| data science maturity model: Diverse Applications and Transferability of Maturity Models Katuu, Shadrack, 2018-10-19 Previously, professionals had to make judgment calls based on subjective criteria, including their own acumen, in their decision making. In order to combat this subjectivity, maturity models can be implemented to allow organizations a means of assessing everyday processes and to offer a path towards advancement using transparent objective criteria. Diverse Applications and Transferability of Maturity Models is a pivotal reference source that provides vital research on the application of maturity models in organizational development in a variety of work environments. While highlighting topics such as open government, archives and records management, enterprise content management, and digital economy, this publication explores methods to help organizations effectively implement plans in any given management system. This book is ideally designed for professionals and researchers seeking current research on a variety of social science and applied science fields including business studies, computer science, digital preservation, information governance, information science, information systems, public administration, records management, and project management. |
| data science maturity model: Analytics at Work Thomas H. Davenport, Jeanne G. Harris, Robert Morison, 2010 As a follow-up to the successful Competing on Analytics, authors Tom Davenport, Jeanne Harris, and Robert Morison provide practical frameworks and tools for all companies that want to use analytics as a basis for more effective and more profitable decision making. Regardless of your company's strategy, and whether or not analytics are your company's primary source of competitive differentiation, this book is designed to help you assess your organization's analytical capabilities, provide the tools to build these capabilities, and put analytics to work. The book helps you answer these pressing questions: What assets do I need in place in my organization in order to use analytics to run my business? Once I have these assets, how do I deploy them to get the most from an analytic approach? How do I get an analytic initiative off the ground in the first place, and then how do I sustain analytics in my organization over time? Packed with tools, frameworks, and all new examples, Analytics at Work makes analytics understandable and accessible and teaches you how to make your company more analytical. |
| data science maturity model: 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 science maturity model: 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 science maturity model: Business Process Maturity Amy Van Looy, 2014-01-27 Organisations face many challenges, which induce them to perform better, and thus to establish mature (or excellent) business processes. As they now face globalisation, higher competitiveness, demanding customers, growing IT possibilities, compliancy rules etc., business process maturity models (BPMMs) have been introduced to help organisations gradually assess and improve their business processes (e.g. CMMI or OMG-BPMM). In fact, there are now so many BPMMs to choose from that organisations risk selecting one that does not fit their needs or one of substandard quality. This book presents a study that distinguishes process management from process orientation so as to arrive at a common understanding. It also includes a classification study to identify the capability areas and maturity types of 69 existing BPMMs, in order to strengthen the basis of available BPMMs. Lastly it presents a selection study to identify criteria for choosing one BPMM from the broad selection, which produced a free online selection tool, BPMM Smart-Selector. |
| data science maturity model: Data Science Thinking Longbing Cao, 2018-08-17 This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective. |
| data science maturity model: Competing on Analytics Thomas H. Davenport, Jeanne G. Harris, 2007-03-06 You have more information at hand about your business environment than ever before. But are you using it to “out-think” your rivals? If not, you may be missing out on a potent competitive tool. In Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling. Exemplars of analytics are using new tools to identify their most profitable customers and offer them the right price, to accelerate product innovation, to optimize supply chains, and to identify the true drivers of financial performance. A wealth of examples—from organizations as diverse as Amazon, Barclay’s, Capital One, Harrah’s, Procter & Gamble, Wachovia, and the Boston Red Sox—illuminate how to leverage the power of analytics. |
| data science maturity model: Information Management William McKnight, 2013-11-30 Information Management: Gaining a Competitive Advantage with Data is about making smart decisions to make the most of company information. Expert author William McKnight develops the value proposition for information in the enterprise and succinctly outlines the numerous forms of data storage. Information Management will enlighten you, challenge your preconceived notions, and help activate information in the enterprise. Get the big picture on managing data so that your team can make smart decisions by understanding how everything from workload allocation to data stores fits together. The practical, hands-on guidance in this book includes: - Part 1: The importance of information management and analytics to business, and how data warehouses are used - Part 2: The