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Das zahlt sich aus: Mitarbeiterbindung jenseits des Gehaltszettels
by Marcia Gerwers Pia ZietzDas Buch von Pia Zietz und Marcia Gerwers gibt praktische Handlungsempfehlungen zur langfristigen Mitarbeiterbindung in Unternehmen. Sie erfahren, wie sich effektive Maßnahmen für mehr Zufriedenheit auch mit schmalem Geldbeutel umsetzen lassen. Anschaulich formulierte Ausflüge in die Kommunikationstheorie untermauern die Ausführungen. Das Buch liefert Personalverantwortlichen kleiner und mittelständischer Unternehmen konkrete Tipps und Tools zur Steigerung der Mitarbeiterzufriedenheit und des Feelgood Managements. Sie erhalten konkrete Praxisempfehlungen unter optimalem Budgeteinsatz. Es wird auch darauf eingegangen, wie sich die vielfältigen Aufgaben eines Feelgood Managers oder eines Kommunikationsmanagers intern und extern aufteilen lassen. Zum Schluss gibt der "5 Punkte Superplan" noch einmal konkrete Handlungsempfehlungen. Er befähigt, die Mitarbeiterbindung eigenständig zu stärken und auszubauen.
Das Zukunfts-Mindset
by Jörg Hawlitzeck Hermann SchererWelche Fähigkeiten brauchen wir, um mit dem durch Digitalisierung und technischen Fortschritt immer schnelleren Entwicklungstempo Schritt zu halten? Welche Eigenschaften und Verhaltensweisen garantieren, dass wir nicht überrollt oder gar überflüssig werden? Wenn Sie diese Fragen beschäftigen, so hat der erfolgreiche Leadership-Experte, Coach und Keynote-Speaker Jörg Hawlitzeck eine gute Nachricht für Sie: Alles, was Sie für Ihre Zukunft brauchen, liegt bereits in Ihnen. Mit dem richtigen Mindset gehen Sie neuen Entwicklungen mutig entgegen, anstatt ihnen hinterherzulaufen. Sie erfahren in diesem Buch, wie jeder Einzelne und unsere Unternehmen aufgestellt sein müssen, um für eine ebenso spannende wie unsichere Zukunft gerüstet zu sein. Anhand vieler praktischer Beispiele, Erfahrungen aus erster Hand und Interviews mit Entscheidungsträgern vielversprechender Unternehmen zeigt der Autor, worum es im Kern geht: Um eine zupackende, optimistische und offene Haltung, die in Tatkraft und Veränderungsbereitschaft mündet – kurz: um das richtige Mindset.„Die Entscheidung, sich vom Zukunfts-Mindset inspirieren zu lassen ist eine Chance, die Sie sich keinesfalls entgehen lassen sollten.“ Hermann Scherer
Das zukunftsfähige Familienunternehmen: Mit dem QScore zu Unabhängigkeit, Resilienz und Robustheit (essentials)
by Werner Gleißner Arnold WeissmanDie Zukunftsfähigkeit eines Unternehmens, also das langfristige Überleben bei adäquatem Erfolg, erfordert finanzielle Nachhaltigkeit (finanzielle Stärke), eine robuste Strategie und resiliente Leistungserstellung sowie Fähigkeiten im Umgang mit Chancen und Gefahren (Unsicherheit).Das im Buch erläuterte QScore-Konzept zeigt, ausgehend von einer Vielzahl wissenschaftlicher Studien, wie man die Zukunftsfähigkeit eines Unternehmens konkret beurteilen und Verbesserungspotenziale ableiten kann.Das Essential erläutert, wie Sie in einem einfachen „Schnelltest“ ausgehend von 20 Fragen die Zukunftsfähigkeit eines Unternehmens abschätzen können.Dies ist ein Open-Access-Buch.
Das zukunftsfähige Unternehmen: Wettbewerbsvorteile durch Wertschöpfungsvernetzung (essentials)
by Friedrich GlaunerDas essentials zeigt den Weg auf, wie Unternehmen sich mit zukunftsfähigen Geschäftsmodellen und einer werteorientierten Unternehmenskultur erfolgreich im Markt behaupten können. Der Ausgangspunkt hierfür ist die Entwicklung eines Nutzenversprechens, das langfristig trägt. Hierbei spielen die gelebten Unternehmenswerte die zentrale Rolle. Ihre Schöpfung ist der zentrale Wertschöpfungsprozess zur Absicherung der Unternehmung. In einer Schritt für Schritt Anleitung wird gezeigt, wie dieser Werteschöpfungsprozess so umgesetzt werden kann, dass das Unternehmen auch auf lange Sicht gesehen wettbewerbsfähig bleibt.
