| data governance in business intelligence: Data Governance John Ladley, 2019-11-08 Managing data continues to grow as a necessity for modern organizations. There are seemingly infinite opportunities for organic growth, reduction of costs, and creation of new products and services. It has become apparent that none of these opportunities can happen smoothly without data governance. The cost of exponential data growth and privacy / security concerns are becoming burdensome. Organizations will encounter unexpected consequences in new sources of risk. The solution to these challenges is also data governance; ensuring balance between risk and opportunity. Data Governance, Second Edition, is for any executive, manager or data professional who needs to understand or implement a data governance program. It is required to ensure consistent, accurate and reliable data across their organization. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. This valuable resource provides comprehensive guidance to beginning professionals, managers or analysts looking to improve their processes, and advanced students in Data Management and related courses. With the provided framework and case studies all professionals in the data governance field will gain key insights into launching successful and money-saving data governance program. - Incorporates industry changes, lessons learned and new approaches - Explores various ways in which data analysts and managers can ensure consistent, accurate and reliable data across their organizations - Includes new case studies which detail real-world situations - Explores all of the capabilities an organization must adopt to become data driven - Provides guidance on various approaches to data governance, to determine whether an organization should be low profile, central controlled, agile, or traditional - Provides guidance on using technology and separating vendor hype from sincere delivery of necessary capabilities - Offers readers insights into how their organizations can improve the value of their data, through data quality, data strategy and data literacy - Provides up to 75% brand-new content compared to the first edition |
| data governance in business intelligence: Non-Invasive Data Governance Robert S. Seiner, 2014-09-01 Data-governance programs focus on authority and accountability for the management of data as a valued organizational asset. Data Governance should not be about command-and-control, yet at times could become invasive or threatening to the work, people and culture of an organization. Non-Invasive Data Governance™ focuses on formalizing existing accountability for the management of data and improving formal communications, protection, and quality efforts through effective stewarding of data resources. Non-Invasive Data Governance will provide you with a complete set of tools to help you deliver a successful data governance program. Learn how: • Steward responsibilities can be identified and recognized, formalized, and engaged according to their existing responsibility rather than being assigned or handed to people as more work. • Governance of information can be applied to existing policies, standard operating procedures, practices, and methodologies, rather than being introduced or emphasized as new processes or methods. • Governance of information can support all data integration, risk management, business intelligence and master data management activities rather than imposing inconsistent rigor to these initiatives. • A practical and non-threatening approach can be applied to governing information and promoting stewardship of data as a cross-organization asset. • Best practices and key concepts of this non-threatening approach can be communicated effectively to leverage strengths and address opportunities to improve. |
| data governance in business intelligence: The Data Governance Imperative Steve Sarsfield, 2009-04-23 This practical book covers both strategies and tactics around managing a data governance initiative to help make the most of your data. |
| data governance in business intelligence: Performance Dashboards Wayne W. Eckerson, 2005-10-27 Tips, techniques, and trends on how to use dashboard technology to optimize business performance Business performance management is a hot new management discipline that delivers tremendous value when supported by information technology. Through case studies and industry research, this book shows how leading companies are using performance dashboards to execute strategy, optimize business processes, and improve performance. Wayne W. Eckerson (Hingham, MA) is the Director of Research for The Data Warehousing Institute (TDWI), the leading association of business intelligence and data warehousing professionals worldwide that provide high-quality, in-depth education, training, and research. He is a columnist for SearchCIO.com, DM Review, Application Development Trends, the Business Intelligence Journal, and TDWI Case Studies & Solution. |
| data governance in business intelligence: Information Quality and Governance for Business Intelligence Yeoh, William, 2013-12-31 Business intelligence initiatives have been dominating the technology priority list of many organizations. However, the lack of effective information quality and governance strategies and policies has been meeting these initiatives with some challenges. Information Quality and Governance for Business Intelligence presents the latest exchange of academic research on all aspects of practicing and managing information using a multidisciplinary approach that examines its quality for organizational growth. This book is an essential reference tool for researchers, practitioners, and university students specializing in business intelligence, information quality, and information systems. |
