Data Driven Decision Making In Entrepreneurship

Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the "Data Doc," shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professionally. Whether you're a top executive, an aspiring leader, or a product-line manager, this eye-opening book provides the tools and thinking you need to do that.

Since the beginning of the 21st century, there has been an explosion in startup organizations. Together, these organizations have been valued at over $3 trillion. In 2019, alone, nearly $300 billion of venture capital was invested globally (Global Startup Ecosystem Report 2020). Simultaneously, an explosion in high volume and high velocity of big data is rapidly changing how organizations function. Gone are the days where organizations can make decisions solely on intuition, logic, or experience. Some have gone as far as to say that data is the most valuable currency and resource available to businesses, and startups are no exception. However, startups and small businesses do differ from their larger counterparts and corporations in three distinct ways: 1) they tend to have fewer resources, time, and specialized training to devote to data analytics; 2) they are part of a unique entrepreneurial ecosystem with unique needs; 3) scholarship and academic research on human capital data analytics in startups is lacking. Existing entrepreneurship research focuses almost exclusively on macro-level aspects. There has been little to no integration of micro- and meso-level research (i.e., individual and team sciences), which is unfortunate given how organizational scientists have significantly advanced human capital data analytics. Unlike other books focused on data analytics and decision for organizations, this proposed book is purposefully designed to be more specifically aimed at addressing the unique idiosyncrasies of the science, research, and practice of startups. Each chapter highlights a specific organizational domain and discuss how a novel data analytic technique can help enhance decision-making, provides a tutorial of said regarding the data analytic technique, and lists references and resources for the respective data analytic technique. The volume will be grounded in sound theory and practice of organizational psychology, entrepreneurship and management and is divided into two parts: assessing and evaluating human capital performance and the use of data analytics to manage human capital.

A hands-on guide to the use of quantitative methods and software for making successful business decisions The appropriate use of quantitative methods lies at the core of successful decisions made by managers, researchers, and students in the field of business. Providing a framework for the development of sound judgment and the ability to utilize quantitative and qualitative approaches, Data Driven Business Decisions introduces readers to the important role that data plays in understanding business outcomes, addressing four general areas that managers need to know about: data handling and Microsoft Excel®, uncertainty, the relationship between inputs and outputs, and complex decisions with trade-offs and uncertainty. Grounded in the author's own classroom approach to business statistics, the book reveals how to use data to understand the drivers of business outcomes, which in turn allows for data-driven business decisions. A basic, non-mathematical foundation in statistics is provided, outlining for readers the tools needed to link data with business decisions; account for uncertainty in the actions of others and in patterns revealed by data; handle data in Excel®; translate their analysis into simple business terms; and present results in simple tables and charts. The author discusses key data analytic frameworks, such as decision trees and multiple regression, and also explores additional topics, including: Use of the Excel® functions Solver and Goal Seek Partial correlation and auto-correlation Interactions and proportional variation in regression models Seasonal adjustment and what it reveals Basic portfolio theory as an introduction to correlations Chapters are introduced with case studies that integrate simple ideas into the larger business context, and are followed by further details, raw data, and motivating insights. Algebraic notation is used only when necessary, and throughout the book, the author utilizes real-world examples from diverse areas such as market surveys, finance, economics, and business ethics. Excel® add-ins StatproGo and TreePlan are showcased to demonstrate execution of the techniques, and a related website features extensive programming instructions as well as insights, data sets, and solutions to problems included in the material. Data Driven Business Decisions is an excellent book for MBA quantitative analysis courses or undergraduate general statistics courses. It also serves as a valuable reference for practicing MBAs and practitioners in the fields of statistics, business, and finance.

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?

Business Analytics: A Data-Driven Decision Making Approach for Business-Part I,/i> provides an overview of business analytics (BA), business intelligence (BI), and the role and importance of these in the modern business decision-making. The book discusses all these areas along with three main analytics categories: (1) descriptive, (2) predictive, and (3) prescriptive analytics with their tools and applications in business. This volume focuses on descriptive analytics that involves the use of descriptive and visual or graphical methods, numerical methods, as well as data analysis tools, big data applications, and the use of data dashboards to understand business performance. The highlights of this volume are: Business analytics at a glance; Business intelligence (BI), data analytics; Data, data types, descriptive analytics; Data visualization tools; Data visualization with big data; Descriptive analytics-numerical methods; Case analysis with computer applications.

