| data management strategy roadmap: The Self-Service Data Roadmap Sandeep Uttamchandani, 2020-09-10 Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can’t scale data science teams fast enough to keep up with the growing amounts of data to transform. What’s the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work. Build a self-service portal to support data discovery, quality, lineage, and governance Select the best approach for each self-service capability using open source cloud technologies Tailor self-service for the people, processes, and technology maturity of your data platform Implement capabilities to democratize data and reduce time to insight Scale your self-service portal to support a large number of users within your organization |
| data management strategy roadmap: 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 management strategy roadmap: Data Management courseware based on CDMP Fundamentals Alliance BV And More Group BV, 1970-01-01 Besides the courseware publication (ISBN: 9789401811491), you are advised to obtain the DAMA DMBOK publication (ISBN: 9781634622349). Optionally, you can use the publication Data management: a gentle introduction (ISBN: 9789401805506) as inspiration for examples and quotes about the field of data management. This material is intended to prepare participants for the CDMP exam by DAMA International. The courseware can only be ordered by partners and is based on the current version of the DAMA DMBOK. The material will be updated when new versions of DMBOK are published. DAMA DMBOK is the industry reference for data management. It is published by DAMA International and is currently in its second version. The DMBOK is developed by professionals and can be seen as a collection of best practices. The domain of data management is divided into functional areas which are discussed in terms of definitions (what is it), goals (what are we trying to achieve), steps (what are typical activities), inputs/outputs, and participating roles. Developing and sustaining an effective data management function is far from an easy task. The DMBOK framework is adopted by many organizations as the foundation for their data management function: standardized language and good practices speed up the learning process. After the training, you have an overview of the field of data management, its terminology, and current best practices. |
| data management strategy roadmap: Business Intelligence Roadmap Larissa Terpeluk Moss, S. Atre, 2003 This software will enable the user to learn about business intelligence roadmap. |
| data management strategy roadmap: A Practitioner's Guide to Data Governance Uma Gupta, San Cannon, 2020-07-08 Data governance looks simple on paper, but in reality it is a complex issue facing organizations. In this practical guide, data experts Uma Gupta and San Cannon look to demystify data governance through pragmatic advice based on real-world experience and cutting-edge academic research. |
| data management strategy roadmap: Data Strategy Sid Adelman, Larissa Terpeluk Moss, Majid Abai, 2005 Without a data strategy, the people within an organization have no guidelines for making decisions that are absolutely crucial to the success of the IT organization and to the entire organization. The absence of a strategy gives a blank check to those who want to pursue their own agendas, including those who want to try new database management systems, new technologies (often unproven), and new tools. This type of environment provides no hope for success. Data Strategy should result in the development of systems with less risk, higher quality systems, and reusability of assets. This is key to keeping cost and maintenance down, thus running lean and mean. Data Strategy provides a CIO with a rationale to counter arguments for immature technology and data strategies that are inconsistent with existing strategies. This book uses case studies and best practices to give the reader the tools they need to create the best strategy for the organization. |
| data management strategy roadmap: 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 management strategy roadmap: Data Strategy Bernard Marr, 2017-04-03 BRONZE RUNNER UP: Axiom Awards 2018 - Business Technology Category Less than 0.5 per cent of all data is currently analyzed and used. However, business leaders and managers cannot afford to be unconcerned or sceptical about data. Data is revolutionizing the way we work and it is the companies that view data as a strategic asset that will survive and thrive. Data Strategy is a must-have guide to creating a robust data strategy. Explaining how to identify your strategic data needs, what methods to use to collect the data and, most importantly, how to translate your data into organizational insights for improved business decision-making and performance, this is essential reading for anyone aiming to leverage the value of their business data and gain competitive advantage. Packed with case studies and real-world examples, advice on how to build data competencies in an organization and crucial coverage of how to ensure your data doesn't become a liability, Data Strategy will equip any organization with the tools and strategies it needs to profit from Big Data, analytics and the Internet of Things (IoT). |
