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The future of data: A 5-pillar approach to modern data management

CIO

This article proposes a methodology for organizations to implement a modern data management function that can be tailored to meet their unique needs. By modern, I refer to an engineering-driven methodology that fully capitalizes on automation and software engineering best practices.

Data 167
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What is data analytics? Analyzing and managing data for decisions

CIO

More specifically: Descriptive analytics uses historical and current data from multiple sources to describe the present state, or a specified historical state, by identifying trends and patterns. In business analytics, this is the purview of business intelligence (BI). Data analytics vs. business analytics.

Analytics 203
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No-code business intelligence service y42 raises $2.9M seed round

TechCrunch

At the core of the service is a lot of open source and the company, for example, contributes to GitLabs’ Meltano platform for building data pipelines. “We’re taking the best of breed open-source software. And that in turn led him to also found a second company that focused on B2B data analytics.

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Achieving Business Analytics Success

Datavail

Today’s thriving companies are embracing emerging data analytics programs to upgrade their business modeling technology from systems maintenance to value creation. The data indicate high success for enterprises that use data to develop their corporate strategies and then implement them into winning business operations.

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What Do CIOs Have To Know About Business Intelligence?

The Accidental Successful CIO

CIOs need to understand how to make use of new business intelligence tools Image Credit: deepak pal. Modern CIOs need to understand that Business intelligence (BI) leverages software and services to transform data into actionable insights that inform an company’s strategic and tactical business decisions.

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Forget the Rules, Listen to the Data

Hu's Place - HitachiVantara

Rule-based fraud detection software is being replaced or augmented by machine-learning algorithms that do a better job of recognizing fraud patterns that can be correlated across several data sources. DataOps is required to engineer and prepare the data so that the machine learning algorithms can be efficient and effective.

Data 90
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New live online training courses

O'Reilly Media - Ideas

Better Business Writing , July 15. Spotlight on Data: Data Storytelling with Mico Yuk , July 15. Product Management for Enterprise Software , July 18. The Power of Lean in Software Projects , July 25. Understanding Business Strategy , August 14. Data science and data tools.

Course 93