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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

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.

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Data Preparation and Data Mapping: The Glue Between Data Management and Data Governance to Accelerate Insights and Reduce Risks

erwin

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. Creating a High-Quality Data Pipeline.

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

My vision is that I can give the keys to my businesses to manage their data and run their data on their own, as opposed to the Data & Tech team being at the center and helping them out,” says Iyengar, director of Data & Tech at Straumann Group North America.

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The Modern Data Stack Explained: What The Future Holds

Alation

It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse. Extract, load, Transform (ELT) tools. Data ingestion/integration services. Data orchestration tools. Better Data Culture.

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Enable data analytics with Talend and Amazon Redshift Serverless

AWS Big Data

Today, in order to accelerate and scale data analytics, companies are looking for an approach to minimize infrastructure management and predict computing needs for different types of workloads, including spikes and ad hoc analytics. Prerequisites To complete the integration, you need a Redshift Serverless data warehouse.

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Estes Express shifts gears on customer experience by streamlining data operations

CIO Business Intelligence

To fuel self-service analytics and provide the real-time information customers and internal stakeholders need to meet customers’ shipping requirements, the Richmond, VA-based company, which operates a fleet of more than 8,500 tractors and 34,000 trailers, has embarked on a data transformation journey to improve data integration and data management.

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Fabrics, Meshes & Stacks, oh my! Q&A with Sanjeev Mohan

Alation

The data warehouse and analytical data stores moved to the cloud and disaggregated into the data mesh. Today, the brightest minds in our industry are targeting the massive proliferation of data volumes and the accompanying but hard-to-find value locked within all that data. Architectures became fabrics.