| data governance in business intelligence: Super Charge Your Data Warehouse Dan Linstedt, 2011-11-11 Do You Know If Your Data Warehouse Flexible, Scalable, Secure and Will It Stand The Test Of Time And Avoid Being Part Of The Dreaded Life Cycle? The Data Vault took the Data Warehouse world by storm when it was released in 2001. Some of the world's largest and most complex data warehouse situations understood the value it gave especially with the capabilities of unlimited scaling, flexibility and security. Here is what industry leaders say about the Data Vault The Data Vault is the optimal choice for modeling the EDW in the DW 2.0 framework - Bill Inmon, The Father of Data Warehousing The Data Vault is foundationally strong and an exceptionally scalable architecture - Stephen Brobst, CTO, Teradata The Data Vault should be considered as a potential standard for RDBMS-based analytic data management by organizations looking to achieve a high degree of flexibility, performance and openness - Doug Laney, Deloitte Analytics Institute I applaud Dan's contribution to the body of Business Intelligence and Data Warehousing knowledge and recommend this book be read by both data professionals and end users - Howard Dresner, From the Foreword - Speaker, Author, Leading Research Analyst and Advisor You have in your hands the work, experience and testing of 2 decades of building data warehouses. The Data Vault model and methodology has proven itself in hundreds (perhaps thousands) of solutions in Insurance, Crime-Fighting, Defense, Retail, Finance, Banking, Power, Energy, Education, High-Tech and many more. Learn the techniques and implement them and learn how to build your Data Warehouse faster than you have ever done before while designing it to grow and scale no matter what you throw at it. Ready to Super Charge Your Data Warehouse? |
| data governance in business intelligence: Data Governance: The Definitive Guide Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy-Grant, Jessi Ashdown, 2021-03-08 As your company moves data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure you meet compliance. Data governance incorporates the ways that people, processes, and technology work together to support business efficiency. With this practical guide, chief information, data, and security officers will learn how to effectively implement and scale data governance throughout their organizations. You'll explore how to create a strategy and tooling to support the democratization of data and governance principles. Through good data governance, you can inspire customer trust, enable your organization to extract more value from data, and generate more-competitive offerings and improvements in customer experience. This book shows you how. Enable auditable legal and regulatory compliance with defined and agreed-upon data policies Employ better risk management Establish control and maintain visibility into your company's data assets, providing a competitive advantage Drive top-line revenue and cost savings when developing new products and services Implement your organization's people, processes, and tools to operationalize data trustworthiness. |
| data governance in business intelligence: Data Governance Neera Bhansali, 2013-06-17 As organizations deploy business intelligence and analytic systems to harness business value from their data assets, data governance programs are quickly gaining prominence. And, although data management issues have traditionally been addressed by IT departments, organizational issues critical to successful data management require the implementation of enterprise-wide accountabilities and responsibilities. Data Governance: Creating Value from Information Assets examines the processes of using data governance to manage data effectively. Addressing the complete life cycle of effective data governance—from metadata management to privacy and compliance—it provides business managers, IT professionals, and students with an integrated approach to designing, developing, and sustaining an effective data governance strategy. Explains how to align data governance with business goals Describes how to build successful data stewardship with a governance framework Outlines strategies for integrating IT and data governance frameworks Supplies business-driven and technical perspectives on data quality management, metadata management, data access and security, and data lifecycle The book summarizes the experiences of global experts in the field and addresses critical areas of interest to the information systems and management community. Case studies from healthcare and financial sectors, two industries that have successfully leveraged the potential of data-driven strategies, provide further insights into real-time practice. Facilitating a comprehensive understanding of data governance, the book addresses the burning issue of aligning data assets to both IT assets and organizational strategic goals. With a focus on the organizational, operational, and strategic aspects of data governance, the text provides you with the understanding required to leverage, derive, and sustain maximum value from the informational assets housed in your IT infrastructure. |
| data governance in business intelligence: Emerging Trends in Intelligent Computing and Informatics Faisal Saeed, Fathey Mohammed, Nadhmi Gazem, 2019-11-01 This book presents the proceedings of the 4th International Conference of Reliable Information and Communication Technology 2019 (IRICT 2019), which was held in Pulai Springs Resort, Johor, Malaysia, on September 22–23, 2019. Featuring 109 papers, the book covers hot topics such as artificial intelligence and soft computing, data science and big data analytics, internet of things (IoT), intelligent communication systems, advances in information security, advances in information systems and software engineering. |
| data governance in business intelligence: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data governance in business intelligence: Multi-Domain Master Data Management Mark Allen, Dalton Cervo, 2015-03-21 Multi-Domain Master Data Management delivers practical guidance and specific instruction to help guide planners and practitioners through the challenges of a multi-domain master data management (MDM) implementation. Authors Mark Allen and Dalton Cervo bring their expertise to you in the only reference you need to help your organization take master data management to the next level by incorporating it across multiple domains. Written in a business friendly style with sufficient program planning guidance, this book covers a comprehensive set of topics and advanced strategies centered on the key MDM disciplines of Data Governance, Data Stewardship, Data Quality Management, Metadata Management, and Data Integration. - Provides a logical order toward planning, implementation, and ongoing management of multi-domain MDM from a program manager and data steward perspective. - Provides detailed guidance, examples and illustrations for MDM practitioners to apply these insights to their strategies, plans, and processes. - Covers advanced MDM strategy and instruction aimed at improving data quality management, lowering data maintenance costs, and reducing corporate risks by applying consistent enterprise-wide practices for the management and control of master data. |