Dashman Co.
by Richard S. Meriam George F.F. Lombard Franklin E. FoltsThe vice president in charge of purchasing sends a letter to each of the company's 20 purchasing executives requesting that contracts made in excess of $10,000 be cleared with him prior to signing. The branches promise to cooperate, but no notices of negotiations are received by the head office.
Dasra: From Strategic Philanthropy to Field Building
by Tanya Bijlani V. Kasturi RanganDasra, a pioneer in the Indian Strategic Philanthropy space founded by a husband and wife team, had grown and evolved with the fast changing philanthropy scene in India. By 2017 it had managed to raise nearly $100 million of new capital for NGOs and Nonprofits in India. At the same time, the rapid growth demanded internal changes that stretched the organization and raised questions regarding structures, systems and capabilities.
Data: The Prerequisite for Everything Analytical--How to Manage Your Data for More Effective General Management
by Thomas H. Davenport Jeanne G. Harris Robert MorisonFor too long, managers have relied on their intuition or their "golden gut" to make decisions. Important decisions have been based not on data, but on the experience and unaided judgment of the decision maker. Sometimes intuitive and experience-based decisions work out well, but all too often they go astray, end in disaster, or result in money being left on the table. If you think that your organization ought to make more decisions based on facts (and less on gut-level instinct), or if you want to unleash the potential buried in your company's underleveraged data, analytics are the answer. In this chapter, the authors of the groundbreaking Competing on Analytics provide an overview of what it means to use analytics in your business - the types of questions analytics can answer, when analytics might fall short, how analytics combine art and science, and even how poor analytical decisions contributed to the 2007-2009 financial crisis. They argue convincingly that even if your company has no plans to restructure itself around becoming an analytical competitor, now is the time to put analytics to work for better decision making at all levels of the organization. This chapter was originally published as Chapter 2 of Analytics at Work: Smarter Decisions, Better Results.
Data: New Trajectories in Law (New Trajectories in Law)
by Robert HerianThis book explores the phenomenon of data – big and small – in the contemporary digital, informatic and legal-bureaucratic context. Challenging the way in which legal interest in data has focused on rights and privacy concerns, this book examines the contestable, multivocal and multifaceted figure of the contemporary data subject. The book analyses "data" and "personal data" as contemporary phenomena, addressing the data realms, such as stores, institutions, systems and networks, out of which they emerge. It interrogates the role of law, regulation and governance in structuring both formal and informal definitions of the data subject, and disciplining data subjects through compliance with normative standards of conduct. Focusing on the ‘personal’ in and of data, the book pursues a re-evaluation of the nature, role and place of the data subject qua legal subject in on and offline societies: one that does not begin and end with the inviolability of individual rights but returns to more fundamental legal principles suited to considerations of personhood, such as stewardship, trust, property and contract. The book’s concern with the production, use, abuse and alienation of personal data within the context of contemporary communicative capitalism will appeal to scholars and students of law, science and technology studies, and sociology; as well as those with broader political interests in this area.
Data Analysis and Applications 3: Computational, Classification, Financial, Statistical and Stochastic Methods
by Andreas Makrides Alex Karagrigoriou Christos H. SkiadasData analysis as an area of importance has grown exponentially, especially during the past couple of decades. This can be attributed to a rapidly growing computer industry and the wide applicability of computational techniques, in conjunction with new advances of analytic tools. This being the case, the need for literature that addresses this is self-evident. New publications are appearing, covering the need for information from all fields of science and engineering, thanks to the universal relevance of data analysis and statistics packages. This book is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians who have been working at the forefront of data analysis. The chapters included in this volume represent a cross-section of current concerns and research interests in these scientific areas. The material is divided into two parts: Computational Data Analysis, and Classification Data Analysis, with methods for both - providing the reader with both theoretical and applied information on data analysis methods, models and techniques and appropriate applications.
Data Analysis and Classification: Methods and Applications (Studies in Classification, Data Analysis, and Knowledge Organization)
by Krzysztof Jajuga Krzysztof Najman Marek WalesiakThis volume gathers peer-reviewed contributions that address a wide range of recent developments in the methodology and applications of data analysis and classification tools in micro and macroeconomic problems. The papers were originally presented at the 29th Conference of the Section on Classification and Data Analysis of the Polish Statistical Association, SKAD 2020, held in Sopot, Poland, September 7–9, 2020. Providing a balance between methodological contributions and empirical papers, the book is divided into five parts focusing on methodology, finance, economics, social issues and applications dealing with COVID-19 data. It is aimed at a wide audience, including researchers at universities and research institutions, graduate and doctoral students, practitioners, data scientists and employees in public statistical institutions.