technologies and data that advance an organization, and extend data warehouses and related functionality - Part 3: Big Data and NoSQL, and how technologies like Hadoop enable management of new forms of data - Part 4: Pulls it all together, while addressing topics of agile development, modern business intelligence, and organizational change management Read the book cover-to-cover, or keep it within reach for a quick and useful resource. Either way, this book will enable you to master all of the possibilities for data or the broadest view across the enterprise. - Balances business and technology, with non-product-specific technical detail - Shows how to leverage data to deliver ROI for a business - Engaging and approachable, with practical advice on the pros and cons of each domain, so that you learn how information fits together into a complete architecture - Provides a path for the data warehouse professional into the new normal of heterogeneity, including NoSQL solutions |
| data science maturity model: Data Science – Analytics and Applications Peter Haber, Thomas J. Lampoltshammer, Helmut Leopold, Manfred Mayr, 2022-03-29 Organizations have moved already from the rigid structure of classical project management towards the adoption of agile approaches. This holds also true for software development projects, which need to be flexible to adopt to rapid requests of clients as well to reflect changes that are required due to architectural design decisions. With data science having established itself as corner stone within organizations and businesses, it is now imperative to perform this crucial step for analytical business processes as well. The non-deterministic nature of data science and its inherent analytical tasks require an interactive approach towards an evolutionary step-by-step development to realize core essential business applications and use cases. The 4th International Data Science Conference (iDSC) 2021 brought together researchers, scientists, and business experts to discuss means of establishing new ways of embracing agile approaches within the various domains of data science, such as machine learning and AI, data mining, or visualization and communication as well as case studies and best practices from leading research institutions and business companies. The proceedings include all full papers presented in the scientific track and the corresponding German abstracts as well as the short papers from the student track. Among the topics of interest are: Artificial Intelligence and Machine Learning Implementation of data mining processes Agile Data Science and Visualization Case Studies and Applications for Agile Data Science --- Organisationen sind bereits von der starren Struktur des klassischen Projektmanagements zu agilen Ansätzen übergegangen. Dies gilt auch für Softwareentwicklungsprojekte, die flexibel sein müssen, um schnell auf die Wünsche der Kunden reagieren zu können und um Änderungen zu berücksichtigen, die aufgrund von Architekturentscheidungen erforderlich sind. Nachdem sich die Datenwissenschaft als Eckpfeiler in Organisationen und Unternehmen etabliert hat, ist es nun zwingend erforderlich, diesen entscheidenden Schritt auch für analytische Geschäftsprozesse durchzuführen. Die nicht-deterministische Natur der Datenwissenschaft und die ihr innewohnenden analytischen Aufgaben erfordern einen interaktiven Ansatz für eine evolutionäre, schrittweise Entwicklung zur Realisierung der wichtigsten Geschäftsanwendungen und Anwendungsfälle. Die 4. Internationale Konferenz zur Datenwissenschaft (iDSC 2021) brachte Forscher, Wissenschaftler und Wirtschaftsexperten zusammen, um Möglichkeiten zu erörtern, wie neue Wege zur Umsetzung agiler Ansätze in den verschiedenen Bereichen der Datenwissenschaft, wie maschinelles Lernen und KI, Data Mining oder Visualisierung und Kommunikation, sowie Fallstudien und Best Practices von führenden Forschungseinrichtungen und Wirtschaftsunternehmen etabliert werden können. Der Tagungsband umfasst alle im wissenschaftlichen Track vorgestellten Volltexte und die Kurzbeiträge aus dem studentischen Track auf Englisch und die dazugehörigen Abstracts auf Deutsch. Zu den Themen, die sie interessieren, gehören unter anderem: Künstliche Intelligenz und Maschinelles Lernen Implementierung von Data-Mining-Prozessen Agile Datenwissenschaft und Visualisierung Fallstudien und Anwendungen für Agile Datenwissenschaft |
| data science maturity model: Big Data MBA Bill Schmarzo, 2015-12-11 Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity. Understand where and how to leverage big data Integrate analytics into everyday operations Structure your organization to drive analytic insights Optimize processes, uncover opportunities, and stand out from the rest Help business stakeholders to “think like a data scientist” Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions. |
| data science maturity model: Data Mining and Learning Analytics Samira ElAtia, Donald Ipperciel, Osmar R. Zaïane, 2016-09-20 Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research. |
