Strategic Imperative – The Enterprise Data Model

With today’s increasingly complex data ecosystems, the Enterprise Data Model (EDM) is a strategic imperative that every organization should adopt. An Enterprise Data Model provides context and consistency for all organizational data assets, as well as a classification framework for data governance. Enterprise modeling is also totally consistent with agile workflows, evolving incrementally to keep pace with changing organizational factors. In this session, IDERA’s Ron Huizenga will discuss the increasing importance of the EDM, how it serves as a framework for all enterprise data assets, and provides a foundation for data governance.
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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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Expanding Biobanking Size and Scope to Advance Big Data Discovery

LabRoots

Currently, the Cleveland Clinic Biorepository is an assembly of several biobanks together with the Lerner Research Institute. Excel spreadsheets track almost everything – and there are no universal LIS, SOPs, universal, centralized freezer monitoring, or means to electronically track patient tissue. The Cleveland Clinic is embarking on an enterprise-wide initiative to accelerate its biobanking capacity, leveraging its clinical volume and disease expertise. As part of this initiative, the Cleveland Clinic is partnering with Brooks Life Sciences to build a state of the art, 21,000 square-foot structure biobanking facility, set to open in the summer of 2019.
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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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Choosing an Analytical Cloud Data Platform

As organizations move into the cloud, the choices for handling high-scale data for analytical use are flourishing and evolving. How do we address BI/analytics, data science, security/application monitoring, and log data management workloads? Do we really need potentially overlapping warehouse, data lake, and security and observability capabilities on top of object storage, or can an evolved data lake or emerging “lakehouse” platform do it all? Join Doug Henschen, VP and principal analyst at Constellation Research and Thomas Hazel, Chief Technology and Science Officer at ChaosSearch for a broad-ranging discussion on the challenges and strategy considerations that go into choosing the right platform.
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