Data Analysis and Classification: Proceedings of the 6th Conference of the Classification and Data Analysis Group of the Società Italiana di Statistica (Studies in Classification, Data Analysis, and Knowledge Organization)
by Carlo Natale Lauro Michael Greenacre Francesco PalumboThe volume provides results from the latest methodological developments in data analysis and classification and highlights new emerging subjects within the field. It contains articles about statistical models, classification, cluster analysis, multidimensional scaling, multivariate analysis, latent variables, knowledge extraction from temporal data, financial and economic applications, and missing values. Papers cover both theoretical and empirical aspects.
Data Analysis and Optimization: In Honor of Boris Mirkin's 80th Birthday (Springer Optimization and Its Applications #202)
by Boris Goldengorin Sergei KuznetsovThis book presents the state-of-the-art in the emerging field of data science and includes models for layered security with applications in the protection of sites—such as large gathering places—through high-stake decision-making tasks. Such tasks include cancer diagnostics, self-driving cars, and others where wrong decisions can possibly have catastrophic consequences. Additionally, this book provides readers with automated methods to analyze patterns and models for various types of data, with applications ranging from scientific discovery to business intelligence and analytics. The book primarily includes exploratory data analysis, pattern mining, clustering, and classification supported by real life case studies. The statistical section of this book explores the impact of data mining and modeling on the predictability assessment of time series. Further new notions of mean values based on ideas of multi-criteria optimization are compared with their conventional definitions, leading to new algorithmic approaches to the calculation of the suggested new means. The style of the written chapters and the provision of a broad yet in-depth overview of data mining, integrating novel concepts from machine learning and statistics, make the book accessible to upper level undergraduate and graduate students in data mining courses. Students and professionals specializing in computer and management science, data mining for high-dimensional data, complex graphs and networks will benefit from the cutting-edge ideas and practically motivated case studies in this book.
Data Analysis in Management with SPSS Software
by J. P. VermaThis book provides readers with a greater understanding of a variety of statistical techniques along with the procedure to use the most popular statistical software package SPSS. It strengthens the intuitive understanding of the material, thereby increasing the ability to successfully analyze data in the future. The book provides more control in the analysis of data so that readers can apply the techniques to a broader spectrum of research problems. This book focuses on providing readers with the knowledge and skills needed to carry out research in management, humanities, social and behavioural sciences by using SPSS.
Data Analysis Using SPSS
by Lokesh JasraiA concise introduction to data analysis for beginners and intermediate students using IBM - Statistical Package for Social Sciences (SPSS) The present book elaborates on the basic understanding and application of statistical tests and data analysis using hypothetical datasets and SPSS version 22.0. It enhances self-learning and develops thorough understanding of the concepts through step-by-step processes for quick comprehension, and screen images, dialog boxes and exhibits for better interaction with the software. Spanning across 17 chapters, Data Analysis Using SPSS begins from the stages of data entry and goes on till editing and data visualization. It takes the readers through descriptive statistics, frequency, univariate, bivariate and regression analysis, cross-tabulation, linear models, and non-parametric test procedures. This textbook will act as a helpful companion to students of management, humanities and social sciences, agriculture and life sciences, as well as young research scholars. Key Features: • Main and sub-dialog boxes of SPSS containing commands of specific test techniques incorporated in the text for effective interaction with the software • Exercises and practice questions to enhance analytical understanding • Addition chapters on Means Analysis, One-way ANOVA, and Probability and Sampling Distribution provided as web supplement for advance reading
Data Analysis with Python: A Modern Approach
by David TaiebLearn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis. Key Features Bridge your data analysis with the power of programming, complex algorithms, and AI Use Python and its extensive libraries to power your way to new levels of data insight Work with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time series Explore this modern approach across with key industry case studies and hands-on projects Book Description Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you're likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence. What you will learn A new toolset that has been carefully crafted to meet for your data analysis challenges Full and detailed case studies of the toolset across several of today's key industry contexts Become super productive with a new toolset across Python and Jupyter Notebook Look into the future of data science and which directions to develop your skills next Who this book is for This book is for developers wanting to bridge the gap between them and data scientists. Introducing PixieDust from its creator, the book is a great desk companion for the accomplished Data Scientist. Some fluency in data interpretation and visualization is assumed. It will be helpful to have some knowledge of Python, using Python libraries, and some proficiency in web development.
Data Analysis with STATA
by Prasad KothariThis book is for all the professionals and students who want to learn STATA programming and apply predictive modelling concepts. This book is also very helpful for experienced STATA programmers as it provides advanced statistical modelling concepts and their application.
