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Data-Driven Process Discovery and Analysis: 8th IFIP WG 2.6 International Symposium, SIMPDA 2018, Seville, Spain, December 13–14, 2018, and 9th International Symposium, SIMPDA 2019, Bled, Slovenia, September 8, 2019, Revised Selected Papers (Lecture Notes in Business Information Processing #379)
by Paolo Ceravolo Maurice Van Keulen María Teresa Gómez-LópezThis book constitutes revised selected papers from the 8th and 9th IFIP WG 2.6 International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2018, held in Seville, Spain, on December 13–14, 2018, and SIMPDA 2019, held in Bled, Slovenia, on September 8, 2019. From 16 submissions received for SIMPDA 2018 and 9 submissions received for SIMPDA 2019, 3 papers each were carefully reviewed and selected for presentation in this volume. They cover theoretical issues related to process representation, discovery, and analysis or provide practical and operational examples of their application.
Data-Driven Process Discovery and Analysis: 7th IFIP WG 2.6 International Symposium, SIMPDA 2017, Neuchatel, Switzerland, December 6-8, 2017, Revised Selected Papers (Lecture Notes in Business Information Processing #340)
by Paolo Ceravolo Maurice Van Keulen Kilian StoffelThis book constitutes the revised selected papers from the 7th IFIP WG 2.6 International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2017, held in Neuchatel, Switzerland, in December 2017. The 6 papers presented in this volume were carefully reviewed and selected from 19 submissions. They cover theoretical issues related to process representation, discovery, and analysis or provide practical and operational examples of their application.
The Data-Driven Project Manager: A Statistical Battle Against Project Obstacles
by Mario VanhouckeDiscover 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 LearnImplement 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 budgetStudy 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 controlWho This Book Is ForProject 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
Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering)
by Xiao-Sheng Si Zheng-Xin Zhang Chang-Hua HuThis book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail. The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based prognosis, residual storage life prognosis, and prognostic information-based decision-making.
Data-driven Retailing: A Non-technical Practitioners' Guide (Management for Professionals)
by Louis-Philippe KerkhoveThis book provides retail managers with a practical guide to using data. It covers three topics that are key areas of innovation for retailers: Algorithmic Marketing, Logistics, and Pricing. Use cases from these areas are presented and discussed in a conceptual and comprehensive manner. Retail managers will learn how data analysis can be used to optimize pricing, customer loyalty and logistics without complex algorithms.The goal of the book is to help managers ask the right questions during a project, which will put them on the path to making the right decisions. It is thus aimed at practitioners who want to use advanced techniques to optimize their retail organization.
Data Driven Science for Clinically Actionable Knowledge in Diseases (Analytics and AI for Healthcare)
by Daniel R. Catchpoole Simeon J. Simoff Paul J. Kennedy Quang Vinh NguyenData-driven science has become a major decision-making aid for the diagnosis and treatment of disease. Computational and visual analytics enables effective exploration and sense making of large and complex data through the deployment of appropriate data science methods, meaningful visualisation and human-information interaction. This edited volume covers state-of-the-art theory, method, models, design, evaluation and applications in computational and visual analytics in desktop, mobile and immersive environments for analysing biomedical and health data. The book is focused on data-driven integral analysis, including computational methods and visual analytics practices and solutions for discovering actionable knowledge in support of clinical actions in real environments. By studying how data and visual analytics have been implemented into the healthcare domain, the book demonstrates how analytics influences the domain through improving decision making, specifying diagnostics, selecting the best treatments and generating clinical certainty.
Data-Enabled Analytics: DEA for Big Data (International Series in Operations Research & Management Science #312)
by Joe Zhu Vincent CharlesThis book explores the novel uses and potentials of Data Envelopment Analysis (DEA) under big data. These areas are of widespread interest to researchers and practitioners alike. Considering the vast literature on DEA, one could say that DEA has been and continues to be, a widely used technique both in performance and productivity measurement, having covered a plethora of challenges and debates within the modelling framework.
