data warehouses are built on the premise of centralized data. This requires team, process and tool alignment which adds significant complexity and layers of process. Oftentimes the internal conflicts lead to subpar data management and quickly fragments to siloed data processing and insights. We will provide our unbiased view on building federated data mesh and the benefits of building an operational metrics layer.
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