| data governance in business intelligence: Data Virtualization for Business Intelligence Systems Rick van der Lans, 2012-07-25 Annotation In this book, Rick van der Lans explains how data virtualization servers work, what techniques to use to optimize access to various data sources and how these products can be applied in different projects. |
| data governance in business intelligence: Disrupting Data Governance Laura Madsen, 2019-12-06 Data governance is broken. It's time we fix it. Why is data governance so ineffective? The truth is data governance programs aren't designed for the way we run our data teams they aren't even designed for a modern organization at all. They were designed when reports still came through inter-office mail. The flow of data into within and out of today's organizations is a tsunami breaking through rigid data governance methods. Yet our programs still rely on that command and control approach. Have you ever tried to control a tsunami? Every organization that uses data knows that they need a data governance program. Data literacy efforts and legislation like GDPR have become the bellwethers for our governance functions. But we still sit in data governance meetings without enough people and too many questions to move things forward. There's no agility to the program because we imply a degree of frailty to the data that doesn't exist. We continue to insist on archaic methods that bring no value to our organizations. Achieving deep insights from data can't happen without good governance practices. Laura Madsen shows you how to redefine governance for the modern age. With a casual witty style Madsen taps on her decades of experience shares interviews with other best-in-field experts and grounds her perspective in research. Witness where it all fell apart challenge long-held beliefs and commit to a fundamental shift--that governance is not about stopping or preventing usage but about supporting the usage of data. Be able to bring back trust and value to our data governance functions and learn the: People-driven approach to governance Processes that support the tsunami of data Cutting edge technology that's enabling data governance |
| data governance in business intelligence: Data Governance Evren Eryurek, Uri Gilad, Jessi Ashdown, Valliappa Lakshmanan, Anita Kibunguchy, 2021-04-13 As your company moves data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure you meet compliance. Data governance incorporates the ways that people, processes, and technology work together to support business efficiency. With this practical guide, chief information, data, and security officers will learn how to effectively implement and scale data governance throughout their organizations. You'll explore how to create a strategy and tooling to support the democratization of data and governance principles. Through good data governance, you can inspire customer trust, enable your organization to extract more value from data, and generate more-competitive offerings and improvements in customer experience. This book shows you how. Enable auditable legal and regulatory compliance with defined and agreed-upon data policies Employ better risk management Establish control and maintain visibility into your company's data assets, providing a competitive advantage Drive top-line revenue and cost savings when developing new products and services Implement your organization's people, processes, and tools to operationalize data trustworthiness |
| data governance in business intelligence: Business Intelligence Guidebook Rick Sherman, 2014-11-04 Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors' tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, Big Data is belittled – projects flounder, are late and go over budget. Business Intelligence Guidebook: From Data Integration to Analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers. After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget – turning the deluge of data into actionable information that fuels business knowledge. Finally, you'll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success. - Provides practical guidelines for building successful BI, DW and data integration solutions. - Explains underlying BI, DW and data integration design, architecture and processes in clear, accessible language. - Includes the complete project development lifecycle that can be applied at large enterprises as well as at small to medium-sized businesses - Describes best practices and pragmatic approaches so readers can put them into action. - Companion website includes templates and examples, further discussion of key topics, instructor materials, and references to trusted industry sources. |
| data governance in business intelligence: Self-Service Data Analytics and Governance for Managers Nathan E. Myers, Gregory Kogan, 2021-06-02 Project governance, investment governance, and risk governance precepts are woven together in Self-Service Data Analytics and Governance for Managers, equipping managers to structure the inevitable chaos that can result as end-users take matters into their own hands Motivated by the promise of control and efficiency benefits, the widespread adoption of data analytics tools has created a new fast-moving environment of digital transformation in the finance, accounting, and operations world, where entire functions spend their days processing in spreadsheets. With the decentralization of application development as users perform their own analysis on data sets and automate spreadsheet processing without the involvement of IT, governance must be revisited to maintain process control in the new environment. In this book, emergent technologies that have given rise to data analytics and which form the evolving backdrop for digital transformation are introduced and explained, and prominent data analytics tools and capabilities will be demonstrated based on real world scenarios. The authors will provide a much-needed process discovery methodology describing how to survey the processing landscape to identify opportunities to deploy these capabilities. Perhaps most importantly, the authors will digest the mature existing data governance, IT governance, and model governance frameworks, but demonstrate that they do not comprehensively cover the full suite of data analytics builds, leaving a considerable governance gap. This book is meant to fill the gap and provide the reader with a fit-for-purpose and actionable governance framework to protect the value created by analytics deployment at scale. Project governance, investment governance, and risk governance precepts will be woven together to equip managers to structure the inevitable chaos that can result as end-users take matters into their own hands. |
