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The data flywheel: A better way to think about your data strategy

CIO Business Intelligence

This article was co-authored by Duke Dyksterhouse , an Associate at Metis Strategy. Data & Analytics is delivering on its promise. Some are our clients—and more of them are asking our help with their data strategy. So, they built a data-lake. We discourage that thinking.

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Differences Between Data Lake and Data Warehouses

TDAN

Data lake is a newer IT term created for a new category of data store. But just what is a data lake? According to IBM, “a data lake is a storage repository that holds an enormous amount of raw or refined data in native format until it is accessed.” That makes sense. I think the […].

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Architecture for the Data Lake

TDAN

For a while now, vendors have been advocating that people put their data in a data lake when they put their data in the cloud. The Data Lake The idea is that you put your data into a data lake. Then, at a later point in time, the end user analyst can come along and […].

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A Retrospective of 2018’s Articles

Peter James Thomas

This increase was driven in part by the launch of my new Maths & Science section , articles from which claimed no fewer than 6 slots in the 2018 top 10 articles, when measured by hits [1]. This is my selection of the articles that I enjoyed writing most, which does not always overlap with the most popular ones. May onwards.

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Data Strategies for Getting Greater Business Value from Distributed Data

Data Virtualization

Reading Time: 11 minutes The post Data Strategies for Getting Greater Business Value from Distributed Data appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

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Building a vision for real-time artificial intelligence

CIO Business Intelligence

Most current data architectures were designed for batch processing with analytics and machine learning models running on data warehouses and data lakes. In this article, I’ll share insights on aligning vision and leadership, as well as reducing complexity to make data actionable for delivering real-time AI solutions.

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Trends in Data Management and Analytics

TDAN

Various databases, plus one or more data warehouses, have been the state-of-the art data management infrastructure in companies for years. The emergence of various new concepts, technologies, and applications such as Hadoop, Tableau, R, Power BI, or Data Lakes indicate that changes are under way.