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Advanced Analytics in Supply Chain Management

Inflexion Analytics

How COVID has impacted the supply chain The resilience of supply chains is being tested. The visualisations include inventory levels of components, spare parts at supply chain storage locations and depots. The visual analytics informs the rapid recovery actions that need to be taken.

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Supply Chain Resilience and How Visualisation Tools Can Be Beneficial

Inflexion Analytics

How supply chain resilience is used in businesses. The resilience of supply chains has been tested in recent years by some catastrophic events and by political disruption to trade flows. The visualisations include data such as inventory levels of components and spare parts at supply chain storage locations and depots.

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What are machine learning and data science in the context of business analytics?

Analysts Corner

Data scientists use a variety of techniques and tools to collect, analyze, and interpret data, and communicate their findings to stakeholders. Data science involves several steps, including data collection, data cleaning, data exploration, data modeling, and data visualization.

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What is the Future of Business Intelligence in the Coming Year?

Smart Data Collective

Also, timely insights into inventory will help to improve supply chain decisions. The strategic decision-making in the future of business intelligence will be shaped by faster reports, deeper data insights, broader areas of data collection. Unique feature: custom visualizations to fit your business needs better.

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OLTP and OLAP: Two Sides of the Same Data Coin?

Astera

These transactions typically involve inserting, updating, or deleting small amounts of data. Normalized data structure: OLTP databases have a normalized data structure. This means that they use a data model that minimizes redundancy and ensures data consistency. They have a denormalized data structure.

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The Future of AI in Data Warehousing: Trends and Predictions 

Astera

By AI taking care of low-level tasks, data engineers can focus on higher-level tasks such as designing data models and creating data visualizations. For instance, Coca-Cola uses AI-powered ETL tools to automate data integration tasks across its global supply chain to optimize procurement and sourcing processes.

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Data Science vs Data Analytics: Key Differences

Astera

Data science covers the complete data lifecycle: from collection and cleaning to analysis and visualization. Data scientists use various tools and methods, such as machine learning, predictive modeling, and deep learning, to reveal concealed patterns and make predictions based on data.