"Gathering data and using it to inform instruction is a requirement for many schools, yet educators are not necessarily formally trained in how to do it. This book helps bridge the gap between classroom practice and the principles of educational psychology. Teachers will find cutting-edge advances in research and theory on human learning and teaching in an easily understood and transferable format. The text's integrated model shows teachers, school leaders, and district administrators how to establish a data culture and transform quantitative and qualitative data into actionable knowledge based on: assessment; statistics; instructional and differentiated psychology; classroom management."--Publisher's description.
The ability to quickly make good decisions is the hallmark for transformational presence. The ability to make decisions that work is an essential characteristic that the world has been, and is still yelling for. The world is crying, groaning, yelling for most of us to revisit our decision-making processes to incorporate transformational presence and to incorporate even the basics of genuine transformation within ourselves and around us. This book is a transformational guide to making such decisions that work on the bigger picture. The book introduces models and concepts to effective and transformational decision-making skills at a time when most people have put off making decisions by endlessly searching for more information or entirely outsourcing other people to offer their recommendations. There is an increasing peril of dependence in decision-making among most individuals. A fundamental question cannot be answered by someone else. Individuals, families, organizations and societies have the wisdom and capacity to champion transformation by refining their decision-making capacities. To refine that capacity, it is essential to create light within self by igniting your decision making capacity through enhancing your perception and intelligence.
Digital disruption is accelerating. Implementing a successful digital transformation strategy requires that senior managers make trade-off decisions to reinvent a business. Equally important all decision makers must learn to ask the right questions, use data and computer support in decision making, and increase their knowledge and skills. Creating a data-centric culture and rewarding data-based decision making leads to successful digital transformation. Join the digital journey. This book is targeted at managers, especially middle-level managers who are trying to come to grips with using data-based decision making in a transforming organization. The authors explore a number of broad questions including: How can managers become data-based decision makers? How can digital transformation become part of an organizational strategy? What new skills do managers need to implement digital transformation? How will we know an organization has been successfully transformed?
The main purpose of this paper is to contribute to the discussion about the design of computer and communication systems that can aid the management process. 1.1 Historical Overview We propose that Decision Support System can be considered as a design conception conceived within the computer industry to facilitate the use of computer technology in organisations (Keen, 1991). This framework, built during the late 1970s, offers computer and communication technology as support to the decision process which constitutes, in this view, the core of the management process. The DSS framework offers the following capabilities: • Access: ease of use, wide variety of data, analysis and modelling capacity. • Technological: software gel)eration tools. • Development modes: interactive and evolutionary. Within this perspective, computer and communication technologies are seen as an amplification of the human data processing capabilities which limit the decision process. Thus, the human being is understood metaphorically as a data processing machine. Mental processes are associated with the manipulation of symbols aOO human communication to signal transmission.
From the ill-fated dot-com bubble to unprecedented merger and acquisition activity to scandal, greed, and, ultimately, recession -- we've learned that widespread and difficult change is no longer the exception. By outlining the process organizations have used to achieve transformational goals and by identifying where and how even top performers derail during the change process, Kotter provides a practical resource for leaders and managers charged with making change initiatives work.
By one estimate, 90 percent of all of the data in history was created in the last two years. In 2014, International Data Corporation calculated the data universe at 4.4 zettabytes, or 4.4 trillion gigabytes. That much information, in volume, could fill enough slender iPad Air tablets to create a stack two-thirds of the way to the moon. Now, that's Big Data. Coal, iron ore, and oil were the key productive assets that fueled the Industrial Revolution. The vital raw material of today's information economy is data. In Data-ism, New York Times reporter Steve Lohr explains how big-data technology is ushering in a revolution in proportions that promise to be the basis of the next wave of efficiency and innovation across the economy. But more is at work here than technology. Big data is also the vehicle for a point of view, or philosophy, about how decisions will be—and perhaps should be—made in the future. Lohr investigates the benefits of data while also examining its dark side. Data-ism is about this next phase, in which vast Internet-scale data sets are used for discovery and prediction in virtually every field. It shows how this new revolution will change decision making—by relying more on data and analysis, and less on intuition and experience—and transform the nature of leadership and management. Focusing on young entrepreneurs at the forefront of data science as well as on giant companies such as IBM that are making big bets on data science for the future of their businesses, Data-ism is a field guide to what is ahead, explaining how individuals and institutions will need to exploit, protect, and manage data to stay competitive in the coming years. With rich examples of how the rise of big data is affecting everyday life, Data-ism also raises provocative questions about policy and practice that have wide implications for everyone. The age of data-ism is here. But are we ready to handle its consequences, good and bad?