| data management strategy roadmap: Implementing Data-Driven Strategies in Smart Cities Didier Grimaldi, Carlos Carrasco-Farré, 2021-09-18 Implementing Data-Driven Strategies in Smart Cities is a guidebook and roadmap for practitioners seeking to operationalize data-driven urban interventions. The book opens by exploring the revolution that big data, data science, and the Internet of Things are making feasible for the city. It explores alternate topologies, typologies, and approaches to operationalize data science in cities, drawn from global examples including top-down, bottom-up, greenfield, brownfield, issue-based, and data-driven. It channels and expands on the classic data science model for data-driven urban interventions – data capture, data quality, cleansing and curation, data analysis, visualization and modeling, and data governance, privacy, and confidentiality. Throughout, illustrative case studies demonstrate successes realized in such diverse cities as Barcelona, Cologne, Manila, Miami, New York, Nancy, Nice, São Paulo, Seoul, Singapore, Stockholm, and Zurich. Given the heavy emphasis on global case studies, this work is particularly suitable for any urban manager, policymaker, or practitioner responsible for delivering technological services for the public sector from sectors as diverse as energy, transportation, pollution, and waste management. - Explores numerous specific urban interventions drawn from global case studies, helping readers understand real urban challenges and create data-driven solutions - Provides a step-by-step and applied holistic guide and methodology for immediate application in the reader's own business agenda - Presents cutting edge technology presentation with coverage of innovations such as the Internet of Things, robotics, 5G, edge/fog computing, blockchain, intelligent transport systems, and connected-automated mobility |
| data management strategy roadmap: New Horizons for a Data-Driven Economy José María Cavanillas, Edward Curry, Wolfgang Wahlster, 2016-04-04 In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment. |
| data management strategy roadmap: 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 management strategy roadmap: 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 management strategy roadmap: Enterprise Master Data Management Allen Dreibelbis, Eberhard Hechler, Ivan Milman, Martin Oberhofer, Paul van Run, Dan Wolfson, 2008-06-05 The Only Complete Technical Primer for MDM Planners, Architects, and Implementers Companies moving toward flexible SOA architectures often face difficult information management and integration challenges. The master data they rely on is often stored and managed in ways that are redundant, inconsistent, inaccessible, non-standardized, and poorly governed. Using Master Data Management (MDM), organizations can regain control of their master data, improve corresponding business processes, and maximize its value in SOA environments. Enterprise Master Data Management provides an authoritative, vendor-independent MDM technical reference for practitioners: architects, technical analysts, consultants, solution designers, and senior IT decisionmakers. Written by the IBM ® data management innovators who are pioneering MDM, this book systematically introduces MDM’s key concepts and technical themes, explains its business case, and illuminates how it interrelates with and enables SOA. Drawing on their experience with cutting-edge projects, the authors introduce MDM patterns, blueprints, solutions, and best practices published nowhere else—everything you need to establish a consistent, manageable set of master data, and use it for competitive advantage. Coverage includes How MDM and SOA complement each other Using the MDM Reference Architecture to position and design MDM solutions within an enterprise Assessing the value and risks to master data and applying the right security controls Using PIM-MDM and CDI-MDM Solution Blueprints to address industry-specific information management challenges Explaining MDM patterns as enablers to accelerate consistent MDM deployments Incorporating MDM solutions into existing IT landscapes via MDM Integration Blueprints Leveraging master data as an enterprise asset—bringing people, processes, and technology together with MDM and data governance Best practices in MDM deployment, including data warehouse and SAP integration |
| data management strategy roadmap: IT STRATEGY AND MANAGEMENT, FOURTH EDITION DUBEY, SANJIVA SHANKAR, 2018-08-01 Businesses are becoming increasingly global, so they need a well-orchestrated IT management strategy to meet the increasing customer expectations and international competition. This concise yet comprehensive edition is designed to prepare students with IT strategy, planning and management with latest management frameworks, researched principles and proven best practices. Besides giving an in-depth study of managing IT as a strategic resource, the book also explains how to prepare an effective plan for implementing IT strategy. Further, it covers the complete lifecycle of IT management encompassing IT projects and program management, IT service management, planning and measuring returns from IT investment, and management of IT-led change in the organization. In addition, it deals with the topics of modern interest such as computer ethics, IPR management, and Indian cyber laws. NEW TO THE EDITION Includes three new chapters on ‘Business Model Strategies’, ‘Business Process Reengineering and ERP’, and ‘Big Data Analytics Strategy’. Several case studies in the Indian context to give a practical under-standing of the subject for the readers. MCQs to help students to test their knowledge. TARGET AUDIENCE • B. Tech (Computer Science) • B.Tech (IT) • M.Sc. (IT) • MBA (PGDM) |
