Buch, Englisch, 288 Seiten, Format (B × H): 153 mm x 216 mm, Gewicht: 533 g
ISBN: 978-981-19-9657-3
Verlag: Springer Nature Singapore
Analytics is changing the landscape of businesses across sectors globally. This has led to the stimulation of interest of scholars and practitioners worldwide in this domain. The emergence of ‘big data’, has fanned the usages of machine learning techniques and the acceptance of ‘Analytics Enabled Decision Making’.This bookprovides a holistic theoretical perspective combined with the application of such theories by drawing on the experiences of industry professionals and academicians from around the world. The book discusses several paradigms including pattern mining, clustering, classification, and data analysis to name a few. The main objective of this book is to offer insight into the process of decision-making that is accelerated and made more precise with the help of analytics.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management E-Commerce, E-Business, E-Marketing
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
- Wirtschaftswissenschaften Betriebswirtschaft Management Prozessmanagement
- Wirtschaftswissenschaften Betriebswirtschaft Management Internationales Management
- Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
Weitere Infos & Material
Chapter 1 Next generation smart manufacturing and service systems using big data analytics.- Chapter 2 Elements of decision making under uncertainty.- Chapter 3 Data, Inference, and Marketing Decisions.- Chapter 4 Competitor and competition Analysis through analytics.- Chapter 5 Influence of big data analytics on business intelligence.- Chapter 6 Improved price alignment to perceived value of product/service using data analytics.- Chapter 7 Changing the landscape of retailing using pricing analytics.- Chapter 8 Enhancing efficiency and effectiveness in supply chain management through Business Intelligence.- Chapter 9 Quantifying thoughts and feelings about a company from big data to improve brand strength.- Chapter 10 Enabling achievement of organisational/corporate goals through HR analytics.- Chapter 11 Workforce Analytics facilitates Human Resource Demand Forecasting.- Chapter 12 Ascertainment of Employee Competencies and measurement with greater precision using T&D Analytics.-Chapter 13 Analytics to Measure Employees’ Behavioural Traits and predict employee performance.- Chapter 14 Using predictive analytics alongside psychometric assessments and other measures to identify the candidates with the right performance and behavioural criteria.- Chapter 15 Designing competitive yet cost effective compensation packages using analytics to reduce attrition.- Chapter 16 Mitigating Compliance Failure Risk using Analytics (gender equal pay, overtime payments, appropriate number of each category of employees).- Chapter 17 Predictive analytics to aid employee alignment with the culture of the organisation.- Chapter 18 Analysing the impact of Employees’ Satisfaction and Frustration on their and organizations’ performance.- Chapter 19 Assessing and Controlling Political Behaviour of Groups in Organizations.- Chapter 20 Measuring Organizational/Industrial Citizenship Behaviour.- Chapter 21 Psychological Framework and Methodology for Analysing Decision Risk.- Chapter 22 Analysing Challenging Behaviours of Two Individuals with Intellectual Differences/Disability.- Chapter 23 Measuring the effectiveness of data analytics in higher education to improve student outcomes.- Chapter 24 Building an industry 4.0 analytics platform with a proven example and proof of concept.- Chapter 25 Gaining competitive advantage through business analytics.