Making Data
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Making Data Work
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Author : Edosa Odaro
language : en
Publisher: CRC Press
Release Date : 2022-04-04
Making Data Work written by Edosa Odaro and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-04 with Computers categories.
If you care about AI transformation, empowering people or advancing organisational success in an increasingly digital world, then you should read this book.—Yomi Ibosiola, Chief Data and Analytics Officer, Union Bank A retail giant already struggling due to the Covid-19 pandemic was faced with a disastrous situation when—at the end of a critical investment in an artificial intelligence project that had been meant to save money—it suddenly discovered that its implementation was likely to leave it worse off. An entire critical service stream within an insurer’s production system crashed. This critical failure resulted in the detentions of fully insured motorists for allegedly not carrying required insurance. Making Data Work: Enabling Digital Transformation, Empowering People and Advancing Organisational Success details these two scenarios as well as others illustrating the consequences that arise when organizations do not know how to make data work properly. It is a journey to determine what to do to "make data work" for ourselves and for our organisations. It is a journey to discover how to bring it all together so organisations can enable digital transformation, empower people, and advance organisational success. It is the journey to a world where data and technology finally live up to the hype and deliver better human outcomes, where artificial intelligence can move us from reacting to situations to predicting future occurrences and enabling desirable possibilities.
Making Data Visual
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Author : Danyel Fisher
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-12-20
Making Data Visual written by Danyel Fisher and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-20 with Computers categories.
"You have a mound of data sitting in front of you and a suite of computation tools at your disposal. And yet, you're stumped as to how to turn that data into insight. Which part of that data actually matters, and where is this insight hidden? If you're a data scientist who struggles to navigate the murky space between data and insight, this book will help you think about and reshape data for visual data exploration. It's ideal for relatively new data scientists, who may be computer-knowledgeable and data-knowledgeable, but do not yet know how to create effective, explorable representations of data. With this book, you'll learn: Task analysis, driven by a series of leading questions that draw out the important aspects of the data to be explored; Visualization patterns, each of which take a different perspective on data and answer different questions; A taxonomy of visualizations for common data types; Techniques for gathering design requirements; When and where to make use of statistical methods."--
Making Data In Qualitative Research
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Author : Laura L. Ellingson
language : en
Publisher: Routledge
Release Date : 2020-03-09
Making Data In Qualitative Research written by Laura L. Ellingson and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-09 with Social Science categories.
Making Data in Qualitative Research offers a generative alternative to outdated approaches to data collection. By reimagining methods through a model of data engagement, qualitative researchers consider what is at stake—ethically, methodologically, and theoretically—when we co-create data and imagine possibilities for doing data differently. Ellingson and Sotirin draw on critical, intersectional perspectives, including feminist, poststructuralist, new materialist, and postqualitative theorizing, to refigure methodological practices of data collection for the contemporary moment. Ellingson and Sotirin’s data engagement model offers a vibrant framework through which data are made rather than found; assembled rather than collected or gathered; and becoming or dynamic rather than static. Further, pragmatism, compassion, and joy form a compelling ethical foundation for engaging with qualitative data reflecting the full range of critical, postpositivist, intepretivist, and arts-based research methods. Chapters illuminate creative possibilities for engaging fieldnotes, audio/video recordings and photographs, transcription, digital/online data, participatory data, and self-as-data. Making Data in Qualitative Research is a great resource for researchers who want to move past simplistic approaches to qualitative data collection and embrace provocative possibilities for engaging with data. Bridging abstract theorizing and pragmatic strategies for making a wide variety of data, this book will appeal to graduate (and advanced undergraduate) qualitative methods students and early career researchers, as well as to advanced scholars looking to update and expand the scope of their methods.
Making Data
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Author : Ian Gwilt
language : en
Publisher: Bloomsbury Publishing
Release Date : 2022-03-24
Making Data written by Ian Gwilt and has been published by Bloomsbury Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-24 with Design categories.
For many outside of the scientific community, big data and the forms it takes, such as statistical lists, spreadsheets and graphs, often seem abstract and unintelligible. This book investigates how digital fabrication and traditional making approaches are being used to present data in newly engaging and interesting ways. The first part of the book introduces the basic premise of the data object and the concept of making digital data into a physical form. Contributors cover topics such as biometrics, new technology, the economics of data and open and community uses of data. The second part presents a selection of exemplar forms and contexts for the application of data-objects, such as smart surfaces, smart cities, augmented reality techniques and next generation technical interfaces that blend physical and digital elements. Making Data delivers the importance and likely future prevalence of physical representations of data. It explores the creative methods, processes, theories and cultural histories of making physical representations of information and proposes that the making of data into physical objects is the next important development in the data visualisation phenomenon.
