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Data architecture strategy for data quality

IBM Big Data Hub

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

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Eating the Elephant of Data Strategy

Paul Blogs on BI

If the data is not correct then its garbage in and garbage out, or as I have always said : BIG DATA + BAD DATA = BIG BAD DATA However, when it comes to a good data strategy, it’s not just about the quality of the data, it’s also about the design and functionality of the structure where it is housed.

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

This post also discusses the art of the possible with newer innovations in AWS services around streaming, machine learning (ML), data sharing, and serverless capabilities. A data hub contains data at multiple levels of granularity and is often not integrated. A data warehouse is one of the components in a data hub.

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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. The AWS modern data architecture shows a way to build a purpose-built, secure, and scalable data platform in the cloud.

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Your Effective Roadmap To Implement A Successful Business Intelligence Strategy

datapine

4) How To Create A Business Intelligence Strategy. Odds are you know your business needs business intelligence (BI). Over the past 5 years, big data and BI became more than just data science buzzwords. Employ a Chief Data Officer (CDO). Are the data sources going to remain disparate?

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The Multifaceted Value Proposition of the Cloudera Data Platform

Cloudera

I have developed this framework to help organizations not only establish the business case for investing in CDP, but also provide a mechanism to prioritize analytical investments based on specific business objectives (e.g., reduce technology costs, accelerate organic growth initiatives). Technology cost reduction / avoidance.