Remove Business Analytics Remove Data Collection Remove Data Lake Remove Structured Data
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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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Understanding Structured and Unstructured Data

Sisense

In our modern digital world, proper use of data can play a huge role in a business’s success. Datasets are exploding at an ever-accelerating rate, so collecting and analyzing data to maximum effect is crucial. Understanding data structure is a key to unlocking its value. Structured vs unstructured data.

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In-depth with CDO Christopher Bannocks

Peter James Thomas

I have since run and driven transformation in Reference Data, Master Data , KYC [3] , Customer Data, Data Warehousing and more recently Data Lakes and Analytics , constantly building experience and capability in the Data Governance , Quality and data services domains, both inside banks, as a consultant and as a vendor.

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What is a Data Pipeline?

Jet Global

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.