| data governance in business intelligence: Healthcare Business Intelligence Laura Madsen, 2012 This book will be constructed as a guidebook for healthcare organizations that are attempting BI/DW. It will address the primary functions of a business intelligence capability and how BI can ease the increasing regulatory reporting pressures on all healthcare organizations. Also included will be tables, checklists and a few forms. Tenative chapter contents: Chapter 1: What is Healthcare BI? Chapter 2: The Five Disciplines of Business Intelligence Chapter 3: The Importance of ETL Chapter 4: Starting with Data Governance Chapter 5: Creating a BI team Chapter 6: Data Modeling for Healthcare Chapter 7: Gaining Support for your BI program Chapter 8: Ensuring good User Adoption Chapter 9: Marketing Your BI Program Chapter 10: Maintaining Your BI Program-- |
| data governance in business intelligence: The DAMA Dictionary of Data Management Dama International, 2011 A glossary of over 2,000 terms which provides a common data management vocabulary for IT and Business professionals, and is a companion to the DAMA Data Management Body of Knowledge (DAMA-DMBOK). Topics include: Analytics & Data Mining Architecture Artificial Intelligence Business Analysis DAMA & Professional Development Databases & Database Design Database Administration Data Governance & Stewardship Data Management Data Modeling Data Movement & Integration Data Quality Management Data Security Management Data Warehousing & Business Intelligence Document, Record & Content Management Finance & Accounting Geospatial Data Knowledge Management Marketing & Customer Relationship Management Meta-Data Management Multi-dimensional & OLAP Normalization Object-Orientation Parallel Database Processing Planning Process Management Project Management Reference & Master Data Management Semantic Modeling Software Development Standards Organizations Structured Query Language (SQL) XML Development |
| data governance in business intelligence: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| data governance in business intelligence: The Data and Analytics Playbook Lowell Fryman, Gregory Lampshire, Dan Meers, 2016-08-12 The Data and Analytics Playbook: Proven Methods for Governed Data and Analytic Quality explores the way in which data continues to dominate budgets, along with the varying efforts made across a variety of business enablement projects, including applications, web and mobile computing, big data analytics, and traditional data integration. The book teaches readers how to use proven methods and accelerators to break through data obstacles to provide faster, higher quality delivery of mission critical programs. Drawing upon years of practical experience, and using numerous examples and an easy to understand playbook, Lowell Fryman, Gregory Lampshire, and Dan Meers discuss a simple, proven approach to the execution of multiple data oriented activities. In addition, they present a clear set of methods to provide reliable governance, controls, risk, and exposure management for enterprise data and the programs that rely upon it. In addition, they discuss a cost-effective approach to providing sustainable governance and quality outcomes that enhance project delivery, while also ensuring ongoing controls. Example activities, templates, outputs, resources, and roles are explored, along with different organizational models in common use today and the ways they can be mapped to leverage playbook data governance throughout the organization. - Provides a mature and proven playbook approach (methodology) to enabling data governance that supports agile implementation - Features specific examples of current industry challenges in enterprise risk management, including anti-money laundering and fraud prevention - Describes business benefit measures and funding approaches using exposure based cost models that augment risk models for cost avoidance analysis and accelerated delivery approaches using data integration sprints for application, integration, and information delivery success |
| data governance in business intelligence: Managing Data in Motion April Reeve, 2013-02-26 Managing Data in Motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment. The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired. The management of the data in motion in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and big data applications are just a few of the challenges facing organizations and businesses today. Managing Data in Motion tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects. - Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems including the emerging solutions for unstructured as well as structured data types - Explains, in non-technical terms, the architecture and components required to perform data integration - Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of Big Data |
| data governance in business intelligence: Governance of Data Alison Holt, Benoit Aubert, David Sutton, édéric Gelissen, Nathalie de Marcellis-Warin, Abdelaziz Khadraoui, Brian Donnellan, Alisdair McKenzie, Geoff Clarke, Mihai Bilauca, Pan Rong, Jose Antonio Costa, Ming Li, Rohan Light, Ardi Kolah, Beenish Saeed, 2018-11-28 Data is fundamentally changing the nature of businesses and organisations, and the mechanisms for delivering products and services. This book is a practical guide to developing strategy and policy for data governance, in line with the developing ISO 38505 governance of data standards. It will assist an organisation wanting to become more of a data driven business by explaining how to assess the value, risks and constraints associated with collecting, using and distributing data. |