Data Analytics: Proceedings Of 4th Conference On Sustainable Urban Mobility (csum2018), 24 - 25 May, Skiathos Island, Greece (Advances In Intelligent Systems and Computing #879)
by Eftihia G. Nathanail Ioannis D. KarakikesThis book aims at showing how big data sources and data analytics can play an important role in sustainable mobility. It is especially intended to provide academicians, researchers, practitioners and decision makers with a snapshot of methods that can be effectively used to improve urban mobility. The different chapters, which report on contributions presented at the 4th Conference on Sustainable Urban Mobility, held on May 24-25, 2018, in Skiathos Island, Greece, cover different thematic areas, such as social networks and traveler behavior, applications of big data technologies in transportation and analytics, transport infrastructure and traffic management, transportation modeling, vehicle emissions and environmental impacts, public transport and demand responsive systems, intermodal interchanges, smart city logistics systems, data security and associated legal aspects. They show in particular how to apply big data in improving urban mobility, discuss important challenges in developing and implementing analytics methods and provide the reader with an up-to-date review of the most representative research on data management techniques for enabling sustainable urban mobility
Data Analytics and Business Intelligence: Computational Frameworks, Practices, and Applications
by Mohini AgarwalBusiness Analytics (BA) is an evolving phenomenon that showcases the increasing importance of using huge volumes of data to generate value for businesses. Advances in BA have offered great opportunities for organisations to improve, innovate, and develop existing or new processes, products, and services. BA is the process of transforming data into actionable insight by using statistical and mathematical analysis, descriptive, prescriptive, and predictive models, machine learning, information systems and network science methods, among others, along with a variety of data, expert knowledge, and fact-based management to support better and faster decision-making. BA and Business Intelligence (BI) generate capabilities for companies to compete in the market effectively and has become one of the main functional areas in most companies. BA tools are used in diverse ways, for example, to identify consumer behaviour patterns and market trends, to derive valuable insights on the performance of stocks, to find information on the attrition rate of employees, to analyse and solve healthcare problems, to offer insight into inventory management and supply chain management, to analyse data from social networks, and to infer traffic behaviour and develop traffic management policy, among others. BA and BI have become one of the most popular research areas in academic circles, as well as in the industry, driven by the increasing demand in the business world. This book aims to become a stimulus for innovative business solutions covering a wide range of aspects of business analytics, such as management science, information technology, descriptive, prescriptive, and predictive models, machine learning, network science, mathematical and statistical techniques. The book will encompass a valuable collection of chapters exploring and discussing computational frameworks, practices, and applications of BA that can assist industries and relevant stakeholders in decision-making and problem-solving exercises, with a view to driving competitive advantage.
Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications (Data Analytics)
by Iván Palomares Carrascosa Harsha Kumara Kalutarage Yan HuangThe book illustrates the inter-relationship between several data management, analytics and decision support techniques and methods commonly adopted in Cybersecurity-oriented frameworks. The recent advent of Big Data paradigms and the use of data science methods, has resulted in a higher demand for effective data-driven models that support decision-making at a strategic level. This motivates the need for defining novel data analytics and decision support approaches in a myriad of real-life scenarios and problems, with Cybersecurity-related domains being no exception. This contributed volume comprises nine chapters, written by leading international researchers, covering a compilation of recent advances in Cybersecurity-related applications of data analytics and decision support approaches. In addition to theoretical studies and overviews of existing relevant literature, this book comprises a selection of application-oriented research contributions. The investigations undertaken across these chapters focus on diverse and critical Cybersecurity problems, such as Intrusion Detection, Insider Threats, Insider Threats, Collusion Detection, Run-Time Malware Detection, Intrusion Detection, E-Learning, Online Examinations, Cybersecurity noisy data removal, Secure Smart Power Systems, Security Visualization and Monitoring. Researchers and professionals alike will find the chapters an essential read for further research on the topic.
Data Analytics and Digital Transformation (Business and Digital Transformation)
by Erik Beulen Marla A. DansUnderstanding the significance of data analytics is paramount for digital transformation but in many organizations they are separate units without fully aligned goals. As organizations are applying digital transformations to be adaptive and agile in a competitive environment, data analytics can play a critical role in their success. This book explores the crossroads between them and how to leverage their connection for improved business outcomes. The need to collaborate and share data is becoming an integral part of digital transformation. This not only creates new opportunities but also requires well-considered and continuously assessed decision-making as competitiveness is at stake. This book details approaches, concepts, and frameworks, as well as actionable insights and good practices, including combined data management and agile concepts. Critical issues are discussed such as data quality and data governance, as well as compliance, privacy, and ethics. It also offers insights into how both private and public organizations can innovate and keep up with growing data volumes and increasing technological developments in the short, mid, and long term. This book will be of direct appeal to global researchers and students across a range of business disciplines, including technology and innovation management, organizational studies, and strategic management. It is also relevant for policy makers, regulators, and executives of private and public organizations looking to implement successful transformation policies.
