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10 Examples of How Big Data in Logistics Can Transform The Supply Chain

datapine

Your Chance: Want to test a professional logistics analytics software? Use our 14-days free trial today & transform your supply chain! After examining their data, UPS found that trucks turning left were costing them a lot of money. Your Chance: Want to test a professional logistics analytics software?

Big Data 275
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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. So questions linger about whether transformed data can be trusted.

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Data Integrity, the Basis for Reliable Insights

Sisense

Data integrity looks at the whole life cycle for your data and considers the processes around how it’s generated, stored, accessed, and applied to accomplish specific business tasks. Throughout that life cycle, a good data integrity program aims to ensure that data is available, complete and accurate.

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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. What are the four types of data analytics? It is frequently used for risk analysis.

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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

Data processes that depended upon the previously defective data will likely need to be re-initiated, especially if their functioning was at risk or compromised by the defected data. This is also the point where data quality rules should be reviewed again. Your Chance: Want to test a professional analytics software?

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Turnkey Cloud DataOps: Solution from Alation and Accenture

Alation

So, how can you quickly take advantage of the DataOps opportunity while avoiding the risk and costs of DIY? They can better understand data transformations, checks, and normalization. They can better grasp the purpose and use for specific data (and improve the pipeline!). IDF provides a focused, business-driven solution.

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The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure

DataKitchen

Here are a few examples that we have seen of how this can be done: Batch ETL with Azure Data Factory and Azure Databricks: In this pattern, Azure Data Factory is used to orchestrate and schedule batch ETL processes. Azure Blob Storage serves as the data lake to store raw data. Azure Machine Learning).