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Data Strategies for Getting Greater Business Value from Distributed Data

Data Virtualization

Reading Time: 11 minutes The post Data Strategies for Getting Greater Business Value from Distributed Data appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

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Data architecture strategy for data quality

IBM Big Data Hub

Next generation of big data platforms and long running batch jobs operated by a central team of data engineers have often led to data lake swamps. Meaning, data architecture is a foundational element of your business strategy for higher data quality. Practice proper data hygiene across interfaces.

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Detect, mask, and redact PII data using AWS Glue before loading into Amazon OpenSearch Service

AWS Big Data

Ingestion: Data lake batch, micro-batch, and streaming Many organizations land their source data into their data lake in various ways, including batch, micro-batch, and streaming jobs. Amazon AppFlow can be used to transfer data from different SaaS applications to a data lake.

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CIO Ryan Snyder on the benefits of interpreting data as a layer cake

CIO Business Intelligence

A data and analytics capability cannot emerge from an IT or business strategy alone. With both technology and business organization deeply involved in the what, why, and how of data, companies need to create cross-functional data teams to get the most out of it. That strategy is doomed to fail. What are the layers?

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Data governance in the age of generative AI

AWS Big Data

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. As part of the transformation, the objects need to be treated to ensure data privacy (for example, PII redaction).

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

Selling the value of data transformation Iyengar and his team are 18 months into a three- to five-year journey that started by building out the data layer — corralling data sources such as ERP, CRM, and legacy databases into data warehouses for structured data and data lakes for unstructured data.

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Breaking down Business Intelligence

BizAcuity

When data is stored in silos and the back-end systems are not able to process the massive amounts of data seamlessly, critical information may be lost. We get critical business insights based on how well we leverage our business data. The more effectively a company uses data, the better it performs. Data Integration.