| data management strategy roadmap: The "Orange" Model of Data Management Irina Steenbeek, 2019-10-21 *This book is a brief overview of the model and has only 24 pages.*Almost every data management professional, at some point in their career, has come across the following crucial questions:1. Which industry reference model should I use for the implementation of data managementfunctions?2. What are the key data management capabilities that are feasible and applicable to my company?3. How do I measure the maturity of the data management functions and compare that withthose of my peers in the industry4. What are the critical, logical steps in the implementation of data management?The Orange (meta)model of data management provides a collection of techniques and templates for the practical set up of data management through the design and implementation of the data and information value chain, enabled by a set of data management capabilities.This book is a toolkit for advanced data management professionals and consultants thatare involved in the data management function implementation.This book works together with the earlier published The Data Management Toolkit. The Orange model assists in specifying the feasible scope of data management capabilities, that fits company's business goals and resources. The Data Management Toolkit is a practical implementation guide of the chosen data management capabilities. |
| data management strategy roadmap: 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 management strategy roadmap: From Business Strategy to Information Technology Roadmap Tiffany Pham, David K. Pham, Andrew Pham, 2018-09-03 Whether you are a CEO, CFO, board member, or an IT executive, From Business Strategy to Information Technology Roadmap: A Practical Guide for Executives and Board Members lays out a practical, how-to approach to identifying business strategies and creating value-driven technology roadmaps in your organization. Unlike many other books on the subject, you will not find theories or grandiose ideas here. This book uses numerous examples, illustrations, and case studies to show you how to solve the real-world problems that business executives and technology leaders face on a day-to-day basis. Filled with actionable advice you can use immediately, the authors introduce Agile and the Lean mindset in a manner that the people in your business and technology departments can easily understand. Ideal for executives in both the commercial and nonprofit sectors, it includes two case studies: one about a commercial family business that thrived to become a multi-million-dollar company and the other about a nonprofit association based in New York City that fights against child illiteracy. |
| data management strategy roadmap: Data Management at Scale Piethein Strengholt, 2020-07-29 As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata |
| data management strategy roadmap: Marketing Analytics Roadmap Jerry Rackley, 2015-05-30 Many managers view marketing as a creative endeavor, not something that is measurable or manageable by numbers. But today’s leaders in the C-suite demand greater accountability. They want to know that they are getting a return on their marketing investment. And to get that ROI number, you need analytics. This expectation is intimidating for the many sales and marketing managers who rely on marketing instincts, not metrics, to do their work. But Marketing Analytics Roadmap: Methods, Metrics, and Tools demonstrates that employing analytics isn't just a way to keep the CEO off your back. It improves marketing results and ensures marketers a seat at the table where big decisions get made. In this book, analytics expert Jerry Rackley shows you how to understand and implement a sound marketing analytics process that helps eliminate the guesswork about the results produced by your marketing efforts. The result? You will acquire—and keep—more customers. Even better, you'll find that an analytics process helps the entire organization make better decisions, and not just marketers. Marketing Analytics Roadmap explains: How to use analytics to create marketing and sales metrics that guide your actions and provide valuable feedback on your efforts How to structure and use dashboards to report marketing results How to put industry-leading analytics software and other tools to good use How Big Data is shaping the marketing analytics landscape Sales and marketing teams that master marketing analytics will find them a powerful servant that enables agility, raises effectiveness, and creates confidence. Marketing Analytics Roadmap shows you how to build a well-planned and executed marketing analytics strategy that will enhance the credibility of your marketing team and help you not only get a seat at the big-decisions table, but keep it once there. |
| data management strategy roadmap: Patterns of Data Modeling Michael Blaha, 2010-06-01 Best-selling author and database expert with more than 25 years of experience modeling application and enterprise data, Dr. Michael Blaha provides tried and tested data model patterns, to help readers avoid common modeling mistakes and unnecessary frustration on their way to building effective data models. Unlike the typical methodology book, Patterns of Data Modeling provides advanced techniques for those who have mastered the basics. Recognizing that database representation sets the path for software, determines its flexibility, affects its quality, and influences whether it succeeds or fails, the text focuses on databases rather than programming. It is one of the first books to apply the popular patterns perspective to database systems and data models. It offers practical advice on the core aspects of applications and provides authoritative coverage of mathematical templates, antipatterns, archetypes, identity, canonical models, and relational database design. |
