Products related to Retributive:
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Data Analytics in Marketing, Entrepreneurship, and Innovation
Innovation based in data analytics is a contemporary approach to developing empirically supported advances that encourage entrepreneurial activity inspired by novel marketing inferences.Data Analytics in Marketing, Entrepreneurship, and Innovation covers techniques, processes, models, tools, and practices for creating business opportunities through data analytics.It features case studies that provide realistic examples of applications.This multifaceted examination of data analytics looks at: Business analyticsApplying predictive analytics Using discrete choice analysis for decision-making Marketing and customer analyticsDeveloping new productsTechnopreneurshipDisruptive versus incremental innovationThe book gives researchers and practitioners insight into how data analytics is used in the areas of innovation, entrepreneurship, and marketing.Innovation analytics helps identify opportunities to develop new products and services, and improve existing methods of product manufacturing and service delivery.Entrepreneurial analytics facilitates the transformation of innovative ideas into strategy and helps entrepreneurs make critical decisions based on data-driven techniques.Marketing analytics is used in collecting, managing, assessing, and analyzing marketing data to predict trends, investigate customer preferences, and launch campaigns.
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Data and Analytics Strategy for Business : Unlock Data Assets and Increase Innovation with a Results-Driven Data Strategy
For many organizations data is a by-product, but for the smarter ones it is the heartbeat of their business.Most businesses have a wealth of data buried in their systems which, if used effectively, could increase revenue, reduce costs and risk and improve customer satisfaction and employee experience.Beginning with how to choose projects which reflect your organization's goals and how to make the business case for investing in data, this book then takes the reader through the five 'waves' of organizational data maturity.It takes the reader from getting started on the data journey with some quick wins, to how data can help your business become a leading innovator which systematically outperforms competitors. Data and Analytics Strategy for Business outlines how to build consistent, high-quality sources of data which will create business value and explores how automation, AI and machine learning can improve performance and decision making.Filled with real-world examples and case studies, this book is a stage-by-stage guide to designing and implementing a results-driven data strategy.
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Social Data Analytics
This book is an introduction to social data analytics along with its challenges and opportunities in the age of Big Data and Artificial Intelligence.It focuses primarily on concepts, techniques and methods for organizing, curating, processing, analyzing, and visualizing big social data: from text to image and video analytics.It provides novel techniques in storytelling with social data to facilitate the knowledge and fact discovery.The book covers a large body of knowledge that will help practitioners and researchers in understanding the underlying concepts, problems, methods, tools and techniques involved in modern social data analytics.It also provides real-world applications of social data analytics, including: Sales and Marketing, Influence Maximization, Situational Awareness, customer success and Segmentation, and performance analysis of the industry.It provides a deep knowledge in social data analytics by comprehensively classifying the current state of research, by describing in-depth techniques and methods, and by highlighting future research directions.Lecturers will find a wealth of material to choose from for a variety of courses, ranging from undergraduate courses in data science to graduate courses in data analytics.
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Big Data Analytics
Big Data Analytics is intended for use as a textbook for third- and fourth-year students of B.E., B.Tech., B.Sc., BCA, MCA, and M.Tech. courses in IT, Software, and Computer Science Engineering.The book has been written to help students who enter the software industry to gain a broad understanding of Big Data and the nuances of handling it to extract useful information.Spread across 21 chapters, it elucidates the concept of Big Data and walks the reader through popular frameworks such as Hadoop, MongoDB, Pig and Hive that are used for processing Big Data.
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What is fairer: distributive or retributive justice?
Both distributive and retributive justice are important aspects of a fair society. Distributive justice focuses on the fair distribution of resources and opportunities among individuals, ensuring that everyone has access to what they need. On the other hand, retributive justice is concerned with holding individuals accountable for their actions and ensuring that they face consequences for any harm they have caused. Ultimately, both forms of justice are necessary for a fair society, as they address different aspects of fairness and equality.
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What does the term 'retributive justice' mean?
Retributive justice is a theory of justice that focuses on punishment for wrongdoing. It is based on the idea that individuals who commit crimes deserve to be punished proportionally to the harm they have caused. This form of justice is often associated with the concept of "an eye for an eye" and seeks to restore balance by meting out punishment to offenders. Retributive justice is different from other forms of justice, such as restorative justice, which focuses on repairing harm and restoring relationships.
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Which data storage medium is suitable for long-term data storage?
For long-term data storage, optical discs such as Blu-ray discs and archival-grade DVDs are suitable options. These discs have a longer lifespan compared to traditional CDs and DVDs, and they are less susceptible to degradation from environmental factors. Additionally, solid-state drives (SSDs) with high-quality NAND flash memory can also be used for long-term data storage due to their durability and reliability. It is important to regularly check and migrate data to newer storage mediums to ensure long-term preservation.
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Why is the vortex tube technology used so little?
