The Many Faces of Metadata Management

Few things bring down the value of data faster than confusion and uncertainty about what it is, where it came from, and whether it is good quality data. Yet as more users seek to access and interact with data and reports for business intelligence and analytics and as data sources become larger and more varied, confusion and uncertainty spread fast. Executives, managers, regulatory administrators, and other key personnel cannot rely on their reports, KPIs, and dashboards. Users cannot even find reports that the organization is producing. Instead, users spend more of their time trying to locate data and reports and correcting mistakes than they do applying data insights to solve business problems.
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OTHER ON-DEMAND WEBINARS

Riding the Data Privacy Wave: How Will You Stay Afloat?

IBM

Data privacy regulation is bigger than just GDPR. Other countries and jurisdictions are enacting their own versions of the data privacy regulation, each with subtle nuances - such as the California Consumer Privacy Act (CCPA), Lei Geral de Proteção de Dados (LGPD), and more - and that’s on top of existing privacy regulations. Moreover, consumers increasingly expect more protection for their sensitive information. A recent IBM-Harris poll of 10,000 individuals revealed that 75% of consumers won’t buy from companies they don’t trust no matter how great their product or service.
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Securing Mission Critical PostgreSQL Data

Is your team responsible for developing scalable enterprise applications on PostgreSQL databases? Does your company’s current infrastructure lack the ability to recover rapidly from a disaster, data loss, or even a cyber attack like ransomware? Are you looking for industry-leading technologies that can enable fast recovery of mission-critical application data? Is your team measured on recovery point and time objectives for new digital transformation initiatives?
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Using DataOps for Data Pipeline Engineering Quality

tdwi.org

Data pipelines facilitate information flows and data exchange for a growing number of operational scenarios, including data extraction, transformation, and loading ETL into data warehouses and data marts, data migrations, production of BI reports, and application interoperability. When data engineers develop data pipelines, they may devise a collection of tests to guide the development process, but ongoing tests are not often put in place once those pipelines are put into production.
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Optimizing Your Data Analytics Resourcing

Join us for a focused discussion on Data Analytics with health system data analytics leaders. We discuss the decisions, benefits and challenges organizations face when determining how to structure and manage data assets, tools and teams. We’re excited to share diverse perspectives and answer audience questions.
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