How to Analyze & Optimize Mobility with Geospatial Data

As 2019 begins, it’s a good time to assess what you accomplished last year and what goals to set for the new year. It’s also a great opportunity to review, evaluate, and update your company’s Data Management Strategy to promote alignment and derive maximum value for the business. IDERA’s Ron Huizenga will discuss data architecture and governance considerations that should be incorporated into your Data Management Strategy and Program.

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.

From AI and machine learning to data discovery and real-time analytics, a strong data architecture strategy is critical to supporting your organization's data-driven goals. Greater speed, flexibility, and scalability are common wish-list items, alongside smarter data governance and security capabilities. Many new technologies and approaches have come to the forefront of data architecture discussions, including data lakes, in-memory databases and engines like Spark and cloud services of all shapes and sizes.

As the demand for data has grown, data workflows and processes have become increasingly complex. Consequently, the need for an efficient, modern data supply chain has never been more pressing. Yet, 451 Research’s latest survey shows that imbalances in the data supply chain could pose risks to data initiatives in today’s DataOps-driven world.