| data governance in business intelligence: Big Data Governance Sunil Soares, 2012 Written by a leading expert in the field, this guide focuses on the convergence of two major trends in information management--big data and information governance--by taking a strategic approach oriented around business cases and industry imperatives. With the advent of new technologies, enterprises are expanding and handling very large volumes of data; this book, nontechnical in nature and geared toward business audiences, encourages the practice of establishing appropriate governance over big data initiatives and addresses how to manage and govern big data, highlighting the relevant processes, procedures, and policies. It teaches readers to understand how big data fits within an overall information governance program; quantify the business value of big data; apply information governance concepts such as stewardship, metadata, and organization structures to big data; appreciate the wide-ranging business benefits for various industries and job functions; sell the value of big data governance to businesses; and establish step-by-step processes to implement big data governance. |
| data governance in business intelligence: Master Data Management David Loshin, 2010-07-28 The key to a successful MDM initiative isn't technology or methods, it's people: the stakeholders in the organization and their complex ownership of the data that the initiative will affect.Master Data Management equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you'll master all the details involved in planning and executing an MDM project that leads to measurable improvements in business productivity and effectiveness. - Presents a comprehensive roadmap that you can adapt to any MDM project - Emphasizes the critical goal of maintaining and improving data quality - Provides guidelines for determining which data to master. - Examines special issues relating to master data metadata - Considers a range of MDM architectural styles - Covers the synchronization of master data across the application infrastructure |
| data governance in business intelligence: Modern Data Strategy Mike Fleckenstein, Lorraine Fellows, 2018-02-12 This book contains practical steps business users can take to implement data management in a number of ways, including data governance, data architecture, master data management, business intelligence, and others. It defines data strategy, and covers chapters that illustrate how to align a data strategy with the business strategy, a discussion on valuing data as an asset, the evolution of data management, and who should oversee a data strategy. This provides the user with a good understanding of what a data strategy is and its limits. Critical to a data strategy is the incorporation of one or more data management domains. Chapters on key data management domains—data governance, data architecture, master data management and analytics, offer the user a practical approach to data management execution within a data strategy. The intent is to enable the user to identify how execution on one or more data management domains can help solve business issues. This book is intended for business users who work with data, who need to manage one or more aspects of the organization’s data, and who want to foster an integrated approach for how enterprise data is managed. This book is also an excellent reference for students studying computer science and business management or simply for someone who has been tasked with starting or improving existing data management. |
| data governance in business intelligence: The Case for the Chief Data Officer Peter Aiken, Michael M. Gorman, 2013-04-22 Data are an organization's sole, non-depletable, non-degrading, durable asset. Engineered right, data's value increases over time because the added dimensions of time, geography, and precision. To achieve data's full organizational value, there must be dedicated individual to leverage data as assets - a Chief Data Officer or CDO who's three job pillars are: - Dedication solely to leveraging data assets, - Unconstrained by an IT project mindset, and - Reports directly to the business Once these three pillars are set into place, organizations can leverage their data assets. Data possesses properties worthy of additional investment. Many existing CDOs are fatally crippled, however, because they lack one or more of these three pillars. Often organizations have some or all pillars already in place but are not operating in a coordinated manner. The overall objective of this book is to present these pillars in an understandable way, why each is necessary (but insufficient), and what do to about it. - Uncovers that almost all organizations need sophisticated, comprehensive data management education and strategies. - Delivery of organization-wide data success requires a highly focused, full time Chief Data Officer. - Engineers organization-wide data advantage which enables success in the marketplace |
| data governance in business intelligence: The Practitioner's Guide to Data Quality Improvement David Loshin, 2010-11-22 The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. - Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. - Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. - Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. |
| data governance in business intelligence: Data Goverence for the Executive, Orr James C., 2011-01-01 |