Data Analytics Applications in Education (Data Analytics Applications)
by Jan Vanthienen Kristof De WitteThe abundance of data and the rise of new quantitative and statistical techniques have created a promising area: data analytics. This combination of a culture of data-driven decision making and techniques to include domain knowledge allows organizations to exploit big data analytics in their evaluation and decision processes. Also, in education and learning, big data analytics is being used to enhance the learning process, to evaluate efficiency, to improve feedback, and to enrich the learning experience. As every step a student takes in the online world can be traced, analyzed, and used, there are plenty of opportunities to improve the learning process of students. First, data analytics techniques can be used to enhance the student’ s learning process by providing real-time feedback, or by enriching the learning experience. Second, data analytics can be used to support the instructor or teacher. Using data analytics, the instructor can better trace, and take targeted actions to improve, the learning process of the student. Third, there are possibilities in using data analytics to measure the performance of instructors. Finally, for policy makers, it is often unclear how schools use their available resources to "produce" outcomes. By combining structured and unstructured data from various sources, data analytics might provide a solution for governments that aim to monitor the performance of schools more closely. Data analytics in education should not be the domain of a single discipline. Economists should discuss the possibilities, issues, and normative questions with a multidisciplinary team of pedagogists, philosophers, computer scientists, and sociologists. By bringing together various disciplines, a more comprehensive answer can be formulated to the challenges ahead. This book starts this discussion by highlighting some economic perspectives on the use of data analytics in education. The book begins a rich, multidisciplinary discussion that may make data analytics in education seem as natural as a teacher in front of a classroom.
Data Analytics Applications in Emerging Markets
by Jose Antonio Nuñez Mora M. Beatriz Mota AragonThis book analyzes the impact of technology in emerging markets by considering conditions and the history of how it has changed the way of working and market development in such contexts. The book delves into key areas such as fintech enterprises, artificial intelligence, pension funds, stock markets, and energy markets though applied studies and research. This book is a useful read for practitioners and scholars interested in how technology has and continues to change the way in which development is defined and achieved, particularly in emerging markets.
Data Analytics Applications in Latin America and Emerging Economies (Data Analytics Applications)
by Eduardo RodriguezThis book focuses on understanding the analytics knowledge management process and its comprehensive application to various socioeconomic sectors. Using cases from Latin America and other emerging economies, it examines analytics knowledge applications where a solution has been achieved. Written for business students and professionals as well as researchers, the book is filled with practical insight into applying concepts and implementing processes and solutions. The eleven case studies presented in the book incorporate the whole analytics process and are useful reference examples for applying the analytics process for SME organizations in both developing and developed economies. The cases also identify multiple tacit factors to deal with during the implementation of analytics knowledge management processes. These factors, which include data cleaning, data gathering, and interpretation of results, are not always easily identified by analytics practitioners. This book promotes the understanding of analytics methods and techniques. It guides readers through numerous techniques and methods available to analytics practitioners by explaining the strengths and weaknesses of these methods and techniques.
Data Analytics-Based Demand Profiling and Advanced Demand Side Management for Flexible Operation of Sustainable Power Networks (Springer Theses)
by Jelena PonoćkoThis thesis deals with two important and very timely aspects of the future power system operation - assessment of demand flexibility and advanced demand side management (DSM) facilitating flexible and secure operation of the power network. It provides a clear and comprehensive literature review in these two areas and states precisely the original contributions of the research. The book first demonstrates the benefits of data mining for a reliable assessment of demand flexibility and its composition even with very limited observability of the end-users. It then illustrates the importance of accurate load modelling for efficient application of DSM and considers different criteria in designing DSM programme to achieve several objectives of the network performance simultaneously. Finally, it demonstrates the importance of considering realistic assumptions when planning and estimating the success of DSM programs.The findings presented here have both scientific and practical significance; they gained her BSc and MSc degrees in electrical engineering from the University of Belgrade in 2011 and 2012 respectively. She graduated with her PhD from the University of Manchester. She has presented at several conferences, and has won runner-up prizes in poster presentation at three. She has authored or co-authored more than 40 journal, conference and technical papers.provide a basis for further research, and can be used to guide future applications in industry.