Data Enclaves
by Kean BirchThis book focuses on our increasing dependence upon Big Tech to live, manage, and enjoy our lives. The author examines how we freely exchange our personal data for access to online platforms, services, and devices without proper consideration of the implications of this trade. Our personal data is the defining resource of the emerging digital economy, and it is increasingly concentrated in a few data enclaves controlled by Big Tech firms, cementing an increasingly parasitic form of technoscientific innovation. Big Tech controls access to these data, dictates the terms of our use of their services and products, and controls the future development of key technologies like artificial intelligence. The contention of this book is that we need to rethink our political and policy approach to data governance and to do so requires unpacking the peculiarities of personal data and how personal data are transformed into a valuable asset.
Data Engineering: Mining, Information and Intelligence (International Series in Operations Research & Management Science #132)
by John Talburt Terry M. Talley Yupo ChanDATA ENGINEERING: Mining, Information, and Intelligence describes applied research aimed at the task of collecting data and distilling useful information from that data. Most of the work presented emanates from research completed through collaborations between Acxiom Corporation and its academic research partners under the aegis of the Acxiom Laboratory for Applied Research (ALAR). Chapters are roughly ordered to follow the logical sequence of the transformation of data from raw input data streams to refined information. Four discrete sections cover Data Integration and Information Quality; Grid Computing; Data Mining; and Visualization. Additionally, there are exercises at the end of each chapter. The primary audience for this book is the broad base of anyone interested in data engineering, whether from academia, market research firms, or business-intelligence companies. The volume is ideally suited for researchers, practitioners, and postgraduate students alike. With its focus on problems arising from industry rather than a basic research perspective, combined with its intelligent organization, extensive references, and subject and author indices, it can serve the academic, research, and industrial audiences.
Data Engineering with Alteryx: Helping data engineers apply DataOps practices with Alteryx
by Paul HoughtonBuild and deploy data pipelines with Alteryx by applying practical DataOps principlesKey FeaturesLearn DataOps principles to build data pipelines with AlteryxBuild robust data pipelines with Alteryx DesignerUse Alteryx Server and Alteryx Connect to share and deploy your data pipelinesBook DescriptionAlteryx is a GUI-based development platform for data analytic applications.Data Engineering with Alteryx will help you leverage Alteryx's code-free aspects which increase development speed while still enabling you to make the most of the code-based skills you have.This book will teach you the principles of DataOps and how they can be used with the Alteryx software stack. You'll build data pipelines with Alteryx Designer and incorporate the error handling and data validation needed for reliable datasets. Next, you'll take the data pipeline from raw data, transform it into a robust dataset, and publish it to Alteryx Server following a continuous integration process.By the end of this Alteryx book, you'll be able to build systems for validating datasets, monitoring workflow performance, managing access, and promoting the use of your data sources.What you will learnBuild a working pipeline to integrate an external data sourceDevelop monitoring processes for the pipeline exampleUnderstand and apply DataOps principles to an Alteryx data pipelineGain skills for data engineering with the Alteryx software stackWork with spatial analytics and machine learning techniques in an Alteryx workflow Explore Alteryx workflow deployment strategies using metadata validation and continuous integrationOrganize content on Alteryx Server and secure user accessWho this book is forIf you're a data engineer, data scientist, or data analyst who wants to set up a reliable process for developing data pipelines using Alteryx, this book is for you. You'll also find this book useful if you are trying to make the development and deployment of datasets more robust by following the DataOps principles. Familiarity with Alteryx products will be helpful but is not necessary.