Systems of record (SORs) are engines that generates value for your business. Systems of engagement (SOE) are always evolving and generating new customer-centric experiences and new opportunities to capitalize on the value in the systems of record. The highest value is gained when systems of record and systems of engagement are brought together to deliver insight. Systems of insight (SOI) monitor and analyze what is going on with various behaviors in the systems of engagement and information being stored or transacted in the systems of record. SOIs seek new opportunities, risks, and operational behavior that needs to be reported or have action taken to optimize business outcomes. Systems of insight are at the core of the Digital Experience, which tries to derive insights from the enormous amount of data generated by automated processes and customer interactions. Systems of Insight can also provide the ability to apply analytics and rules to real-time data as it flows within, throughout, and beyond the enterprise (applications, databases, mobile, social, Internet of Things) to gain the wanted insight. Deriving this insight is a key step toward being able to make the best decisions and take the most appropriate actions. Examples of such actions are to improve the number of satisfied clients, identify clients at risk of leaving and incentivize them to stay loyal, identify patterns of risk or fraudulent behavior and take action to minimize it as early as possible, and detect patterns of behavior in operational systems and transportation that lead to failures, delays, and maintenance and take early action to minimize risks and costs. IBM® Operational Decision Manager is a decision management platform that provides capabilities that support both event-driven insight patterns, and business-rule-driven scenarios. It also can easily be used in combination with other IBM Analytics solutions, as the detailed examples will show. IBM Operational Decision Manager Advanced, along with complementary IBM software offerings that also provide capability for systems of insight, provides a way to deliver the greatest value to your customers and your business. IBM Operational Decision Manager Advanced brings together data from different sources to recognize meaningful trends and patterns. It empowers business users to define, manage, and automate repeatable operational decisions. As a result, organizations can create and shape customer-centric business moments. This IBM Redbooks® publication explains the key concepts of systems of insight and how to implement a system of insight solution with examples. It is intended for IT architects and professionals who are responsible for implementing a systems of insights solution requiring event-based context pattern detection and deterministic decision services to enhance other analytics solution components with IBM Operational Decision Manager Advanced.
In Dempster–Shafer evidence theory, the basic probability assignment (BPA) can effectively represent and process uncertain information. How to transform the BPA of uncertain information into a decision probability remains a problem to be solved. In the light of this issue, we develop a novel decision probability transformation method to realize the transition from the belief decision to the probability decision in the framework of Dempster–Shafer evidence theory. The newly proposed method considers the transformation of BPA with multi-subset focal elements from the perspective of the belief interval, and applies the continuous interval argument ordered weighted average operator to quantify the data information contained in the belief interval for each singleton. Afterward, we present an approach to calculate the support degree of the singleton based on quantitative data information. According to the support degree of the singleton, the BPA of multi-subset focal elements is allocated reasonably. Furthermore, we introduce the concepts of probabilistic information content in this paper, which is utilized to evaluate the performance of the decision probability transformation method. Eventually, a few numerical examples and a practical application are given to demonstrate the rationality and accuracy of our proposed method.
Decisions, large and small, determine the course and quality of life--for an individual, a couple, a family, an organization, a community, a country, and the world. Today, we are experiencing a decision-making crisis. Increasing complexities in life are outpacing our decision-making resources. In this book, the authors, a life coach/consultant and a psychotherapist, introduce and explain a revolutionary, transformative decision-making process that: *Engages the whole human being--spiritually, intellectually, socially and emotionally;*Improves relationships at all levels;*Calls forth latent qualities & capacities in its practitioners;*Fosters true interpenetration of diverse thoughts;*Dramatically deepens understanding;*Yields creative, robust decisions that rise above partisan & ideological bickering;*Results in justice & unity.
Connect data and instruction to improve practice Gathering data and using it to inform instruction is a requirement for many schools, yet educators are not necessarily formally trained in how to do it. This book helps bridge the gap between classroom practice and the principles of educational psychology. Teachers will find cutting-edge advances in research and theory on human learning and teaching in an easily understood and transferable format. The text′s integrated model shows teachers, school leaders, and district administrators how to establish a data culture and transform quantitative and qualitative data into actionable knowledge based on: Assessment Statistics Instructional and differentiated psychology Classroom management
