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8 data strategy mistakes to avoid

CIO Business Intelligence

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

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Webinar Summary: Driving Data Analytic Team Excellence Through Agility, Efficiency, and Aphorisms

DataKitchen

He drew from his twenty-five years of experience in business analytics, pharmaceutical brand launch strategy, and project management. He also highlighted the importance of agility and adaptability in data analytics. It is essential to recognize the evolution of the field and the changing expectations of data consumers.

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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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What is a data scientist? A key data analytics role and a lucrative career

CIO Business Intelligence

Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. Data scientist job description. Semi-structured data falls between the two.

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Why optimize your warehouse with a data lakehouse strategy

IBM Big Data Hub

To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures. Now, let’s chat about why data warehouse optimization is a key value of a data lakehouse strategy. To effectively use raw data, it often needs to be curated within a data warehouse.

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Data Mining vs Data Warehousing: 8 Critical Differences

Analytics Vidhya

The two pillars of data analytics include data mining and warehousing. They are essential for data collection, management, storage, and analysis. Providing insights into the trends, prediction, and appropriate strategy for the company and serving numerous other uses are distinct.

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Data governance in the age of generative AI

AWS Big Data

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Let’s look at some of the key changes in the data pipelines namely, data cataloging, data quality, and vector embedding security in more detail.