Data Envelopment Analysis: A Handbook of Models and Methods (International Series in Operations Research & Management Science #221)
by Joe ZhuThis handbook compiles state-of-the-art empirical studies and applications using Data Envelopment Analysis (DEA). It includes a collection of 18 chapters written by DEA experts. Chapter 1 examines the performance of CEOs of U. S. banks and thrifts. Chapter 2 describes the network operational structure of transportation organizations and the relative network data envelopment analysis model. Chapter 3 demonstrates how to use different types of DEA models to compute total-factor energy efficiency scores with an application to energy efficiency. In chapter 4, the authors explore the impact of incorporating customers' willingness to pay for service quality in benchmarking models on cost efficiency of distribution networks, and chapter 5 provides a brief review of previous applications of DEA to the professional baseball industry, followed by two detailed applications to Major League Baseball. Chapter 6 examines efficiency and productivity of U. S. property-liability (P-L) insurers using DEA, while chapter 7 presents a two-stage network DEA model that decomposes the overall efficiency of a decision-making unit into two components. Chapter 8 presents a review of the literature of DEA models for the perfoemance assessment of mutual funds, and chapter 9 discusses the management strategies formulation of the international tourist hotel industry in Taiwan. Chapter 10 presents a novel use of the two-stage network DEA to evaluate sustainable product design performances. In chapter 11 authors highlight limitations of some DEA environmental efficiency models, and chapter 12 reviews applications of DEA in secondary and tertiary education. Chapter 13 measures the relative performance of New York State school districts in the 2011-2012 academic year. Chapter 14 provides an introductory prelude to chapters 15 and 16, which both provide detailed applications of DEA in marketing. Chapter 17 then shows how to decompose a new total factor productivity index that satisfies all economically-relevant axioms from index theory with an application to U. S. agriculture. Finally, chapter 18 presents a unique study that conducts a DEA research front analysis, applying a network clustering method to group the DEA literature over the period 2000 to 2014.
Data Envelopment Analysis in the Financial Services Industry: A Guide for Practitioners and Analysts Working in Operations Research Using DEA (International Series in Operations Research & Management Science #266)
by H. David Sherman Joseph C. Paradi Fai Keung TamThis book presents the methodology and applications of Data Envelopment Analysis (DEA) in measuring productivity, efficiency and effectiveness in Financial Services firms such as banks, bank branches, stock markets, pension funds, mutual funds, insurance firms, credit unions, risk tolerance, and corporate failure prediction. Financial service DEA research includes banking; insurance businesses; hedge, pension and mutual funds; and credit unions. Significant business transactions among financial service organizations such as bank mergers and acquisitions and valuation of IPOs have also been the focus of DEA research. The book looks at the range of DEA uses for financial services by presenting prior studies, examining the current capabilities reflected in the most recent research, and projecting future new uses of DEA in finance related applications.
Data Envelopment Analysis with GAMS: A Handbook on Productivity Analysis and Performance Measurement (International Series in Operations Research & Management Science #338)
by Ali Emrouznejad Konstantinos Petridis Vincent CharlesThis book provides a comprehensive and practical introduction to Data Envelopment Analysis (DEA). It explains how this non-parametric technique is used to measure performance and extract efficiency from homogeneous entities within a production procedure. It situates DEA within a growing field of productivity analysis and performance measurement, for which numerous models have been proposed. This book encapsulates all of the advances in DEA models proposed in the literature. These models are presented in the context of the GAMS software, which is a powerful tool for mathematical programming models. This book serves two educational purposes: it introduces readers to DEA models and provides examples using GAMS. In addition, the reader is introduced to GAMS programming, as well as innovative and practical applications. GAMS codes are available for free, allowing readers to test and expand the models to meet their specific needs.
Data Envelopment Analysis with R (Studies in Fuzziness and Soft Computing #386)
by Farhad Hosseinzadeh Lotfi Ali Ebrahimnejad Mohsen Vaez-Ghasemi Zohreh MoghaddasThis book introduces readers to the use of R codes for optimization problems. First, it provides the necessary background to understand data envelopment analysis (DEA), with a special emphasis on fuzzy DEA. It then describes DEA models, including fuzzy DEA models, and shows how to use them to solve optimization problems with R. Further, it discusses the main advantages of R in optimization problems, and provides R codes based on real-world data sets throughout. Offering a comprehensive review of DEA and fuzzy DEA models and the corresponding R codes, this practice-oriented reference guide is intended for masters and Ph.D. students in various disciplines, as well as practitioners and researchers.
