Data Architecture Best Practices for Advanced Analytics

Data Architecture Best Practices for Advanced Analytics
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.  

There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
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Spotlight

OTHER ON-DEMAND WEBINARS

Introducing Cloudera Data Science Workbench for HDP

Data scientists require on-demand access to data, powerful processing infrastructure, and multiple tools and libraries for development and experimentation. Meanwhile, IT has a difficult time keeping up with these changing needs while ensuring operational efficiency and compliance. Sound familiar? CDSW was designed precisely to r
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DataEd Webinar: Essential Reference and Master Data Management

Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions: its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on-time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach typically involving Data Governance and Data Quality activities.
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Overcoming the Obstacles for Data Lake Success

Business users have a tremendous appetite for data. The “single version of the truth” was a rallying cry to deliver business data in data warehouses for years. Users were able to digest and analyze large volumes of corporate data. They reviewed trends, identified anomalies, and supported decision-making because they had the detailed data to support action. As the business/data environment matured, the need for more diverse detail and increased delivery speed only grew. The data lake became a successful mechanism for delivering data from diverse systems in a timely manner.
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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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