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Case Study Modernizing an Operational Data Architecture

Explore a real-life modernization strategy for an operational data store, overcoming conflicts and bottlenecks by creating a corporate data platform with API management and open-source technologies. Gain insights into industry versus real-life expectations and effective strategy setting.

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Case Study Modernizing an Operational Data Architecture

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  1. Case Study Modernizing an Operational Data Architecture • Jennifer Lim (Cerner) • Tuesday, 09/11/2018 • Data-driven business management • Level: Non-technical • Secondary topics: Data Platforms

  2. Abstract • Our internal operational data store once provided significant value, but has since become a bottleneck to the architecture. We’ve thrown hardware at it. We’ve optimized the software and the data model. We’ve stopped allowing access to it — and then allowed access to it again because its data is so valuable. Yet it still comes up at almost every system risk review meeting. The discussion will start by explaining the problem statement that I was given to resolve: help us address the numerous resource utilization conflicts within the Operational Data Store (ODS). • This leads us to our modernization strategy, creating the corporate data platform. We’re addressing our ability to decouple our architecture through API Management, scale with hybrid Cloud Data Architectures, and incorporate open source technologies. I will share some take-aways into differences I found between industry articles and what I found in real life, as expectation management is an often missed piece of setting an achievable strategy.

  3. Cernertoday

  4. About Me. • I lead Enterprise Architecture for Cerner, a company focused on creating intelligent solutions for the health care industry. My team creates and influences the technical strategies in the areas of corporate information management, employee experience, and IT systems’ architecture. • With over 18 years of experience in the telecommunications, banking and federal, and healthcare IT industries, I’ve held strategic leadership positions in both IT and Business domains across Data Management, Finance, Marketing, CTO-Research, Architecture, and Application Development. • I hold a BS in Management Information Systems and an MBA in Management. Jennifer Lim Enterprise Architecture Cerner Corporation https://www.linkedin.com/in/jenniferchaselim/

  5. “Help us address the numerous resource utilization conflicts within the Operational Data Store (ODS)”

  6. It’s simple, right? Problem Definition Deliver Solution You have a problem. Just go solve it.

  7. In reality, solving a problem with technology often feels like this… Problem Definition Develop Solution

  8. Here’s how we built our Modernization Strategy

  9. “Help us address the numerous resource utilization conflicts within the Operational Data Store (ODS)” We’d outgrown our architecture. How?

  10. Lessons Learned

  11. Data Marketecture Vendors tell you that getting your data into the cloud is Simple & Quick… “fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools.” “just connect your favorite BI tool & you’re ready to go” https://aws.amazon.com/redshift/ https://azure.microsoft.com/en-us/services/sql-data-warehouse/?v=17.44 “Quickly implement a high-performance, secure, and compliant cloud data warehouse. Azure SQL Data Warehouse is the SQL analytics platform that lets you scale compute and storage elastically and independently, with a massively parallel processing architecture. ”

  12. Architecture Risks: Data Lake Storage approach variation between snapshots of files and our ability to create a consistent “current view” file for the inputs As more systems enter the lake, the file structure was getting confusing. Plus, some of the compute technologies work better with specific folder structures at eh storage – we’ll need to move & rename some of what we’ve done. Compute methods are still exploratory. We created a framework to better direct the solutions. We discovered ways to determine who is using what for multi-tenancy. Training sessions to learn to spin up compute only when required . Real Life…Batch In reality, it’s not that simple. Here’s a thin slice of cloud functionality that was built for an application. Risk: Our targets are still on-prem Risk: Lots of Data, in both files and folders. With many contributors, beyond this application. Value: • Supports Big Data Variety & Velocity needs • Many options for compute, not limited into making everything fit into one method or technology Risk: Compute needed to move data so SQL based tools can use it. Risk: Publishing. Risk: Had 1 Storage Outage (rare). Turns out it was not tied into standard IT alerts. Standard on-prem infrastructure processes need to adjust to capture cloud. Blue = application

  13. Services Marketecture

  14. Real Life…

  15. Jennifer Lim Enterprise Architecture Cerner Corporation https://www.linkedin.com/in/jenniferchaselim/

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