| data management strategy roadmap: 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 management strategy roadmap: The Analytics Lifecycle Toolkit Gregory S. Nelson, 2018-04-03 Data has become the new currency; organizations are drowning in it, but few are cashing in on its true value. The Analytics Lifecycle Toolkit translates the entire analytics lifecycle into actionable insights, providing a framework for building an effective analytics capability and the processes that turn data into action. Part 1 describes the “who,” “how,” and “why” of modern enterprise analytics, giving leaders clear insight into the value of strategically-aligned capabilities. Part 2 details best practices that include problem framing, data sensemaking, model development, change management, data management, product management, and more. Part 3 rounds out the discussion by providing guidance on sustaining high performance and guiding the analytics function into new phases of business. For organizations who see the value of analytics but lack the depth of knowledge needed to structure appropriate solutions, this book breaks the cycle of frustration and provides a roadmap for putting the right people, processes, and technologies into place. For those who have already implemented analytics, this book serves as a reference for leadership and a “refresher course” to update the team on the latest in practices and processes. Rather than a simple catalogue of analytics models, the discussion emphasizes underlying principles in key process areas to help organizations build analytics capabilities tailored to their specific needs—allowing them to harvest the highest-value information to better inform strategic decisions. In line with the book’s practical focus, the companion website provides downloadable resources, tools, videos, and more to support and streamline implementation. The discussion itself assumes no prior knowledge of analytics and explicitly clarifies complex concepts and terms, using real-world examples to illustrate what effective practice looks like on the ground. With clear guidance, expert insight, and a wealth of practical tools, The Analytics Lifecycle Toolkit is an essential resource for any organization seeking an optimized analytics program. |
| data management strategy roadmap: Strategize: Product Strategy and Product Roadmap Practices for the Digital Age Roman Pichler, 2022-09-07 Create a winning game plan for your digital products with Strategize: Product Strategy and Product Roadmap Practices for the Digital Age, 2nd edition. Using a wide range of proven techniques and tools, product management expert Roman Pichler explains how to create a winning product strategy and actionable roadmap. Comprehensive and insightful, the book will enable you to make the right strategic decisions in today’s dynamic digital age. If you work as a product manager, Scrum product owner, product portfolio manager, head of product, or product coach, then this book is for you. What you will learn: * Create an inspiring vision for your product. * Develop a product strategy that maximises the chances of launching a winning product. * Successfully adapt the strategy across the product life cycle to achieve sustained product success. * Measure the value your product creates using the right key performance indicators (KPIs). * Build an actionable outcome-based product roadmap that aligns stakeholders and directs the product backlog. * Regularly review the product strategy and roadmap and keep them up-to-date. Written in an engaging and easily accessible style, Strategize offers practical advice and valuable examples so that you can apply the practices directly to your products. This second, revised, and extended edition offers new concepts, more tools, and additional tips and examples. Praise for Strategize: Strategize offers a comprehensive approach to product strategy using the latest practices geared specifically to digital products. Not just theory, the book is chock-full of real-world examples, making it easier to apply the principles to your company and products. Strategize is essential reading for everyone in charge of products: product executives, product managers, and product owners. Steve Johnson, Founder at Under10 Consulting. Whether you are new to product management or an experienced practitioner, Strategize is a must read. You are guaranteed to get new ideas about how to develop or improve your product strategy and how to execute it successfully. It’s an essential addition to every product manager’s reading list. Marc Abraham, Senior Group Product Manager at Intercom. |
| data management strategy roadmap: 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 management strategy roadmap: Data Strategy: From Insight to Impact Subbarao Pothineni, 2022-04-19 Data Strategy: From Insight to Impact is a comprehensive guide to help businesses understand and develop a successful data strategy. Starting with a look at the evolution of data and its impact on the business world, this book covers all the key elements of a successful data strategy. From understanding data and analytics, developing a data strategy, and data collection and management, to data-driven decision-making, data-driven business transformation, and data governance and compliance. |
| data management strategy roadmap: Data Governance Ismael Caballero, Mario Piattini, 2024-01-28 This 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 management strategy roadmap: Data Management Fundamentals (DMF) - CDMP exam preparation Paul Rakké, 1970-01-01 Besides this Data Management Fundamentals (DMF) CDMP exam preparation book, you are advised to obtain the publication the Data Management courseware based on CDMP Fundamentals - Revised edition (ISBN: 9789401811491) for your preparation for your Certified Data Management Professional (CDMP) certification. This CDMP certification based on the DAMA DMBok (Data Management Body of Knowledge) is a globally recognized credential that validates the knowledge and skills required in the field of data management.This exam preparation book is a well-balanced guide to help you pass the CDMP exam and earn your certification. All the knowledge areas as described in the related courseware and/or DAMA-DMBOK (2nd edition) of the well-known study book plus extra topics as described in the book too, will be treated with exam-like questions. The number of questions per topic can differ, depending on the weights as used in the formal exam composition. All the questions are newly defined questions by the author. Separately the correct answers and guiding explanations with references to the DAMA-DMBOK book are provided. Besides the set of questions per topic which consist of a set of 140 questions, also a set of 100 extra questions with the same weights per topic is provided to give you the opportunity to prepare yourself on the exam with this similar exam. So this 240 new questions provided in this book make your road to the CDMP certification complete. |