Making Data Smarter With Ibm Spectrum Discover Practical Ai Solutions
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Author : Ivaylo B. Bozhinov
language : en
Publisher: IBM Redbooks
Release Date : 2020-10-19
Making Data Smarter With Ibm Spectrum Discover Practical Ai Solutions written by Ivaylo B. Bozhinov and has been published by IBM Redbooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-19 with Computers categories.
More than 80% of all data that is collected by organizations is not in a standard relational database. Instead, it is trapped in unstructured documents, social media posts, machine logs, and so on. Many organizations face significant challenges to manage this deluge of unstructured data, such as the following examples: Pinpointing and activating relevant data for large-scale analytics Lacking the fine-grained visibility that is needed to map data to business priorities Removing redundant, obsolete, and trivial (ROT) data Identifying and classifying sensitive data IBM® Spectrum Discover is a modern metadata management software that provides data insight for petabyte-scale file and Object Storage, storage on-premises, and in the cloud. This software enables organizations to make better business decisions and gain and maintain a competitive advantage. IBM Spectrum® Discover provides a rich metadata layer that enables storage administrators, data stewards, and data scientists to efficiently manage, classify, and gain insights from massive amounts of unstructured data. It improves storage economics, helps mitigate risk, and accelerates large-scale analytics to create competitive advantage and speed critical research. This IBM Redbooks® publication presents several use cases that are focused on artificial intelligence (AI) solutions with IBM Spectrum Discover. This book helps storage administrators and technical specialists plan and implement AI solutions by using IBM Spectrum Discover and several other IBM Storage products.
Data Literacy In Practice
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Author : Angelika Klidas
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-11-30
Data Literacy In Practice written by Angelika Klidas and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-30 with Computers categories.
Accelerate your journey to smarter decision making by mastering the fundamentals of data literacy and developing the mindset to work confidently with data Key FeaturesGet a solid grasp of data literacy fundamentals to support your next steps in your careerLearn how to work with data and extract meaningful insights to take the right actionsApply your knowledge to real-world business intelligence projectsBook Description Data is more than a mere commodity in our digital world. It is the ebb and flow of our modern existence. Individuals, teams, and enterprises working with data can unlock a new realm of possibilities. And the resultant agility, growth, and inevitable success have one origin—data literacy. This comprehensive guide is written by two data literacy pioneers, each with a thorough footprint within the data and analytics commercial world and lectures at top universities in the US and the Netherlands. Complete with best practices, practical models, and real-world examples, Data Literacy in Practice will help you start making your data work for you by building your understanding of data literacy basics and accelerating your journey to independently uncovering insights. You'll learn the four-pillar model that underpins all data and analytics and explore concepts such as measuring data quality, setting up a pragmatic data management environment, choosing the right graphs for your readers, and questioning your insights. By the end of the book, you'll be equipped with a combination of skills and mindset as well as with tools and frameworks that will allow you to find insights and meaning within your data for data-informed decision making. What you will learnStart your data literacy journey with simple and actionable stepsApply the four-pillar model for organizations to transform data into insightsDiscover which skills you need to work confidently with dataVisualize data and create compelling visual data storiesMeasure, improve, and leverage your data to meet organizational goalsMaster the process of drawing insights, ask critical questions and action your insightsDiscover the right steps to take when you analyze insightsWho this book is for This book is for data analysts, data professionals, and data teams starting or wanting to accelerate their data literacy journey. If you're looking to develop the skills and mindset you need to work independently with data, as well as a solid knowledge base of the tools and frameworks, you'll find this book useful.
Making Data Portability More Effective For The Digital Economy
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Author : Jan Krämer
language : en
Publisher: Centre on Regulation in Europe asbl (CERRE)
Release Date : 2020-06-15
Making Data Portability More Effective For The Digital Economy written by Jan Krämer and has been published by Centre on Regulation in Europe asbl (CERRE) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-15 with Political Science categories.