| data science maturity model: Research Anthology on Privatizing and Securing Data Management Association, Information Resources, 2021-04-23 With the immense amount of data that is now available online, security concerns have been an issue from the start, and have grown as new technologies are increasingly integrated in data collection, storage, and transmission. Online cyber threats, cyber terrorism, hacking, and other cybercrimes have begun to take advantage of this information that can be easily accessed if not properly handled. New privacy and security measures have been developed to address this cause for concern and have become an essential area of research within the past few years and into the foreseeable future. The ways in which data is secured and privatized should be discussed in terms of the technologies being used, the methods and models for security that have been developed, and the ways in which risks can be detected, analyzed, and mitigated. The Research Anthology on Privatizing and Securing Data reveals the latest tools and technologies for privatizing and securing data across different technologies and industries. It takes a deeper dive into both risk detection and mitigation, including an analysis of cybercrimes and cyber threats, along with a sharper focus on the technologies and methods being actively implemented and utilized to secure data online. Highlighted topics include information governance and privacy, cybersecurity, data protection, challenges in big data, security threats, and more. This book is essential for data analysts, cybersecurity professionals, data scientists, security analysts, IT specialists, practitioners, researchers, academicians, and students interested in the latest trends and technologies for privatizing and securing data. |
| data science maturity model: Service-Oriented Perspectives in Design Science Research Hemant Jain, Atish P. Sinha, Padmal Vitharana, 2011-04-21 This book constitutes the refereed proceedings of the 6th International Conference on Service-Oriented Perspectives in Design Science Research, DERIST 2011, held in Milwaukee, WI, USA, in May 2011. The 29 revised full papers presented together with 5 revised short papers were carefully reviewed and selected from 50 submissions. The papers are organized in topical sections on design theory, design science research strategies, design methods and techniques, design evaluation, design guidelines, service-oriented perspectives in design science, process design, neuroscience in design research, and designing for social media. |
| data science maturity model: Implementing the Capability Maturity Model James R. Persse, 2001-08-27 Practical guidelines for an effective implementation of software development processes Designed to ensure effective software development processes, the Capability Maturity Model (CMM)--North America's leading standard for software development--requires companies to complete five steps, or levels, in the development process. But while it is widely adopted by Fortune 500 companies, many others get stuck at the initial planning stage. Focusing on Levels 2 and 3 of the CMM, this book helps readers to get over the hurdle of the two most problematic areas in this process--the project management and software development steps. It offers clear, step-by-step guidance on how to establish basic project management processes to track costs, schedules, and functionality; how to document, standardize, and integrate software processes; and how to improve software quality. |
| data science maturity model: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science maturity model: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| data science maturity model: Big Data Bill Schmarzo, 2013-09-23 Leverage big data to add value to your business Social media analytics, web-tracking, and other technologies help companies acquire and handle massive amounts of data to better understand their customers, products, competition, and markets. Armed with the insights from big data, companies can improve customer experience and products, add value, and increase return on investment. The tricky part for busy IT professionals and executives is how to get this done, and that's where this practical book comes in. Big Data: Understanding How Data Powers Big Business is a complete how-to guide to leveraging big data to drive business value. Full of practical techniques, real-world examples, and hands-on exercises, this book explores the technologies involved, as well as how to find areas of the organization that can take full advantage of big data. Shows how to decompose current business strategies in order to link big data initiatives to the organization’s value creation processes Explores different value creation processes and models Explains issues surrounding operationalizing big data, including organizational structures, education challenges, and new big data-related roles Provides methodology worksheets and exercises so readers can apply techniques Includes real-world examples from a variety of organizations leveraging big data Big Data: Understanding How Data Powers Big Business is written by one of Big Data's preeminent experts, William Schmarzo. Don't miss his invaluable insights and advice. |
| data science maturity model: Building a Smarter University Jason E. Lane, 2014-09-30 The Big Data movement and the renewed focus on data analytics are transforming everything from healthcare delivery systems to the way cities deliver services to residents. Now is the time to examine how this Big Data could help build smarter universities. While much of the cutting-edge research that is being done with Big Data is happening at colleges and universities, higher education has yet to turn the digital mirror on itself to advance the academic enterprise. Institutions can use the huge amounts of data being generated to improve the student learning experience, enhance research initiatives, support effective community outreach, and develop campus infrastructure. This volume focuses on three primary themes related to creating a smarter university: refining the operations and management of higher education institutions, cultivating the education pipeline, and educating the next generation of data scientists. Through an analysis of these issues, the contributors address how universities can foster innovation and ingenuity in the academy. They also provide scholarly and practical insights in order to frame these topics for an international discussion. |