| data governance in business intelligence: Data Governance and Data Management Rupa Mahanti, 2021-09-08 This 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 in business intelligence: Encyclopedia of Organizational Knowledge, Administration, and Technology Khosrow-Pour D.B.A., Mehdi, 2020-09-29 For any organization to be successful, it must operate in such a manner that knowledge and information, human resources, and technology are continually taken into consideration and managed effectively. Business concepts are always present regardless of the field or industry – in education, government, healthcare, not-for-profit, engineering, hospitality/tourism, among others. Maintaining organizational awareness and a strategic frame of mind is critical to meeting goals, gaining competitive advantage, and ultimately ensuring sustainability. The Encyclopedia of Organizational Knowledge, Administration, and Technology is an inaugural five-volume publication that offers 193 completely new and previously unpublished articles authored by leading experts on the latest concepts, issues, challenges, innovations, and opportunities covering all aspects of modern organizations. Moreover, it is comprised of content that highlights major breakthroughs, discoveries, and authoritative research results as they pertain to all aspects of organizational growth and development including methodologies that can help companies thrive and analytical tools that assess an organization’s internal health and performance. Insights are offered in key topics such as organizational structure, strategic leadership, information technology management, and business analytics, among others. The knowledge compiled in this publication is designed for entrepreneurs, managers, executives, investors, economic analysts, computer engineers, software programmers, human resource departments, and other industry professionals seeking to understand the latest tools to emerge from this field and who are looking to incorporate them in their practice. Additionally, academicians, researchers, and students in fields that include but are not limited to business, management science, organizational development, entrepreneurship, sociology, corporate psychology, computer science, and information technology will benefit from the research compiled within this publication. |
| data governance in business intelligence: Data Strategy in Colleges and Universities Kristina Powers, 2019-10-16 This valuable resource helps institutional leaders understand and implement a data strategy at their college or university that maximizes benefits to all creators and users of data. Exploring key considerations necessary for coordination of fragmented resources and the development of an effective, cohesive data strategy, this book brings together professionals from different higher education experiences and perspectives, including academic, administration, institutional research, information technology, and student affairs. Focusing on critical elements of data strategy and governance, each chapter in Data Strategy in Colleges and Universities helps higher education leaders address a frustrating problem with much-needed solutions for fostering a collaborative, data-driven strategy. |
| data governance in business intelligence: Data Leadership Anthony J. Algmin, 2020-10-14 Data has never been more important to your success than it is today, yet you are surrounded with data you can't trust, and the overwhelming burden of fixing it. Everyone deserves data that helps-not hurts-their organization. |
| data governance in business intelligence: Big Data For Dummies Judith S. Hurwitz, Alan Nugent, Fern Halper, Marcia Kaufman, 2013-04-02 Find the right big data solution for your business or organization Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work. Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals Authors are experts in information management, big data, and a variety of solutions Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more Provides essential information in a no-nonsense, easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization. |
| data governance in business intelligence: E-Business Robert M.X. Wu, Marinela Mircea, 2021-05-19 This book provides the latest viewpoints of scientific research in the field of e-business. It is organized into three sections: “Higher Education and Digital Economy Development”, “Artificial Intelligence in E-Business”, and “Business Intelligence Applications”. Chapters focus on China’s higher education in e-commerce, digital economy development, natural language processing applications in business, Information Technology Governance, Risk and Compliance (IT GRC), business intelligence, and more. |
| data governance in business intelligence: Data Architecture: A Primer for the Data Scientist W.H. Inmon, Daniel Linstedt, Mary Levins, 2019-04-30 Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the bigger picture and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. - New case studies include expanded coverage of textual management and analytics - New chapters on visualization and big data - Discussion of new visualizations of the end-state architecture |
| data governance in business intelligence: Data Strategy and the Enterprise Data Executive Peter Aiken, Todd Harbour, 2017 Master a proven approach to create, implement, and sustain a data strategy. |
| data governance in business intelligence: Implementing Business Intelligence Solutions Leveraging Data Analytics for Enhanced Decision-Making SURAJ DHARMAPURAM ANTONY SATYA VIVEK VARDHAN AKISETTY RAFA ABDUL DR. SINGH RAJ, 2024-11-10 In the ever-evolving landscape of the modern world, the synergy between technology and management has become a cornerstone of innovation and progress. This book, Implementing Business Intelligence Solutions: Leveraging Data Analytics for Enhanced Decision-Making, is conceived to bridge the gap between emerging technological advancements in data analytics and their strategic application in business management. Our objective is to equip readers with the tools and insights necessary to excel in this dynamic intersection of fields. This book is structured to provide a comprehensive exploration of the methodologies and strategies that define the innovation of business intelligence (BI) solutions and their integration into decision-making practices. From foundational theories to advanced applications, we delve into the critical aspects that drive successful BI initiatives in various industries. We have made a concerted effort to present complex concepts in a clear and accessible manner, making this work suitable for a diverse audience, including students, managers, and industry professionals. In authoring this book, we have drawn upon the latest research and best practices to ensure that readers not only gain a robust theoretical understanding but also acquire practical skills that can be applied in real-world scenarios. The chapters are designed to strike a balance between depth and breadth, covering topics ranging from technological development and data analytics adoption to the strategic management of BI initiatives. Additionally, we emphasize the importance of effective communication, dedicating sections to the art