Vortex tube technology is used relatively little because it has limitations in terms of efficiency and practicality compared to other cooling methods. While it can provide cooling without any moving parts or electricity, it is not as efficient as traditional refrigeration systems. Additionally, the cooling capacity of vortex tubes is limited, making them more suitable for small-scale applications. Finally, the complexity and cost of vortex tube technology may also deter its widespread use in industrial or commercial settings.
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Business Models : Innovation, Digital Transformation, and Analytics
Since the beginning of time, running a business has involved using logic by which the business operates.This logic is called the business model in management science, which increasingly is focusing on issues surrounding business models.Research trends related to business models include value creation, value chain operationalization, and social and ecological aspects, as well as innovation and digital transformation.Business Models: Innovation, Digital Transformation, and Analytics examines how innovation, digital transformation, and the composition of value affect the existence and development of business models.The book starts by addressing the conceptual development of business models and by discussing the essence of innovation in those models.Chapters in the book investigate how: Business models can analyze digital transformation scenarios Individual business model elements effect selected performance measures as well as how the elements are significant for the enterprise value composition The environment effects the profitability of the high-growth enterprise business models Employer branding business models are perceived by the generation Z workforce To implement responsible business models in the enterprise Cyber risk is captured in business models Decision algorithms are important to business analyticsThis book is a compendium of knowledge about the use of business models in the context of innovative activities, digital transformation, and value composition.It attempts to combine the theory and practice and offers a look at business models currently used in companies, especially high-growth enterprises, in various countries of the world and indicates the prospects for their development.
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Data Analytics Initiatives : Managing Analytics for Success
The categorisation of analytical projects could help to simplify complexity reasonably and, at the same time, clarify the critical aspects of analytical initiatives.But how can this complex work be categorized? What makes it so complex?Data Analytics Initiatives: Managing Analytics for Success emphasizes that each analytics project is different.At the same time, analytics projects have many common aspects, and these features make them unique compared to other projects.Describing these commonalities helps to develop a conceptual understanding of analytical work.However, features specific to each initiative affects the entire analytics project lifecycle.Neglecting them by trying to use general approaches without tailoring them to each project can lead to failure. In addition to examining typical characteristics of the analytics project and how to categorise them, the book looks at specific types of projects, provides a high-level assessment of their characteristics from a risk perspective, and comments on the most common problems or challenges.The book also presents examples of questions that could be asked of relevant people to analyse an analytics project.These questions help to position properly the project and to find commonalities and general project challenges.
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Innovation in Information Technology
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Technology and Innovation Management
Technology and Innovation Management is one of the most sought-after courses offered like MBA or PGDM in Business Schools and various Technology Institutes, today.This book, written with deep ingrained practical insights and well-researched theoretical foundations integrates people, processes and technology to achieve maximum economic benefits to society.The book is designed to be a compendium for students and managers, who wish to understand technology and innovation management to the core.The book explains the relationship between technology innovation and strategy in a simplified manner.Keeping Indian education framework in mind, this book details on practices and principles that are easy to implement.The theories are simple to grasp, and anecdotal stories on Technology and Innovation implementations make it a student-friendly edition, to help achieve success in exams as well as in the professional front.It further explains the core principles of Technology and Innovation Management.S-Curve and the Segment Zero Principle, adopting industry 4.0 and innovation 4.0 to make India a smart and intelligent manufacturing hub in the era of fourth industrial revolution, design thinking for solving complex business problems along with the role and contribution of Government in Technology Development.
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Are Instagram analytics apps legal?
Yes, Instagram analytics apps are legal as long as they comply with Instagram's terms of service and data privacy regulations. These apps provide users with insights and data about their Instagram account performance, such as follower growth, engagement metrics, and audience demographics. It is important to choose a reputable analytics app that prioritizes user privacy and data security to ensure compliance with legal requirements.
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How does a data storage work?
Data storage works by storing digital information in a physical or virtual medium, such as a hard drive, solid-state drive, or cloud storage. When data is stored, it is organized and indexed so that it can be easily accessed and retrieved when needed. The data is typically stored in binary code, represented by 0s and 1s, and can be written, read, and modified as necessary. Different storage mediums have different capacities and speeds, allowing for various types of data to be stored and accessed efficiently.
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What are two data storage devices?
Two data storage devices are hard disk drives (HDD) and solid-state drives (SSD). HDDs use spinning disks to store data and are commonly found in desktop computers and laptops. SSDs, on the other hand, use flash memory to store data and are known for their faster read and write speeds, making them popular in high-performance computers and servers. Both devices are used to store and retrieve digital information, but they differ in their technology and performance.
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What are local data storage devices?
Local data storage devices are physical hardware devices that are used to store and retrieve digital data on a local level. These devices include hard disk drives (HDD), solid-state drives (SSD), USB flash drives, memory cards, and optical discs such as CDs and DVDs. Local data storage devices are used to store files, documents, photos, videos, and other digital content, and they provide a convenient and accessible way to store and access data without relying on an internet connection. These devices are commonly used in personal computers, laptops, and other electronic devices for storing and accessing data locally.
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