| data science maturity model: Data Analytics and AI Jay Liebowitz, 2020-08-06 Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that artificial intelligence is included. We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data. |
| data science maturity model: The Practical Guide to HR Analytics Shonna D. Waters, Rachael Johnson-Murray, Valerie N. Streets, Lindsay McFarlane, 2018 The need for HR professionals to understand and apply data analytics is greater than ever. Today's successful HR professionals must ask insightful questions, understand key terms, and intelligently apply data, but may lack a clear understanding of the many forms, types, applications, interpretations, and capabilities of HR analytics. HR Analytics provides a practical approach to using data to solve real HR challenges in organizations and demystifies analytics with clear guidelines and recommendations for making the business case, starting an HR analytics function, avoiding common pitfalls, presenting data through visualization and storytelling, and much more. |
| data science maturity model: Architecting Modern Data Platforms Jan Kunigk, Ian Buss, Paul Wilkinson, Lars George, 2018-12-05 There’s a lot of information about big data technologies, but splicing these technologies into an end-to-end enterprise data platform is a daunting task not widely covered. With this practical book, you’ll learn how to build big data infrastructure both on-premises and in the cloud and successfully architect a modern data platform. Ideal for enterprise architects, IT managers, application architects, and data engineers, this book shows you how to overcome the many challenges that emerge during Hadoop projects. You’ll explore the vast landscape of tools available in the Hadoop and big data realm in a thorough technical primer before diving into: Infrastructure: Look at all component layers in a modern data platform, from the server to the data center, to establish a solid foundation for data in your enterprise Platform: Understand aspects of deployment, operation, security, high availability, and disaster recovery, along with everything you need to know to integrate your platform with the rest of your enterprise IT Taking Hadoop to the cloud: Learn the important architectural aspects of running a big data platform in the cloud while maintaining enterprise security and high availability |
| data science maturity model: Machine Learning and Data Science in the Oil and Gas Industry Patrick Bangert, 2021-03-04 Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. - Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful - Gain practical understanding of machine learning used in oil and gas operations through contributed case studies - Learn change management skills that will help gain confidence in pursuing the technology - Understand the workflow of a full-scale project and where machine learning benefits (and where it does not) |
| data science maturity model: Systems, Software and Services Process Improvement Murat Yilmaz, Jörg Niemann, Paul Clarke, Richard Messnarz, 2020-08-10 This volume constitutes the refereed proceedings of the 27th European Conference on Systems, Software and Services Process Improvement, EuroSPI conference, held in Düsseldorf, Germany, in September 2020*. The 50 full papers and 13 short papers presented were carefully reviewed and selected from 100 submissions. They are organized in topical sections on visionary papers, SPI manifesto and improvement strategies, SPI and emerging software and systems engineering paradigms, SPI and standards and safety and security norms, SPI and team performance & agile & innovation, SPI and agile, emerging software engineering paradigms, digitalisation of industry, infrastructure and e-mobility, good and bad practices in improvement, functional safety and cybersecurity, experiences with agile and lean, standards and assessment models, recent innovations, virtual reality. *The conference was partially held virtually due to the COVID-19 pandemic. |
| data science maturity model: The Economics of Data, Analytics, and Digital Transformation Bill Schmarzo, Dr. Kirk Borne, 2020-11-30 Build a continuously learning and adapting organization that can extract increasing levels of business, customer and operational value from the amalgamation of data and advanced analytics such as AI and Machine Learning Key Features Master the Big Data Business Model Maturity Index methodology to transition to a value-driven organizational mindset Acquire implementable knowledge on digital transformation through 8 practical laws Explore the economics behind digital assets (data and analytics) that appreciate in value when constructed and deployed correctly Book Description In today's digital era, every organization has data, but just possessing enormous amounts of data is not a sufficient market discriminator. The Economics of Data, Analytics, and Digital Transformation aims to provide actionable insights into the real market discriminators, including an organization's data-fueled analytics products that inspire innovation, deliver insights, help make practical decisions, generate value, and produce mission success for the enterprise. The book begins by first building your mindset to be value-driven and introducing the Big Data Business Model Maturity Index, its maturity index phases, and how to navigate the index. You will explore value engineering, where you will learn how to identify key business initiatives, stakeholders, advanced analytics, data sources, and instrumentation strategies that are essential to data science success. The book will help you accelerate and optimize your company's operations through AI and machine learning. By the end of the book, you will have the tools and techniques to drive your organization's digital transformation. Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the book: Data analytics should first and foremost be about action and value. Consequently, the great value of this book is that it seeks to be actionable. It offers a dynamic progression of purpose-driven ignition points that you can act upon. What you will learn Train your organization to transition from being data-driven to being value-driven Navigate and master the big data business model maturity index Learn a methodology for determining the economic value of your data and analytics Understand how AI and machine learning can create analytics assets that appreciate in value the more that they are used Become aware of digital transformation misconceptions and pitfalls Create empowered and dynamic teams that fuel your organization's digital transformation Who this book is for This book is designed to benefit everyone from students who aspire to study the economic fundamentals behind data and digital transformation to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their business careers. |
| data science maturity model: Data Science for Fundraising Ashutosh R Nandeshwar, Devine Rodger, 2018-02-14 Although the non-profit industry has advanced using CRMs and donor databases, it has not fully explored the data stored in those databases. Data Science for Fundraising will help you generate data-driven results and effective solutions for several challenges in your non-profit. Discover the techniques used by the top R programmers. |
| data science maturity model: Technology Management Dilek Cetindamar, Robert Phaal, 2017-09-16 This is an exciting and innovative core textbook that focuses on the micro-level analysis of TM as a dynamic capability. Now in its second edition and fully updated throughout, it systematically addresses the major tools and techniques needed for businesses to successfully conduct TM activities. Arguing that there is no single best way to manage technology in a company and there is no mechanistic route to success, this accessible handbook provides a wealth of resources designed to increase the dynamic capability of an organisation. Written by a highly experienced team of authors from the Universities of Sabanci and Cambridge, Technology Management is the perfect companion for undergraduate and postgraduate students on a variety of business, management and engineering degree courses. It is also suitable for practitioners seeking to progress their professional development and industry knowledge. |
| data science maturity model: Data Analytics for IT Networks John Garrett, 2018-10-24 Use data analytics to drive innovation and value throughout your network infrastructure Network and IT professionals capture immense amounts of data from their networks. Buried in this data are multiple opportunities to solve and avoid problems, strengthen security, and improve network performance. To achieve these goals, IT networking experts need a solid understanding of data science, and data scientists need a firm grasp of modern networking concepts. Data Analytics for IT Networks fills these knowledge gaps, allowing both groups to drive unprecedented value from telemetry, event analytics, network infrastructure metadata, and other network data sources. Drawing on his pioneering experience applying data science to large-scale Cisco networks, John Garrett introduces the specific data science methodologies and algorithms network and IT professionals need, and helps data scientists understand contemporary network technologies, applications, and data sources. After establishing this shared understanding, Garrett shows how to uncover innovative use cases that integrate data science algorithms with network data. He concludes with several hands-on, Python-based case studies reflecting Cisco Customer Experience (CX) engineers’ supporting its largest customers. These are designed to serve as templates for developing custom solutions ranging from advanced troubleshooting to service assurance. Understand the data analytics landscape and its opportunities in Networking See how elements of an analytics solution come together in the practical use cases Explore and access network data sources, and choose the right data for your problem Innovate more successfully by understanding mental models and cognitive biases Walk through common analytics use cases from many industries, and adapt them to your environment Uncover new data science use cases for optimizing large networks Master proven algorithms, models, and methodologies for solving network problems Adapt use cases built with traditional statistical methods Use data science to improve network infrastructure analysisAnalyze control and data planes with greater sophistication Fully leverage your existing Cisco tools to collect, analyze, and visualize data |