Data Ethics: Practical Strategies for Implementing Ethical Information Management and Governance
by Katherine O'Keefe Daragh O BrienData-gathering technology is more sophisticated than ever, as are the ethical standards for using this data. This second edition shows how to navigate this complex environment.Data Ethics provides a practical framework for the implementation of ethical principles into information management systems. It shows how to assess the types of ethical dilemmas organizations might face as they become more data-driven. This fully updated edition includes guidance on sustainability and environmental management and on how ethical frameworks can be standardized across cultures that have conflicting values. There is also discussion of data colonialism, the challenge of ethical trade-offs with ad-tech and analytics such as Covid-19 tracking systems and case studies on Smart Cities and Demings Principles.As the pace of developments in data-processing technology continues to increase, it is vital to capitalize on the opportunities this affords while ensuring that ethical standards and ideals are not compromised. Written by internationally regarded experts in the field, Data Ethics is the essential guide for students and practitioners to optimizing ethical data standards in organizations.
Data-First Marketing: How To Compete and Win In the Age of Analytics
by Janet Driscoll Miller Julia LimSupercharge your marketing strategy with data analytics In Data-First Marketing: How to Compete & Win in the Age of Analytics, distinguished authors Miller and Lim demystify the application of data analytics to marketing in any size business. Digital transformation has created a widening gap between what the CEO and business expect marketing to do and what the CMO and the marketing organization actually deliver. The key to unlocking the true value of marketing is data – from actual buyer behavior to targeting info on social media platforms to marketing’s own campaign metrics. Data is the next big battlefield for not just marketers, but also for the business because the judicious application of data analytics will create competitive advantage in the Age of Analytics. Miller and Lim show marketers where to start by leveraging their decades of experience to lay out a step-by-step process to help businesses transform into data-first marketing organizations. The book includes a self-assessment which will help to place your organization on the Data-First Marketing Maturity Model and serve as a guide for which steps you might need to focus on to complete your own transformation. Data-First Marketing: How to Compete & Win in the Age of Analytics should be used by CMOs and heads of marketing to institute a data-first approach throughout the marketing organization. Marketing staffers can pick up practical tips for incorporating data in their daily tasks using the Data-First Marketing Campaign Framework. And CEOs or anyone in the C-suite can use this book to see what is possible and then help their marketing teams to use data analytics to increase pipeline, revenue, customer loyalty – anything that drives business growth.
Data Flood: Helping the Navy Address the Rising Tide of Sensor Information
by Evan Saltzman Bradley Wilson Erin-Elizabeth Johnson Shane Tierney Isaac R. PorcheNavy analysts are struggling to keep pace with the growing flood of data collected by intelligence, surveillance, and reconnaissance sensors. This challenge is sure to intensify as the Navy continues to field new and additional sensors. The authors explore options for solving the Navy's "big data" challenge, considering changes across four dimensions: people, tools and technology, data and data architectures, and demand and demand management.
The Data Game: Controversies in Social Science Statistics (Habitat Guides)
by Mark Maier Jennifer ImazekiThis book introduces students to the collection, uses, and interpretation of statistical data in the social sciences. It would suit all social science introductory statistics and research methods courses. Separate chapters are devoted to data in the fields of demography, housing, health, education, crime, the economy, wealth, income, poverty, labor, business statistics, and public opinion polling, with a concluding chapter devoted to the common problem of ambiguity. Each chapter includes multiple case studies illustrating the controversies, overview of data sources including web sites, chapter summary and a set of case study questions designed to stimulate further thought.
