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Business Leaders Make the Case for Data Science

Dataiku

Led by Conor Jensen of Dataiku, a panel of esteemed industry leaders discussed the case for strong data science foundations in their respective spaces.

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Accelerating Drug Discovery and Development with DataOps

DataKitchen

– Kurt Zimmer, AstraZeneca, Head of Data Engineering inside Data Enablement (CDO Summit 2021). Meta-orchestration is problematic for some because it requires an orchestration tool to connect seamlessly with data professionals’ vast ecosystem of tools.

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A Data Prediction for 2025

DataKitchen

Central IT Data Teams focus on standards, compliance, and cost reduction. ’ They are data enabling vs. value delivery. Their software purchase behavior will align with enabling standards for line-of-business data teams who use various tools that act on data. Recession: the party is over.

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What is data architecture? A framework to manage data

CIO Business Intelligence

Data architectures should integrate with legacy applications using standard API interfaces. They should also be optimized to share data across systems, geographies, and organizations. Real-time data enablement. Be decoupled and extensible.

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Variety is the Secret Sauce for Big Discoveries in Big Data

Rocket-Powered Data Science

In “data science language”, what we are describing are different segments (clusters) in the hyperspace of symptoms and causes in which the many causes (clusters) are projected on top of one another (overlap one another) in the symptom space.

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The Power of Graph Databases, Linked Data, and Graph Algorithms

Rocket-Powered Data Science

All of that “metadata” (which is simply “other data about your data”) enables rich discovery of shortest paths, central nodes, and communities. Any node and its relationship to a particular node becomes a type of contextual metadata for that particular note.

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DataOps For Business Analytics Teams

DataKitchen

For example, teams working under the VP/Directors of Data Analytics may be tasked with accessing data, building databases, integrating data, and producing reports. Data scientists derive insights from data while business analysts work closely with and tend to the data needs of business units.