| data management strategy roadmap: Practitioner's Guide to Operationalizing Data Governance Mary Anne Hopper, 2023-05-09 Discover what does—and doesn’t—work when designing and building a data governance program In A Practitioner’s Guide to Operationalizing Data Governance, veteran SAS and data management expert Mary Anne Hopper walks readers through the planning, design, operationalization, and maintenance of an effective data governance program. She explores the most common challenges organizations face during and after program development and offers sound, hands-on advice to meet tackle those problems head-on. Ideal for companies trying to resolve a wide variety of issues around data governance, this book: Offers a straightforward starting point for companies just beginning to think about data governance Provides solutions when company employees and leaders don’t—for whatever reason—trust the data the company has Suggests proven strategies for getting a data governance program that’s gone off the rails back on track Complete with visual examples based in real-world case studies, A Practitioner’s Guide to Operationalizing Data Governance will earn a place in the libraries of information technology executives and managers, data professionals, and project managers seeking a one-stop resource to help them deliver practical data governance solutions. |
| data management strategy roadmap: Data Governance Success Rupa Mahanti, 2021-12-13 While good data is an enterprise asset, bad data is an enterprise liability. Data governance enables you to effectively and proactively manage data assets throughout the enterprise by providing guidance in the form of policies, standards, processes and rules and defining roles and responsibilities outlining who will do what, with respect to data. While implementing data governance is not rocket science, it is not a simple exercise. There is a lot confusion around what data governance is, and a lot of challenges in the implementation of data governance. Data governance is not a project or a one-off exercise but a journey that involves a significant amount of effort, time and investment and cultural change and a number of factors to take into consideration to achieve and sustain data governance success. Data Governance Success: Growing and Sustaining Data Governance is the third and final book in the Data Governance series and discusses the following: • Data governance perceptions and challenges • Key considerations when implementing data governance to achieve and sustain success• Strategy and data governance• Different data governance maturity frameworks• Data governance – people and process elements• Data governance metrics This book shares the combined knowledge related to data and data governance that the author has gained over the years of working in different industrial and research programs and projects associated with data, processes, and technologies and unique perspectives of Thought Leaders and Data Experts through Interviews conducted. This book will be highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge to support and succeed in data governance implementations. This book is technology agnostic and contains a balance of concepts and examples and illustrations making it easy for the readers to understand and relate to their own specific data projects. |
| data management strategy roadmap: Data Management for Researchers Kristin Briney, 2015-09-01 A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management. Data Management for Researchers includes sections on: * The data problem – an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code. * The data lifecycle – a framework for data’s place within the research process and how data’s role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data. * Planning for data management – covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. * Documenting your data – an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. * Organizing your data – explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. * Improving data analysis – covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data – many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage – deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data – digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data – addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data – as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it. This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation. An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline. —Robert Buntrock, Chemical Information Bulletin |
| data management strategy roadmap: Financial Services and General Government Appropriations for 2011, Part 1, 111-2 Hearings , 2010 |
