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DataOps For Business Analytics Teams

DataKitchen

Data scientists derive insights from data while business analysts work closely with and tend to the data needs of business units. Business analysts sometimes perform data science, but usually, they integrate and visualize data and create reports and dashboards from data supplied by other groups.

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A summary of Gartner’s recent DataOps-driven data engineering best practices article

DataKitchen

His team is already on Azure and uses ADF, Synapse, PowerBi, and several powerful Azure Data Science tools. Errors have caused the company’s leaders to lose confidence in the data products their team produces. Shouldn’t Marcus consider upgrading his technology? A better ETL tool? Pick some other hot tool?

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The Failed Promises of Digital Transformation and What to Do About It

Ontotext

Graph technologies have nothing to do with charts and visualizations and everything to do with mathematical graph theory. It’s all about connections and relationships in data. Traditionally, data are stored in rows and columns and tables, like spreadsheets.

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A Data Prediction for 2025

DataKitchen

Central IT Data Teams focus on standards, compliance, and cost reduction. ’ They are data enabling vs. value delivery. Their software purchase behavior will align with enabling standards for line-of-business data teams who use various tools that act on data. Recession: the party is over.

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Move Beyond Excel, PowerPoint And Static Business Reporting with Powerful Interactive Dashboards

datapine

Visualizing the data and interacting on a single screen is no longer a luxury but a business necessity. They enable you to easily visualize your data, filter on-demand, and slice and dice your data to dig deeper. Say you want to visualize your revenue data by country.

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Accelerating Drug Discovery and Development with DataOps

DataKitchen

Each platform has its own set of database , ETL, visualization and other tools. – Kurt Zimmer, AstraZeneca, Head of Data Engineering inside Data Enablement (CDO Summit 2021). Sometimes a custom tool is best for a particular job.

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How to choose the best AI platform

IBM Big Data Hub

AI platforms offer a wide range of capabilities that can help organizations streamline operations, make data-driven decisions, deploy AI applications effectively and achieve competitive advantages. Visual modeling: Combine visual data science with open source libraries and notebook-based interfaces on a unified data and AI studio.