Discover solutions to common obstacles faced by project managers. Written as a business novel, the book is highly interactive, allowing readers to participate and consider options at each stage of a project. The book is based on years of experience, both through the author's research projects as well as his teaching lectures at business schools. The book tells the story of Emily Reed and her colleagues who are in charge of the management of a new tennis stadium project. The CEO of the company, Jacob Mitchell, is planning to install a new data-driven project management methodology as a decision support tool for all upcoming projects. He challenges Emily and her team to start a journey in exploring project data to fight against unexpected project obstacles. Data-driven project management is known in the academic literature as “dynamic scheduling” or “integrated project management and control.” It is a project management methodology to plan, monitor, and control projects in progress in order to deliver them on time and within budget to the client. Its main focus is on the integration of three crucial aspects, as follows: Baseline Scheduling: Plan the project activities to create a project timetable with time and budget restrictions. Determine start and finish times of each project activity within the activity network and resource constraints. Know the expected timing of the work to be done as well as an expected impact on the project’s time and budget objectives. Schedule Risk Analysis: Analyze the risk of the baseline schedule and its impact on the project’s time and budget. Use Monte Carlo simulations to assess the risk of the baseline schedule and to forecast the impact of time and budget deviations on the project objectives. Project Control: Measure and analyze the project’s performance data and take actions to bring the project on track. Monitor deviations from the expected project progress and control performance in order to facilitate the decision-making process in case corrective actions are needed to bring projects back on track. Both traditional Earned Value Management (EVM) and the novel Earned Schedule (ES) methods are used. What You'll Learn Implement a data-driven project management methodology (also known as "dynamic scheduling") which allows project managers to plan, monitor, and control projects while delivering them on time and within budget Study different project management tools and techniques, such as PERT/CPM, schedule risk analysis (SRA), resource buffering, and earned value management (EVM) Understand the three aspects of dynamic scheduling: baseline scheduling, schedule risk analysis, and project control Who This Book Is For Project managers looking to learn data-driven project management (or "dynamic scheduling") via a novel, demonstrating real-time simulations of how project managers can solve common project obstacles

This book deals with Business Analytics (BA) - an emerging area in modern business decision making. Business analytics is a data driven decision making approach that uses statistical and quantitative analysis along with data mining, management science, and fact-based data to measure past business performance to guide an organization in business planning and effective decision making. Business Analytics tools are also used to predict future business outcomes with the help of forecasting and predictive modeling.In this age of technology, massive amount of data are collected by companies. Successful companies use their data as an asset and use them for competitive advantage. Business Analytics is helping businesses in making informed business decisions and automating and optimizing business processes.Successful business analytics depends on the quality of data. Skilled analysts, who understand the technologies and their business, use business analytics tools as an organizational commitment to data-driven decision making.

In today's academic environment, the challenge of ensuring lasting commercial and economic success for organizations has become more daunting than ever before. The relentless surge in data-driven decision-making, based on innovative technologies such as blockchain, IoT, and AI, has created a digital frontier filled with complexity. Maintaining a healthy firm that can continually provide innovative products and services to the public while fueling economic growth has become a formidable puzzle. Moreover, this digital transformation has ushered in new risks, from pervasive cybersecurity threats to the ethical challenges surrounding artificial intelligence. In this evolving landscape, academic scholars face the pressing challenge of deciphering the path to long-term organizational prosperity in an era dominated by data. Data-Driven Decision Making for Long-Term Business Success serves as guidance and insights amidst this academic challenge. It is the definitive solution for scholars seeking to uncover the complexities of data-driven decision-making and its profound impact on organizational success. Each meticulously curated chapter delves into a specific facet of this transformative journey, from the implications of modern technologies and pricing optimization to the ethics underpinning data-driven strategies and the metaverse's influence on decision-making.