| data science maturity model: The Capability Maturity Model Mark C. Paulk, 1995 Principal Contributors and Editors: Mark C. Paulk, Charles V. Weber, Bill Curtis, Mary Beth Chrissis In every sense, the CMM represents the best thinking in the field today... this book is targeted at anyone involved in improving the software process, including members of assessment or evaluation teams, members of software engineering process groups, software managers, and software practitioners... From the Foreword by Watts Humphrey The Capability Maturity Model for Software (CMM) is a framework that demonstrates the key elements of an effective software process. The CMM describes an evolutionary improvement path for software development from an ad hoc, immature process to a mature, disciplined process, in a path laid out in five levels. When using the CMM, software professionals in government and industry can develop and improve their ability to identify, adopt, and use sound management and technical practices for delivering quality software on schedule and at a reasonable cost. This book provides a description and technical overview of the CMM, along with guidelines for improving software process management overall. It is a sequel to Watts Humphrey's important work, Managing the Software Process, in that it structures the maturity framework presented in that book more formally. Features: Compares the CMM with ISO 9001 Provides an overview of ISO's SPICE project, which is developing international standards for software process improvement and capability determination Presents a case study of IBM Houston's Space Shuttle project, which is frequently referred to as being at Level 5 0201546647B04062001 |
| data science maturity model: Interop John Palfrey, Urs Gasser, 2012-06-05 In Interop, technology experts John Palfrey and Urs Gasser explore the immense importance of interoperability -- the standardization and integration of technology -- and show how this simple principle will hold the key to our success in the coming decades and beyond. The practice of standardization has been facilitating innovation and economic growth for centuries. The standardization of the railroad gauge revolutionized the flow of commodities, the standardization of money revolutionized debt markets and simplified trade, and the standardization of credit networks has allowed for the purchase of goods using money deposited in a bank half a world away. These advancements did not eradicate the different systems they affected; instead, each system has been transformed so that it can interoperate with systems all over the world, while still preserving local diversity. As Palfrey and Gasser show, interoperability is a critical aspect of any successful system -- and now it is more important than ever. Today we are confronted with challenges that affect us on a global scale: the financial crisis, the quest for sustainable energy, and the need to reform health care systems and improve global disaster response systems. The successful flow of information across systems is crucial if we are to solve these problems, but we must also learn to manage the vast degree of interconnection inherent in each system involved. Interoperability offers a number of solutions to these global challenges, but Palfrey and Gasser also consider its potential negative effects, especially with respect to privacy, security, and co-dependence of states; indeed, interoperability has already sparked debates about document data formats, digital music, and how to create successful yet safe cloud computing. Interop demonstrates that, in order to get the most out of interoperability while minimizing its risks, we will need to fundamentally revisit our understanding of how it works, and how it can allow for improvements in each of its constituent parts. In Interop, Palfrey and Gasser argue that there needs to be a nuanced, stable theory of interoperability -- one that still generates efficiencies, but which also ensures a sustainable mode of interconnection. Pointing the way forward for the new information economy, Interop provides valuable insights into how technological integration and innovation can flourish in the twenty-first century. |
| data science maturity model: The Data Science Design Manual Steven S. Skiena, 2017-07-01 This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com) |
| data science maturity model: Software Ecosystems Slinger Jansen, Michael A. Cusumano, Sjaak Brinkkemper, 2013-01-01 This book describes the state-of-the-art of software ecosystems. It constitutes a fundamental step towards an empirically based, nuanced understanding of the implications for management, governance, and control of software ecosystems. This is the first book of its kind dedicated to this emerging field and offers guidelines on how to analyze software ecosystems; methods for managing and growing; methods on transitioning from a closed software organization to an open one; and instruments for dealing with open source, licensing issues, product management and app stores. It is unique in bringing together industry experiences, academic views and tackling challenges such as the definition of fundamental concepts of software ecosystems, describing those forces that influence its development and lifecycles, and the provision of methods for the governance of software ecosystems. This book is an essential starting point for software industry researchers, product managers, and entrepreneurs. |