of presenting data-driven insights and solutions in a precise and academically rigorous manner. The inspiration for this book arises from a recognition of the crucial role that business intelligence and data analytics play in shaping the future of business decision-making. We are profoundly grateful to Chancellor Shri Shiv Kumar Gupta of Maharaja Agrasen Himalayan Garhwal University for his unwavering support and vision. His dedication to fostering academic excellence and promoting a culture of innovation has been instrumental in bringing this project to fruition. We hope this book will serve as a valuable resource and inspiration for those eager to deepen their understanding of how data analytics and BI can be harnessed together to drive business innovation. We believe that the knowledge and insights contained within these pages will empower readers to lead the way in creating data-driven solutions that will define the future of business decision-making. Thank you for joining us on this journey. Authors |
| data governance in business intelligence: Successful Business Intelligence: Secrets to Making BI a Killer App Cindi Howson, 2007-12-17 Praise for Successful Business Intelligence If you want to be an analytical competitor, you've got to go well beyond business intelligence technology. Cindi Howson has wrapped up the needed advice on technology, organization, strategy, and even culture in a neat package. It's required reading for quantitatively oriented strategists and the technologists who support them. --Thomas H. Davenport, President's Distinguished Professor, Babson College and co-author, Competing on Analytics When used strategically, business intelligence can help companies transform their organization to be more agile, more competitive, and more profitable. Successful Business Intelligence offers valuable guidance for companies looking to embark upon their first BI project as well as those hoping to maximize their current deployments. --John Schwarz, CEO, Business Objects A thoughtful, clearly written, and carefully researched examination of all facets of business intelligence that your organization needs to know to run its business more intelligently and exploit information to its fullest extent. --Wayne Eckerson, Director, TDWI Research Using real-world examples, Cindi Howson shows you how to use business intelligence to improve the performance, and the quality, of your company. --Bill Baker, Distinguished Engineer & GM, Business Intelligence Applications, Microsoft Corporation This book outlines the key steps to make BI an integral part of your company's culture and demonstrates how your company can use BI as a competitive differentiator. --Robert VanHees, CFO, Corporate Express Given the trend to expand the business analytics user base, organizations are faced with a number of challenges that affect the success rate of these projects. This insightful book provides practical advice on improving that success rate. --Dan Vesset, Vice President, Business Analytics Solution Research, IDC |
| data governance in business intelligence: Business Intelligence Roadmap Larissa Terpeluk Moss, S. Atre, 2003 This software will enable the user to learn about business intelligence roadmap. |
| data governance in business intelligence: Business Intelligence Rajiv Sabherwal, Irma Becerra-Fernandez, 2013-02-19 Business professionals who want to advance their careers need to have a strong understanding of how to utilize business intelligence. This new book provides a comprehensive introduction to the basic business and technical concepts they’ll need to know. It integrates case studies that demonstrate how to apply the material. Business professionals will also find suggested further readings that will develop their knowledge and help them succeed. |
Definitive Guide to Data Governance - Talend
• Improved quality of data: Data governance creates a plan that ensures data accuracy, completeness, and consistency • A data map: Data governance provides an advanced ability …
A Framework for Business Intelligence Data Governance
BI Data Governance is focused on the processes and practices that define how data is managed within a BI environment. The BI Data Governance Framework introduces a structured …
Enterprise Data Architecture and Data Governance: Use …
Operationalizing data governance leverages the practical aspects of the data architecture by enabling business analysts and their IT partners to layer business rules on top of the data …
TRANSFORMING DATA INTO ACTION: THE BUSINESS …
This report, Transforming data into action: the business outlook for data governance, explores the business contributions of data governance at organisations globally and across...
The 2020 State of Data Governance and Automation
The 2020 State of Data Governance and Automation Many organizations have started their data governance journeys to achieve data intelligence, but they have not automated their data …
THE COMPREHENSIVE GUIDE TO DATA GOVERNANCE - BDO …
Effective governance is the key to unlocking the value of data. Everyone understands that data can be an asset when exploited. It can have a direct impact on company profitability and top …
The DGI Data Governance Framework - neweditions.net
This paper describes core concepts, the components of the DGI Data Governance Framework, and typical steps in implementing a program. The DGI Data Governance Framework . …
Designing data governance that delivers value - McKinsey
Six critical practices are needed to ensure data governance creates value. 1. Secure top management’s attention. As the aforementioned example highlights, success with data …
Trends in Data Governance and Data Quality
and industries are turning to data governance to provide a strong framework that enables them to proactively find, understand, and manage data and realize business success. The survey …
Data Governance - The Institute of Internal Auditors or The IIA
Data governance best practices. Risks associated with failing to establish proper data governance. Potential reputational and financial damages resulting from failed data …
Data Governance 101 - precisely.com
With today’s enterprises relying on big data analytics for business intelligence, implementing an effective data governance program is a top priority. Without data governance there are …
THE BENEFITS OF ARTIFICIAL INTELLIGENCE IN BUSINESS …
