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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

Build data validation rules directly into ingestion layers so that insufficient data is stopped at the gate and not detected after damage is done. Use lineage tooling to trace data from source to report. Understanding how data transforms and where it breaks is crucial for audibility and root-cause resolution.

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What is Data Lineage? Top 5 Benefits of Data Lineage

erwin

The CEO also makes decisions based on performance and growth statistics. An understanding of the data’s origins and history helps answer questions about the origin of data in a Key Performance Indicator (KPI) reports, including: How the report tables and columns are defined in the metadata?

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Is your data supply chain a liability?

CIO Business Intelligence

In the data supply chain, there are a variety of sources of internal and external data (from data brokers, social media/sentiment analysis, etc.) and just like a physical supply chain, reducing complexity in the data supply chain helps improve overall quality. Data monitoring and reporting.

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Adding AI to Products: A High-Level Guide for Product Managers

Sisense

This is also an important takeaway for teams seeking to implement AI successfully: Start with the key performance indicators (KPIs) you want to measure your AI app’s success with, and see where that dovetails with your expert domain knowledge. What data transformations are needed from your data scientists to prepare the data?

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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

Solution overview In this post, we walk through a call center analytics solution that provides insights into the call center’s performance in near-real time through metrics that determine agent efficiency in handling calls in the queue.

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Unlock scalability, cost-efficiency, and faster insights with large-scale data migration to Amazon Redshift

AWS Big Data

However, you might face significant challenges when planning for a large-scale data warehouse migration. Success criteria alignment by all stakeholders (producers, consumers, operators, auditors) is key for successful transition to a new Amazon Redshift modern data architecture.

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The Modern Data Stack Explained: What The Future Holds

Alation

These help data analysts visualize key insights that can help you make better data-backed decisions. ELT Data Transformation Tools: ELT data transformation tools are used to extract, load, and transform your data. Examples of data transformation tools include dbt and dataform.