Data Management

Blend, cleanse and prepare data for analytics, reporting or data modernization efforts

Analytics | Data Management
John Gottula 0
From 9 cows to the future of agtech

The COVID-19 pandemic challenged agriculture and supply chains, but the overarching resilience of agriculture around the world speaks to the industry's efficiency, built-in redundancy and indispensability. In the US, flourishing interactions between government, industry and academic stakeholders underscore how ag represents unity and consilience. And there may be no better

Analytics | Customer Intelligence | Data for Good | Data Management | Work & Life at SAS
SAS Colombia 0
7 razones para disminuir la incertidumbre por el futuro de los negocios

A propósito del Global Business Barometer apoyado por SAS y The Economist). En el mundo de los negocios hay una frase atribuida a John Maxwell que aplica muy bien al momento (a este y a cualquiera): “El pesimista se queja del viento. El optimista espera que cambie. El líder arregla

Data Management | Fraud & Security Intelligence
Nuth Ratanachu-ek 0
A practical guide to improve the effectiveness of watch list screening

Watch list screening has been one of the rules with highest false-positive rate. Watch list screening has been one of the pillars for know your customer (KYC) and anti-money laundering (AML) regulatory requirements since the beginning. It was introduced to prevent known criminals (or known high risk entities) from utilizing

Analytics | Data Management
Jeanne (Hyunjin) Byun 0
데이터 3법 시대를 맞이하여 알아보는 2020 데이터 관리 및 개인정보보호 트렌드

요즘 네이버, 구글 등 각종 검색 포털 창에 ‘데이터’를 입력하면 어떤 키워드가 가장 먼저 완성될까요? 빅데이터? 데이터 분석? 물론 두 단어의 인기는 산업을 불문하고 여전히 뜨겁지만😊 그 못 지 않게 2020년 1월, 화제의 중심에 선 키워드가 있죠. 최근 뉴스 기사들을 통해 모두가 짐작했을 주인공, 바로 ‘데이터 3법’입니다. 지난 1월 9일,

Advanced Analytics | Analytics | Data Management
Ivan Fernando Herrera 0
Operacionalización de la Analítica: ¿cómo lograr que los modelos analíticos realmente apoyen el éxito de los negocios?

El éxito en el desarrollo e implementación de las iniciativas analíticas empresariales requiere que se tengan propósitos claros, una alineación con los objetivos del negocio, una adecuada captura y calidad de datos, una gestión y mejoramiento continuo de los modelos analíticos desarrollados y no menos importante, la operacionalización o puesta

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