Data.gov (Abridged)
by Karim R. Lakhani Robert D. Austin Yumi YiThis case presents the logic and execution underlying the launch of Data.gov, an instantiation of President Obama's initiative for transparency and open government. The process used by Vivek Kundra, the federal CIO, and his team to rapidly develop the website and to make available high-value data sets for reuse is highlighted. The case recounts Kundra's experience at the state and local government levels in developing open data initiatives and the application of that experience to the federal government. The case demonstrates the benefits of making government data available in terms of both engaged citizens and the potential for new innovations from the private sector. Potential drawbacks of open access including security and privacy issues are illustrated. Issues related to the role of government in releasing data and the balance between accountability and private-sector innovation are explored.
Data Governance: From the Fundamentals to Real Cases
by Ismael Caballero Mario PiattiniThis book presents a set of models, methods, and techniques that allow the successful implementation of data governance (DG) in an organization and reports real experiences of data governance in different public and private sectors. To this end, this book is composed of two parts. Part I on “Data Governance Fundamentals” begins with an introduction to the concept of data governance that stresses that DG is not primarily focused on databases, clouds, or other technologies, but that the DG framework must be understood by business users, systems personnel, and the systems themselves alike. Next, chapter 2 addresses crucial topics for DG, such as the evolution of data management in organizations, data strategy and policies, and defensive and offensive approaches to data strategy. Chapter 3 then details the central role that human resources play in DG, analysing the key responsibilities of the different DG-related roles and boards, while chapter 4 discusses the most common barriers to DG in practice. Chapter 5 summarizes the paradigm shifts in DG from control to value creation. Subsequently chapter 6 explores the needs, characteristics and key functionalities of DG tools, before this part ends with a chapter on maturity models for data governance. Part II on “Data Governance Applied” consists of five chapters which review the situation of DG in different sectors and industries. Details about DG in the banking sector, public administration, insurance companies, healthcare and telecommunications each are presented in one chapter. The book is aimed at academics, researchers and practitioners (especially CIOs, Data Governors, or Data Stewards) involved in DG. It can also serve as a reference for courses on data governance in information systems.
Data Governance and Compliance: Evolving to Our Current High Stakes Environment
by Rupa MahantiThis book sets the stage of the evolution of corporate governance, laws and regulations, other forms of governance, and the interaction between data governance and other corporate governance sub-disciplines. Given the continuously evolving and complex regulatory landscape and the growing number of laws and regulations, compliance is a widely discussed issue in the field of data. This book considers the cost of non-compliance bringing in examples from different industries of instances in which companies failed to comply with rules, regulations, and other legal obligations, and goes on to explain how data governance helps in avoiding such pitfalls.The first in a three-volume series on data governance, this book does not assume any prior or specialist knowledge in data governance and will be highly beneficial for IT, management and law students, academics, information management and business professionals, and researchers to enhance their knowledge and get guidance in managing their own data governance projects from a governance and compliance perspective.
Data Governance and Data Management: Contextualizing Data Governance Drivers, Technologies, and Tools
by Rupa MahantiThis book delves into the concept of data as a critical enterprise asset needed for informed decision making, compliance, regulatory reporting and insights into trends, behaviors, performance and patterns. With good data being key to staying ahead in a competitive market, enterprises capture and store exponential volumes of data. Considering the business impact of data, there needs to be adequate management around it to derive the best value. Data governance is one of the core data management related functions. However, it is often overlooked, misunderstood or confused with other terminologies and data management functions. Given the pervasiveness of data and the importance of data, this book provides comprehensive understanding of the business drivers for data governance and benefits of data governance, the interactions of data governance function with other data management functions and various components and aspects of data governance that can be facilitated by technology and tools, the distinction between data management tools and data governance tools, the readiness checks to perform before exploring the market to purchase a data governance tool, the different aspects that must be considered when comparing and selecting the appropriate data governance technologies and tools from large number of options available in the marketplace and the different market players that provide tools for supporting data governance. This book combines the data and data governance knowledge that the author has gained over years of working in different industrial and research programs and projects associated with data, processes and technologies with unique perspectives gained through interviews with thought leaders and data experts. This book is highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge and get guidance on implementing data governance in their own data initiatives.
