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Data quality : empowering businesses with analytics and AI /

"Quality data is the key for business enterprises to offer improved performance in operations, compliance, and decision making. According to McKinsey, data driven organizations provide EBITDA increases between 15 to 25% than peers. However, to be a data driven organization, data quality is very...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Southekal, Prashanth H. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, New Jersey : John Wiley & Sons, Inc., [2023]
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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100 1 |a Southekal, Prashanth H.,  |e author. 
245 1 0 |a Data quality :  |b empowering businesses with analytics and AI /  |c Prashanth H. Southekal. 
246 3 0 |a Empowering businesses with analytics and Artificial Intelligence 
264 1 |a Hoboken, New Jersey :  |b John Wiley & Sons, Inc.,  |c [2023] 
300 |a 1 online resource (xxvi, 271 pages) :  |b illustrations (chiefly color) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
505 0 |a Business data -- Data quality in business -- Causes for poor data quality -- Data lifecycle and lineage -- Profiling for data quality -- Reference architecture for data quality -- Best practices to realize data quality -- Best practices to realize data quality -- Data governance -- Protecting data -- Data ethics. 
520 |a "Quality data is the key for business enterprises to offer improved performance in operations, compliance, and decision making. According to McKinsey, data driven organizations provide EBITDA increases between 15 to 25% than peers. However, to be a data driven organization, data quality is very important. But most companies are plagued with poor data quality. A HBR study found that just 3% of the data in a business enterprise meets quality standards. According to Gartner, 27% of data in the world's top companies is flawed--so companies are looking for practical guidance to improve data quality. This book examines the four-phase DARS approach (Define-Assess-Realize-Sustain) for companies to manage high quality data in organizations. This approach provides a combination of strategy and tactical elements to deliver the greatest value from data to the business. It is a playbook that offers prescriptive recommendations based on proven best practices to realize and sustain data quality"--  |c Provided by publisher. 
588 |a Description based on online resource; title from digital title page (viewed on July 20, 2023). 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a Business  |x Data processing. 
650 0 |a Electronic data processing  |x Quality control. 
650 0 |a Data protection. 
650 0 |a Artificial intelligence. 
650 6 |a Gestion  |x Informatique. 
650 6 |a Protection de l'information (Informatique) 
650 6 |a Intelligence artificielle. 
650 7 |a artificial intelligence.  |2 aat 
650 7 |a Artificial intelligence  |2 fast 
650 7 |a Business  |x Data processing  |2 fast 
650 7 |a Data protection  |2 fast 
650 7 |a Electronic data processing  |x Quality control  |2 fast 
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