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Data transformation takes flight at Atlanta’s Hartsfield-Jackson airport

CIO Business Intelligence

As part of its plan, the IT team conducted a wide-ranging data assessment to determine who has access to what data, and each data source’s encryption needs.

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How Your Finance Team Can Lead Your Enterprise Data Transformation

Alation

Because of the criticality of the data they deal with, we think that finance teams should lead the enterprise adoption of data and analytics solutions. Recent articles extol the benefits of supercharging analytics for finance departments 1. This is because accurate data is “table stakes” for finance teams.

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SAP Datasphere Powers Business at the Speed of Data

Rocket-Powered Data Science

Datasphere is a data discovery tool with essential functionalities: recommendations, data marketplace, and business content (i.e., incorporates the business context of the data and data products that are being recommended and delivered). As you would guess, maintaining context relies on metadata.

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Key Challenges Affecting Data Transformations—Dev and Testing

Wayne Yaddow

Common challenges and practical mitigation strategies for reliable data transformations. Photo by Mika Baumeister on Unsplash Introduction Data transformations are important processes in data engineering, enabling organizations to structure, enrich, and integrate data for analytics , reporting, and operational decision-making.

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A Planning Center of Excellence Delivers Performance Improvement

David Menninger's Analyst Perspectives

Finance people think in terms of money, but line-of-business managers almost always think in terms of things. Automating data transformation and aggregation also makes it practical to expand the scope of usable data for forecasting, planning, analysis and reporting by removing time constraints.

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Accelerate your data workflows with Amazon Redshift Data API persistent sessions

AWS Big Data

In this post, we’ll walk through an example ETL process that uses session reuse to efficiently create, populate, and query temporary staging tables across the full data transformation workflow—all within the same persistent Amazon Redshift database session.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

Competitive advantage: As mentioned in the previous points, the bottom line of being in possession of good quality data is improved performance across all areas of the organization. This means there are no unintended data errors, and it corresponds to its appropriate designation (e.g., date, month, and year).