| data management strategy roadmap: 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 management strategy roadmap: Product Roadmaps Relaunched C. Todd Lombardo, Bruce McCarthy, Evan Ryan, Michael Connors, 2017-10-25 A good product roadmap is one of the most important and influential documents an organization can develop, publish, and continuously update. In fact, this one document can steer an entire organization when it comes to delivering on company strategy. This practical guide teaches you how to create an effective product roadmap, and demonstrates how to use the roadmap to align stakeholders and prioritize ideas and requests. With it, you’ll learn to communicate how your products will make your customers and organization successful. Whether you're a product manager, product owner, business analyst, program manager, project manager, scrum master, lead developer, designer, development manager, entrepreneur, or business owner, this book will show you how to: Articulate an inspiring vision and goals for your product Prioritize ruthlessly and scientifically Protect against pursuing seemingly good ideas without evaluation and prioritization Ensure alignment with stakeholders Inspire loyalty and over-delivery from your team Get your sales team working with you instead of against you Bring a user and buyer-centric approach to planning and decision-making Anticipate opportunities and stay ahead of the game Publish a comprehensive roadmap without overcommitting |
| data management strategy roadmap: Think Bigger Mark Van Rijmenam, 2014-04-03 Offering real-world insight and explanations, this book provides a roadmap for organizations looking to develop a profitable big data strategy and reveals why it's not something they can leave to the I.T. department. Big data--the enormous amount of data that is created as virtually every movement, transaction, and choice we make becomes digitized--is revolutionizing business. Sharing best practices from companies that have implemented a big data strategy including Walmart, InterContinental Hotel Group, Walt Disney, and Shell, this helpful resource covers the most important big data trends affecting organizations, as well as key technologies like Hadoop and MapReduce, and several crucial types of analyses. In Think Bigger, you will find guidance on topics such as: how to ensure security, respecting the privacy rights of consumers, and how big data is impacting specific industries--and where opportunities can be found. Big data is changing the way businesses--and even governments--are operated and managed. Think Bigger is an essential resource for anyone who wants to ensure that their company isn't left in the dust. |
| data management strategy roadmap: Roadmap to Successful Digital Health Ecosystems Evelyn Hovenga, Heather Grain, 2022-02-12 Roadmap to Successful Digital Health Ecosystems: A Global Perspective presents evidence-based solutions found on adopting open platforms, standard information models, technology neutral data repositories, and computable clinical data and knowledge (ontologies, terminologies, content models, process models, and guidelines), resulting in improved patient, organizational, and global health outcomes. The book helps engaging countries and stakeholders take action and commit to a digital health strategy, create a global environment and processes that will facilitate and induce collaboration, develop processes for monitoring and evaluating national digital health strategies, and enable learnings to be shared in support of WHO's global strategy for digital health. The book explains different perspectives and local environments for digital health implementation, including data/information and technology governance, secondary data use, need for effective data interpretation, costly adverse events, models of care, HR management, workforce planning, system connectivity, data sharing and linking, small and big data, change management, and future vision. All proposed solutions are based on real-world scientific, social, and political evidence. - Provides a roadmap, based on examples already in place, to develop and implement digital health systems on a large-scale that are easily reproducible in different environments - Addresses World Health Organization (WHO)-identified research gaps associated with the feasibility and effectiveness of various digital health interventions - Helps readers improve future decision-making within a digital environment by detailing insights into the complexities of the health system - Presents evidence from real-world case studies from multiple countries to discuss new skills that suit new paradigms |
| data management strategy roadmap: Data Governance Dimitrios Sargiotis, |
| data management strategy roadmap: Delivering Research Data Management Services Graham Pryor, Sarah Jones, Angus Whyte, 2013-12-10 Step-by-step guidance to setting up and running effective institutional research data management services to support researchers and networks. The research landscape is changing, with key global research funders now requiring institutions to demonstrate how they will preserve and share research data. However, the practice of structured research data management is very new, and the construction of services remains experimental and in need of models and standards of approach. This groundbreaking guide will lead researchers, institutions and policy makers through the processes needed to set up and run effective institutional research data management services. This ‘how to’ guide provides a step-by-step explanation of the components for an institutional service. Case studies from the newly emerging service infrastructures in the UK, USA and Australia draw out the lessons learnt. Different approaches are highlighted and compared; for example, a researcher-focused strategy from Australia is contrasted with a national, top-down approach, and a national research data management service is discussed as an alternative to institutional services. Key topics covered: • Research data provision • Options and approaches to research data management service provision • A spectrum of roles, responsibilities and competences • A pathway to sustainable research data services: from scoping to sustainability • The range and components of RDM infrastructure and services Case studies: • Johns Hopkins University • University of Southampton • Monash University • The UK Data Service • Jisc Managing Research Data programmes. Readership: This book will be an invaluable guide to those entering a new and untried enterprise. It will be particularly relevant to heads of libraries, information technology managers, research support office staff and research directors planning for these types of services. It will also be of interest to researchers, funders and policy makers as a reference tool for understanding how shifts in policy will have a range of ramifications within institutions. Library and information science students will find it an informative window on an emerging area of practice. |