| data science maturity model: The Data Science Framework Juan J. Cuadrado-Gallego, Yuri Demchenko, 2020-10-01 This edited book first consolidates the results of the EU-funded EDISON project (Education for Data Intensive Science to Open New science frontiers), which developed training material and information to assist educators, trainers, employers, and research infrastructure managers in identifying, recruiting and inspiring the data science professionals of the future. It then deepens the presentation of the information and knowledge gained to allow for easier assimilation by the reader. The contributed chapters are presented in sequence, each chapter picking up from the end point of the previous one. After the initial book and project overview, the chapters present the relevant data science competencies and body of knowledge, the model curriculum required to teach the required foundations, profiles of professionals in this domain, and use cases and applications. The text is supported with appendices on related process models. The book can be used to develop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines. |
| data science maturity model: Design Patterns Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides, 1995 Software -- Software Engineering. |
| data science maturity model: AI in Marketing, Sales and Service Peter Gentsch, 2018-10-22 AI and Algorithmics have already optimized and automated production and logistics processes. Now it is time to unleash AI on the administrative, planning and even creative procedures in marketing, sales and management. This book provides an easy-to-understand guide to assessing the value and potential of AI and Algorithmics. It systematically draws together the technologies and methods of AI with clear business scenarios on an entrepreneurial level. With interviews and case studies from those cutting edge businesses and executives who are already leading the way, this book shows you: how customer and market potential can be automatically identified and profiled; how media planning can be intelligently automated and optimized with AI and Big Data; how (chat)bots and digital assistants can make communication between companies and consumers more efficient and smarter; how you can optimize Customer Journeys based on Algorithmics and AI; and how to conduct market research in more efficient and smarter way. A decade from now, all businesses will be AI businesses – Gentsch shows you how to make sure yours makes that transition better than your competitors. |
| data science maturity model: Using the Project Management Maturity Model Harold Kerzner, 2011-11-29 Updated for today's businesses-a proven model FOR assessment and ongoing improvement Using the Project Management Maturity Model, Second Edition is the updated edition of Harold Kerzner's renowned book covering his Project Management Maturity Model (PMMM). In this hands-on book, Kerzner offers a unique, industry-validated tool for helping companies of all sizes assess and improve their progress in integrating project management into every part of their organizations. Conveniently organized into two sections, this Second Edition begins with an examination of strategic planning principles and the ways they relate to project management. In the second section, PMMM is introduced with in-depth coverage of the five different levels of development for achieving maturity. Easily adaptable benchmarking instruments for measuring an organization's progress along the maturity curve make this a practical guide for any type of company. Complete with an associated Web site packed with both teaching and learning tools, Using the Project Management Maturity Model, Second Edition helps managers, engineers, project team members, business consultants, and others build a powerful foundation for company improvement and excellence. |
| data science maturity model: It's All Analytics, Part III Scott Burk, Gary Miner, 2023-09-15 Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially the last 25 years, there has been an explosion of terms and methods born that automate and improve decision-making and operations. One term, called analytics, is an overarching description of a compilation of methodologies. But artificial intelligence (AI), statistics, decision science, and optimization, which have been around for decades, have resurged. Also, things like business intelligence, online analytical processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology, terminology? Extending on the foundations introduced in the first book, this book illustrates how professionals in healthcare, business, and government are applying these disciplines, methods, and technologies. The goal of this book is to get leaders and practitioners to start thinking about how they may deploy techniques outside their function or industry into their domain. Application of modern technology into new areas is one of the fastest, most effective ways to improve results. By providing a rich set of examples, this book fosters creativity in the application and use of AI and analytics in innovative ways. |
