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How DataOps is Transforming Commercial Pharma Analytics

DataKitchen

Marketing invests heavily in multi-level campaigns, primarily driven by data analytics. This analytics function is so crucial to product success that the data team often reports directly into sales and marketing. The Otezla team built a system with tens of thousands of automated tests checking data and analytics quality.

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10 Examples of How Big Data in Logistics Can Transform The Supply Chain

datapine

Table of Contents 1) Benefits Of Big Data In Logistics 2) 10 Big Data In Logistics Use Cases Big data is revolutionizing many fields of business, and logistics analytics is no exception. The complex and ever-evolving nature of logistics makes it an essential use case for big data applications.

Big Data 275
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A summary of Gartner’s recent DataOps-driven data engineering best practices article

DataKitchen

Productivity does not come through the fingers of each data engineer; it comes from building a system around those engineers that allows them to run production with minimal errors and move things into production quickly, with low risk, so they can focus on making their customers successful. What if you took another perspective?

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5 Ways Layered Navigation Improves Business Intelligence Strategies

Smart Data Collective

It is one of the biggest reasons that the market for big data is projected to be worth $273 billion by 2026. Companies are finding more creative ways to employ data analytics to improve their business intelligence strategies. One of them is by using layered navigation.

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ISO 20022: Are your payment systems ready?

IBM Big Data Hub

Increased automation: ISO 20022 provides a more structured way of exchanging payment data, enabling greater automation and reducing the need for manual intervention, all of which help reduce errors and improve overall payment processing efficiency. appeared first on IBM Blog.

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DataOps For Business Analytics Teams

DataKitchen

A DataOps process hub offers a way for business analytics teams to cope with fast-paced requirements without expanding staff or sacrificing quality. Analytics Hub and Spoke. The data analytics function in large enterprises is generally distributed across departments and roles.

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Data Teams and Their Types of Data Journeys

DataKitchen

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.