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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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Five Steps for Building a Successful BI Strategy

Sisense

And every business – regardless of the industry, product, or service – should have a data analytics tool driving their business. Every business needs a business intelligence strategy to take it forward. . The BI strategy played a major role in the setup, execution, and ongoing implementation of the BI platform.

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ChatGPT: le nuove sfide della strategia sui dati nell’era dell’IA generativa

CIO Business Intelligence

Le aziende italiane investono in infrastrutture, software e servizi per la gestione e l’analisi dei dati (+18% nel 2023, pari a 2,85 miliardi di euro, secondo l’Osservatorio Big Data & Business Analytics della School of Management del Politecnico di Milano), ma quante sono giunte alla data maturity?

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Top 4 Business Analytics Techniques Companies Need to Adopt

TreehouseTechGroup

There are four important techniques in business analytics that correspond to the different stages of maturity in the analytics lifecycle. Most organizations start their analytics journey by asking ‘what has happened’. Let’s take a look at them below: 1.

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Incorporating Artificial Intelligence for Businesses : The Modern Approach to Data Analytics

BizAcuity

Combined, it has come to a point where data analytics is your safety net first, and business driver second. By 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance. AI Adoption and Data Strategy. Source: PwC).

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Three Types of Actionable Business Analytics Not Called Predictive or Prescriptive

Rocket-Powered Data Science

Decades (at least) of business analytics writings have focused on the power, perspicacity, value, and validity in deploying predictive and prescriptive analytics for business forecasting and optimization, respectively. How do predictive and prescriptive analytics fit into this statistical framework?

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The Data Behind Tokyo 2020: The Evolution of the Olympic Games

Sisense

All of this data is integral to learning and knowledge management.“While While data and analytics are nothing new to the Olympics — they’ve been used in some form or another for many, many years — what is new is the importance of using data to manage the evolving changing models for delivery of the Games,” Chris says. >>>Infused