| data management strategy roadmap: Financial Services and General Government Appropriations for 2011 United States. Congress. House. Committee on Appropriations. Subcommittee on Financial Services and General Government, 2010 |
| data management strategy roadmap: The Elements of Big Data Value Edward Curry, Andreas Metzger, Sonja Zillner, Jean-Christophe Pazzaglia, Ana García Robles, 2021-08-01 This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation. |
| data management strategy roadmap: Analytics Phil Simon, 2017-07-03 For years, organizations have struggled to make sense out of their data. IT projects designed to provide employees with dashboards, KPIs, and business-intelligence tools often take a year or more to reach the finish line...if they get there at all. This has always been a problem. Today, though, it's downright unacceptable. The world changes faster than ever. Speed has never been more important. By adhering to antiquated methods, firms lose the ability to see nascent trends—and act upon them until it's too late. But what if the process of turning raw data into meaningful insights didn't have to be so painful, time-consuming, and frustrating? What if there were a better way to do analytics? Fortunately, you're in luck... Analytics: The Agile Way is the eighth book from award-winning author and Arizona State University professor Phil Simon. Analytics: The Agile Way demonstrates how progressive organizations such as Google, Nextdoor, and others approach analytics in a fundamentally different way. They are applying the same Agile techniques that software developers have employed for years. They have replaced large batches in favor of smaller ones...and their results will astonish you. Through a series of case studies and examples, Analytics: The Agile Way demonstrates the benefits of this new analytics mind-set: superior access to information, quicker insights, and the ability to spot trends far ahead of your competitors. |
OCDO Data Strategy FY 2022-2026 - SEC.gov
Establish and apply data and data architecture standards consistently across all data assets to facilitate work across data assets, reduce data siloes, enhance collaboration, facilitate …
Data Management Strategy 2019-2022 - City of Dallas
The COD data management strategy (DMS) is the first step toward enabling such a plan and increasing the City’s Analytics IQ. A DMS ensures that all data initiatives follow a common …
Master Data Management Strategy: Best Practices - GHX
Here we provide a roadmap to a successful master data management strategy, featuring best practices from four organizations that have implemented a strategy and are reaping the …
Data Strategy and Roadmap - Centric Consulting
The Centric approach results in a business-aligned and execution-oriented roadmap across people, process, information, structure, metrics and technology to achieve your strategic …
Data Management Strategy (DMS) - Arkansas Department of …
To complete the DMS, as well as the MITA Information Assessment and Technical Assessment, NTT Data met with various State personnel to better understand the current data management …
Deloitte Strategy Framework DataStrata Value Proposition
DataStrata is our unique approach to creating and managing a data strategy based on an organization's specific business, risks and capabilities. Our framework incorporates a proven …
REPORT TO THE CLERK OF THE PRIVY COUNCIL: A DATA …
To this end, the Clerk asked us in January 2018 to develop a Data Strategy. The strategy is intended to position the public service to provide the best possible advice
DATA MANAGEMENT STRATEGY AND IMPLEMENTATION …
IPPF’s Data Management Strategy will serve as a statement of principles for data management across the Federation, and a guide to action so we can achieve our goals.
Data Management Strategy Workplan - International Seabed …
Create data synergies that complement existing data and integrate multiple data sources with DeepData Outcome 3. The capacity of developing States to use the best available deep -sea …
USask Research Data Management Strategy and Roadmap
Research data management (RDM) refers to the collection, documentation, storage, sharing, and preservation of research data throughout the lifecycle of a research project [1]. Good RDM …
2024 2025 2026 2027 2029 2030+ - IBM
Business innovation driven by generative AI is fueled by open data stores, formats, and engines; a product-oriented data fabric; and the infusion of AI at all levels to radically improve data …
Data Strategy for the U.S. Department of Justice
Dec 30, 2022 · The purpose of the DOJ Data Strategy is to promote awareness, use, and reuse of DOJ data assets to the maximum extent possible, without superseding the DOJ component …
EMA SPOR master data management roadmap - European …
This report outlines an EMA multi-year programme which defines a Master Data Management (MDM)1 strategy for the use of medicinal product data specifically related to Substance, …
Anatomy of a Data Strategy - Ortecha
To define data-related strategic actions, consider these four areas: . •People– Become a data-driven organization. •Process– Build sustainable data management capability . •Data– …
IBM Master Data Management strategy
