Remove Data Architecture Remove Data Collection Remove Data Science Remove Metadata
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What is data governance? Best practices for managing data assets

CIO Business Intelligence

The program must introduce and support standardization of enterprise data. Programs must support proactive and reactive change management activities for reference data values and the structure/use of master data and metadata.

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AI at Scale isn’t Magic, it’s Data – Hybrid Data

Cloudera

This leads to the obvious question – how do you do data at scale ? Al needs machine learning (ML), ML needs data science. Data science needs analytics. And they all need lots of data. The challenge for AI is how to do data in all its complexity – volume, variety, velocity.

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Breaking State and Local Data Silos with Modern Data Architectures

Cloudera

Modern data architectures. To eliminate or integrate these silos, the public sector needs to adopt robust data management solutions that support modern data architectures (MDAs). Towards Data Science ). Solutions that support MDAs are purpose-built for data collection, processing, and sharing.

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Why We Started the Data Intelligence Project

Alation

In 2013 I joined American Family Insurance as a metadata analyst. I was changing careers and had just completed a degree in Library and Information Science. I had always been fascinated by how people find, organize, and access information, so a metadata management role after school was a natural choice.

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. We recommend building your data strategy around five pillars of C360, as shown in the following figure.

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The Gartner 2022 Leadership Vision for Data and Analytics Leaders Questions and Answers

Andrew White

This research does not tell you where to do the work; it is meant to provide the questions to ask in order to work out where to target the work, spanning reporting/analytics (classic), advanced analytics and data science (lab), data management and infrastructure, and D&A governance. We write about data and analytics.