Data Governance for Managers: The Driver of Value Stream Optimization and a Pacemaker for Digital Transformation (Management for Professionals)
by Lars Michael BollwegProfessional data management is the foundation for the successful digital transformation of traditional companies. Unfortunately, many companies fail to implement data governance because they do not fully understand the complexity of the challenge (organizational structure, employee empowerment, change management, etc.) and therefore do not include all aspects in the planning and implementation of their data governance. This book explains the driving role that a responsive data organization can play in a company's digital transformation. Using proven process models, the book takes readers from the basics, through planning and implementation, to regular operations and measuring the success of data governance. All the important decision points are highlighted, and the advantages and disadvantages are discussed in order to identify digitization potential, implement it in the company, and develop customized data governance. The book will serve as a useful guide for interested newcomers as well as for experienced managers.
Data Governance für Manager: Datengetriebene Prozess- und Systemoptimierung als Taktgeber der digitalen Transformation
by Lars Michael BollwegDieses Fachbuch führt den Leser in fünf Buchteilen und mit der Hilfe praxiserprobter Vorgehensmodelle von den Grundlagen (Was ist Data Governance?), über die Planung (Welche Gestaltungsoptionen habe ich?) und Implementierung (Wie kann ich Data Governance im Unternehmen einführen?) bis zum Regelbetrieb (Wie kann ich Mehrwerte erzielen?) und der Erfolgsmessung einer Data Governance. Wie jedes Unternehmen ist auch jede Data Governance anders, deshalb werden alle wichtigen Entscheidungspunkte aufgezeigt, die Vor- und Nachteile diskutiert, um dem Leser, die Möglichkeit zu bieten, eine maßgeschneiderte Data Governance zu entwickeln.Ein professionelles Datenmanagement (Data Governance) ist die Grundlage für die erfolgreiche digitale Transformation traditioneller Unternehmen. Leider scheitern eine Vielzahl an Unternehmen an der Einführung einer Data Governance, weil sie die Komplexität der Herausforderung (Organisationsaufbau, Befähigung der Mitarbeiter, Change Management etc.) nicht vollständig überblicken und deshalb nicht alle Aspekte mit in die Planung und Umsetzung ihrer Data Governance miteinbeziehen. Hier setzt dieses Buch an: Es erläutert die treibende Rolle, die eine reaktionsfähige Datenorganisation innerhalb der digitalen Transformation eines Unternehmens einnehmen kann. Der Leser wird befähigt, Digitalisierungspotenziale aufzuzeigen und diese im Unternehmen in die Umsetzung zu überführen.Der InhaltGrundlagen Data GovernanceErfolgsfaktoren der ImplementierungEntwicklung eines reaktionsfähigen Operating Model Data Governance als Treiber der Wertstromoptimierung und Taktgeber der digitalen TransformationErfolgsmessung einer Data Governance
The Data Governance Imperative: A Business Strategy for Corporate Data
by Steve SarsfieldAttention to corporate information has never been more important than now. The ability to generate accurate business intelligence, accurate financial reports and to understand your business relies on better processes and personal commitment to clean data. Every byte of data that resides inside your company, and some that resides outside its walls, has the potential to make you stronger by giving you the agility, speed and intelligence that none of your competitors yet have. Data governance is the term given to changing the hearts and minds of your company to see the value of such information quality. "The Data Governance Imperative" is a business person's view of data governance. This practical book covers both strategies and tactics around managing a data governance initiative. The author, Steve Sarsfield, works for a major enterprise software company and is a leading expert in data quality and data governance, focusing on the business perspectives that are important to data champions, front-office employees, and executives.