| data science maturity model: Engineering MLOps Emmanuel Raj, 2021-04-19 Get up and running with machine learning life cycle management and implement MLOps in your organization Key FeaturesBecome well-versed with MLOps techniques to monitor the quality of machine learning models in productionExplore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed modelsPerform CI/CD to automate new implementations in ML pipelinesBook Description Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production. The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you'll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You'll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you'll apply the knowledge you've gained to build real-world projects. By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization. What you will learnFormulate data governance strategies and pipelines for ML training and deploymentGet to grips with implementing ML pipelines, CI/CD pipelines, and ML monitoring pipelinesDesign a robust and scalable microservice and API for test and production environmentsCurate your custom CD processes for related use cases and organizationsMonitor ML models, including monitoring data drift, model drift, and application performanceBuild and maintain automated ML systemsWho this book is for This MLOps book is for data scientists, software engineers, DevOps engineers, machine learning engineers, and business and technology leaders who want to build, deploy, and maintain ML systems in production using MLOps principles and techniques. Basic knowledge of machine learning is necessary to get started with this book. |
| data science maturity model: The Enterprise Big Data Lake Alex Gorelik, 2019-02-21 The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities. But is it right for your company? This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises. You’ll learn what a data lake is, why enterprises need one, and how to build one successfully with the best practices in this book. Alex Gorelik, CTO and founder of Waterline Data, explains why old systems and processes can no longer support data needs in the enterprise. Then, in a collection of essays about data lake implementation, you’ll examine data lake initiatives, analytic projects, experiences, and best practices from data experts working in various industries. Get a succinct introduction to data warehousing, big data, and data science Learn various paths enterprises take to build a data lake Explore how to build a self-service model and best practices for providing analysts access to the data Use different methods for architecting your data lake Discover ways to implement a data lake from experts in different industries |
| data science maturity model: The Real Work of Data Science Ron S. Kenett, Thomas C. Redman, 2019-05-06 The essential guide for data scientists and for leaders who must get more from their data science teams The Economist boldly claims that data are now the world's most valuable resource. But, as Kenett and Redman so richly describe, unlocking that value requires far more than technical excellence. The Real Work of Data Science explores understanding the problems, dealing with quality issues, building trust with decision makers, putting data science teams in the right organizational spots, and helping companies become data-driven. This is the work that spells the difference between a good data scientist and a great one, between a team that makes marginal contributions and one that drives the business, between a company that gains some value from its data and one in which data truly is the most valuable resource. These two authors are world-class experts on analytics, data management, and data quality; they've forgotten more about these topics than most of us will ever know. Their book is pragmatic, understandable, and focused on what really counts. If you want to do data science in any capacity, you need to read it. —Thomas H. Davenport, Distinguished Professor, Babson College and Fellow, MIT Initiative on the Digital Economy I like your book. The chapters address problems that have faced statisticians for generations, updated to reflect today's issues, such as computational Big Data. —Sir David Cox, Warden of Nuffield College and Professor of Statistics, Oxford University Data science is critical for competitiveness, for good government, for correct decisions. But what is data science? Kenett and Redman give, by far, the best introduction to the subject I have seen anywhere. They address the critical questions of formulating the right problem, collecting the right data, doing the right analyses, making the right decisions, and measuring the actual impact of the decisions. This book should become required reading in statistics and computer science departments, business schools, analytics institutes and, most importantly, by all business managers. —A. Blanton Godfrey, Joseph D. Moore Distinguished University Professor, Wilson College of Textiles, North Carolina State University |
| data science maturity model: Systems, Software and Services Process Improvement Alastair Walker, Rory V. O'Connor, Richard Messnarz, 2019-09-09 This volume constitutes the refereed proceedings of the 26th European Conference on Systems, Software and Services Process Improvement, EuroSPI conference, held in Edinburgh, Scotland, in September 2019. The 18 revised full papers presented were carefully reviewed and selected from 28 submissions. They are organized in topical sections: Visionary Papers, SPI and Safety and Security, SPI and Assessments, SPI and Future Qualification & Team Performance, and SPI Manifesto and Culture. The selected workshop papers are also presented and organized in following topical sections: GamifySPI, Digitalisation of Industry, Infrastructure and E-Mobility. -Best Practices in Implementing Traceability. -Good and Bad Practices in Improvement. -Functional Safety and Cybersecurity. -Experiences with Agile and Lean. -Standards and Assessment Models. -Team Skills and Diversity Strategies. -Recent Innovations. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
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Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their …