IBM has developed a Multiform MDM strategy supported by a product portfolio that allows organizations to meet short-term tactical MDM needs, while providing a roadmap for growth in …
Building a Data Strategy: Practical Steps for Aligning
• Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace, from digital transformation to marketing, customer centricity, …
A Ninety-Day Plan to Build a Data and Digital Strategy
To speed the process, we identified 10 to 15 foun-dational use cases that companies must implement to contend in their industry. In fashion e-commerce, for exam-ple, the basic use …
Department of Commerce Data Strategic Action Plan
The Commerce Data Strategy, published in August 2021, describes the vision, scope, goals, and objectives for establishing a foundation to effectively manage, share, and maximize the value …
Federal Data Strategy Data Governance Playbook
The Federal Data Strategy (hereinafter “Strategy”) supports a coordinated approach to federal data leadership, including data use and management, to help agencies deliver on mission in …
A comparative perspective on data strategies - PwC
deeper look at the data strategy of companies across industries to find out which key factors drive the data journey, which decisions are crucial for developing and implementing a company …
OCDO Data Strategy FY 2022-2026 - SEC.gov
Establish and apply data and data architecture standards consistently across all data assets to facilitate work across data assets, reduce data siloes, enhance collaboration, facilitate …
Data Management Strategy 2019-2022 - City of Dallas
The COD data management strategy (DMS) is the first step toward enabling such a plan and increasing the City’s Analytics IQ. A DMS ensures that all data initiatives follow a common …
Master Data Management Strategy: Best Practices - GHX
Here we provide a roadmap to a successful master data management strategy, featuring best practices from four organizations that have implemented a strategy and are reaping the …
Data Strategy and Roadmap - Centric Consulting
The Centric approach results in a business-aligned and execution-oriented roadmap across people, process, information, structure, metrics and technology to achieve your strategic …
Data Management Strategy (DMS) - Arkansas Department …
To complete the DMS, as well as the MITA Information Assessment and Technical Assessment, NTT Data met with various State personnel to better understand the current data management …
Deloitte Strategy Framework DataStrata Value Proposition
DataStrata is our unique approach to creating and managing a data strategy based on an organization's specific business, risks and capabilities. Our framework incorporates a proven …
REPORT TO THE CLERK OF THE PRIVY COUNCIL: A DATA …
To this end, the Clerk asked us in January 2018 to develop a Data Strategy. The strategy is intended to position the public service to provide the best possible advice
DATA MANAGEMENT STRATEGY AND IMPLEMENTATION …
IPPF’s Data Management Strategy will serve as a statement of principles for data management across the Federation, and a guide to action so we can achieve our goals.
Data Management Strategy Workplan - International …
Create data synergies that complement existing data and integrate multiple data sources with DeepData Outcome 3. The capacity of developing States to use the best available deep -sea …
USask Research Data Management Strategy and Roadmap
Research data management (RDM) refers to the collection, documentation, storage, sharing, and preservation of research data throughout the lifecycle of a research project [1]. Good RDM …
2024 2025 2026 2027 2029 2030+ - IBM
Business innovation driven by generative AI is fueled by open data stores, formats, and engines; a product-oriented data fabric; and the infusion of AI at all levels to radically improve data …
Data Strategy for the U.S. Department of Justice
Dec 30, 2022 · The purpose of the DOJ Data Strategy is to promote awareness, use, and reuse of DOJ data assets to the maximum extent possible, without superseding the DOJ component …
EMA SPOR master data management roadmap - European …
This report outlines an EMA multi-year programme which defines a Master Data Management (MDM)1 strategy for the use of medicinal product data specifically related to Substance, …
Anatomy of a Data Strategy - Ortecha
To define data-related strategic actions, consider these four areas: . •People– Become a data-driven organization. •Process– Build sustainable data management capability . •Data– …
IBM Master Data Management strategy
IBM has developed a Multiform MDM strategy supported by a product portfolio that allows organizations to meet short-term tactical MDM needs, while providing a roadmap for growth in …
Building a Data Strategy: Practical Steps for Aligning
• Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace, from digital transformation to marketing, customer centricity, …
A Ninety-Day Plan to Build a Data and Digital Strategy
To speed the process, we identified 10 to 15 foun-dational use cases that companies must implement to contend in their industry. In fashion e-commerce, for exam-ple, the basic use …
Department of Commerce Data Strategic Action Plan
The Commerce Data Strategy, published in August 2021, describes the vision, scope, goals, and objectives for establishing a foundation to effectively manage, share, and maximize the value …
Federal Data Strategy Data Governance Playbook
The Federal Data Strategy (hereinafter “Strategy”) supports a coordinated approach to federal data leadership, including data use and management, to help agencies deliver on mission in …
A comparative perspective on data strategies - PwC
deeper look at the data strategy of companies across industries to find out which key factors drive the data journey, which decisions are crucial for developing and implementing a company …