This study provides recommendations on how to make personal data portability more effective. This will truly empower consumers to use the services they want and share their data with whoever they wish and stimulate innovation in Europe. With the entry into force of the GDPR, European citizens gained new rights, notably with data portability. But two years later, there is still little sign of people exercising this right, and of companies offering an easy and convenient service for data portability. While the European Commission is finalising its evaluation of the GDPR and closes its consultation on the European data strategy, the authors, professors Jan Krämer, Pierre Senellart and Alexandre de Streel*, warn that the current legal framework requires clarifications to better empower European citizens in a data-driven society. In this study, they identify barriers to data portability, including the lack of possibilities to import data as well as the lack of common standards and tools to access data as easy as the click of a button. The ability to provide users with a centralised dashboard for monitoring and controlling the flow of their data is also critically missing. “Today, consumers do not widely use data portability for reasons that can and should be overcome. Making data portability more effective is better for competition, for innovation and to empower users,” stress the authors. “There should be no second-guessing on whether to make data portability more effective, the time to act is now.” The current EU framework encourages data portability, but there are legal gaps that the EU should fill. The authors insist on the need for detailed guidance on how data portability can be facilitated and on which data is subject to data portability without violating privacy rights. They advocate that data provided by users when using a service, such as search history (i.e. “observed data”) should clearly be included under the scope of data portability. The authors consider it essential that the obligation to offer standardised Application Programming Interfaces (APIs) be much more widespread to enable consumers to continuously port their data. “We believe that standardised APIs that enable continuous data portability is a prerequisite for encouraging more organisations to import personal data, and for encouraging more consumers to initiate such transfers,” explain the authors. Projects, such as the Data Transfer Project have highlighted that continuous data portability is technically feasible. The authors argue that Personal Management Information Systems (PIMSs) facilitate the complex consent management and offer users a centralised dashboard for monitoring and controlling the flow of their data will have a crucial role to play for the wider adoption of data portability. “It must be as easy as clicking a button for consumers to continuously share data they created with one provider to another provider. This may also require educating and informing users on their rights through information campaigns alongside clear policy measures,” explain the authors. Nevertheless, they stress that PIMSs are not likely to find a sustainable business model, and thus, policy makers should support the emergence of open-source projects by setting common standards for data transfers, consent management, and identity management.
Data Driven Decision Making For Long Term Business Success
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Author : Singh, Sonia
language : en
Publisher: IGI Global
Release Date : 2023-12-21
Data Driven Decision Making For Long Term Business Success written by Singh, Sonia and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-21 with Business & Economics categories.
In today's academic environment, the challenge of ensuring lasting commercial and economic success for organizations has become more daunting than ever before. The relentless surge in data-driven decision-making, based on innovative technologies such as blockchain, IoT, and AI, has created a digital frontier filled with complexity. Maintaining a healthy firm that can continually provide innovative products and services to the public while fueling economic growth has become a formidable puzzle. Moreover, this digital transformation has ushered in new risks, from pervasive cybersecurity threats to the ethical challenges surrounding artificial intelligence. In this evolving landscape, academic scholars face the pressing challenge of deciphering the path to long-term organizational prosperity in an era dominated by data. Data-Driven Decision Making for Long-Term Business Success serves as guidance and insights amidst this academic challenge. It is the definitive solution for scholars seeking to uncover the complexities of data-driven decision-making and its profound impact on organizational success. Each meticulously curated chapter delves into a specific facet of this transformative journey, from the implications of modern technologies and pricing optimization to the ethics underpinning data-driven strategies and the metaverse's influence on decision-making.
Management Decision Making Big Data And Analytics
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Author : Simone Gressel
language : en
Publisher: SAGE
Release Date : 2020-10-12
Management Decision Making Big Data And Analytics written by Simone Gressel and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-12 with Business & Economics categories.
An exciting new textbook examining big data and business analytics to look at how they can help managers become more effective decision-makers.
Data Science And Business Intelligence For Corporate Decision Making
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Author : Dr. P. S. Aithal
language : en
Publisher: Srinivas Publication, Mangalore
Release Date : 2024-02-09
Data Science And Business Intelligence For Corporate Decision Making written by Dr. P. S. Aithal and has been published by Srinivas Publication, Mangalore this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-09 with Art categories.
About the Book: A comprehensive book plan on "Data Science and Business Intelligence for Corporate Decision-Making" with 15 chapters, each with several sections: Chapter 1: Introduction to Data Science and Business Intelligence Chapter 2: Foundations of Data Science Chapter 3: Business Intelligence Tools and Technologies Chapter 4: Data Visualization for Decision-Making Chapter 5: Machine Learning for Business Intelligence Chapter 6: Big Data Analytics Chapter 7: Data Ethics and Governance Chapter 8: Data-Driven Decision-Making Process Chapter 9: Business Intelligence in Marketing Chapter 10: Financial Analytics and Business Intelligence Chapter 11: Operational Excellence through Data Analytics Chapter 12: Human Resources and People Analytics Chapter 13: Case Studies in Data-Driven Decision-Making Chapter 14: Future Trends in Data Science and Business Intelligence Chapter 15: Implementing Data Science Strategies in Corporations Each chapter dives deep into the concepts, methods, and applications of data science and business intelligence, providing practical insights, real-world examples, and case studies for corporate decision-making processes.