Deloitte can help organizations more easily embrace cloud data governance and cataloging by practicing three recommended behaviors: Advancements in AI, ML, and Generative AI …
Demystifying Data Governance - PwC
The objective of data governance is for an organisation to have greater control over its data assets. Data governance is achieved by planning, monitoring and enforcing stringent policies, …
Effective Data Governance - Infosys
Infosys holistic service offerings help next-generation organizations to implement effective data governance. The goal of data governance initiative is to manage data for delivering timely, …
Data Governance Suite - BigID
Reimagine your governance approach with BigID to lead with the data, and apply ML and deep data insight for scalable, efficient and accurate data governance. apps to take action. Data …
Clinical & Business Intelligence: Data Management – A …
The intended audience of this data governance module is executives, managers and data practitioners who are facing new or expanding data integration demands while trying to …
Business Intelligence for IT Governance of a Technology …
Abstract: Managers are required to make fast, reliable, and fact-based decisions to encompass the dynamicity of modern business environments. Data visualization and reporting are thus …
Enhancing Data Governance and Self-Service BI with Power BI
This white paper aims to highlight the key elements of the Power BI Governance framework, its significance in promoting a data-driven culture, and the importance of following unified usage …
Global and industry frameworks for data governance - PwC
A data governance framework encompasses every part of an organisation’s data management process, down to individual technologies, databases and data models. This article unveils …
Definitive Guide to Data Governance - Talend
• Improved quality of data: Data governance creates a plan that ensures data accuracy, completeness, and consistency • A data map: Data governance provides an advanced ability …
A Framework for Business Intelligence Data Governance
BI Data Governance is focused on the processes and practices that define how data is managed within a BI environment. The BI Data Governance Framework introduces a structured …
Enterprise Data Architecture and Data Governance: Use …
Operationalizing data governance leverages the practical aspects of the data architecture by enabling business analysts and their IT partners to layer business rules on top of the data …
TRANSFORMING DATA INTO ACTION: THE BUSINESS …
This report, Transforming data into action: the business outlook for data governance, explores the business contributions of data governance at organisations globally and across...
Building a Business Case for Data Governance - Bitpipe
In this white paper, readers will learn how to leverage a blend of processes, technology, and industry best practices to successfully make and sell a business case for data governance. …
The 2020 State of Data Governance and Automation
The 2020 State of Data Governance and Automation Many organizations have started their data governance journeys to achieve data intelligence, but they have not automated their data …
THE COMPREHENSIVE GUIDE TO DATA GOVERNANCE
Effective governance is the key to unlocking the value of data. Everyone understands that data can be an asset when exploited. It can have a direct impact on company profitability and top …
The DGI Data Governance Framework - neweditions.net
This paper describes core concepts, the components of the DGI Data Governance Framework, and typical steps in implementing a program. The DGI Data Governance Framework . …
Designing data governance that delivers value - McKinsey & …
Six critical practices are needed to ensure data governance creates value. 1. Secure top management’s attention. As the aforementioned example highlights, success with data …
Trends in Data Governance and Data Quality
and industries are turning to data governance to provide a strong framework that enables them to proactively find, understand, and manage data and realize business success. The survey …
Data Governance - The Institute of Internal Auditors or The IIA
Data governance best practices. Risks associated with failing to establish proper data governance. Potential reputational and financial damages resulting from failed data …
Data Governance 101 - precisely.com
With today’s enterprises relying on big data analytics for business intelligence, implementing an effective data governance program is a top priority. Without data governance there are …
THE BENEFITS OF ARTIFICIAL INTELLIGENCE IN …
Deloitte can help organizations more easily embrace cloud data governance and cataloging by practicing three recommended behaviors: Advancements in AI, ML, and Generative AI …
Demystifying Data Governance - PwC
The objective of data governance is for an organisation to have greater control over its data assets. Data governance is achieved by planning, monitoring and enforcing stringent policies, …
Effective Data Governance - Infosys
Infosys holistic service offerings help next-generation organizations to implement effective data governance. The goal of data governance initiative is to manage data for delivering timely, …
Data Governance Suite - BigID
Reimagine your governance approach with BigID to lead with the data, and apply ML and deep data insight for scalable, efficient and accurate data governance. apps to take action. Data …
Clinical & Business Intelligence: Data Management – A …
The intended audience of this data governance module is executives, managers and data practitioners who are facing new or expanding data integration demands while trying to …
Business Intelligence for IT Governance of a Technology …
Abstract: Managers are required to make fast, reliable, and fact-based decisions to encompass the dynamicity of modern business environments. Data visualization and reporting are thus …
Enhancing Data Governance and Self-Service BI with Power …
This white paper aims to highlight the key elements of the Power BI Governance framework, its significance in promoting a data-driven culture, and the importance of following unified usage …
Global and industry frameworks for data governance - PwC
A data governance framework encompasses every part of an organisation’s data management process, down to individual technologies, databases and data models. This article unveils …
