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Skills for the Digital Transition Assessing Recent Trends Using Big Data

Assessing Recent Trends Using Big Data

This report presents the most recent trends in the labour market demand for digital professionals and skills, highlighting where bottlenecks are emerging and policy action is – and will be – needed to support individuals who aim to thrive in the digital transition.

This report presents the most recent trends in the labour market demand for digital professionals and skills, highlighting where bottlenecks are emerging and policy action is – and will be – needed to support individuals who aim to ...

Teknologi Big Data

Sistem Canggih dibalik Google Facebook Yahoo! IBM

Buku ini adalah edisi ke-2 ( revisi ) setelah edisi pertama beredar selama hampir 4 tahun ( terbit pada 31 Maret 2019 ). Dibanding edisi sebelumnya, tentu saja edisi revisi ini memiliki konten yang lebih up to date dan lebih lengkap. Edisi terbaru ini didedikasikan untuk dapat memberikan pengetahuan dasar namun komprehensif tentang konsep Big Data beserta teknologi terapannya. Isinya disajikan sedemikian rupa mulai dari teori yang sifatnya konseptual, tutorial, hingga contoh implementasi sederhana yang berbasis real world data. Pembahasan dimulai dengan definisi Big Data, latar belakang munculnya istilah tersebut, serta berbagai contoh kasus aplikasinya. Kemudian dilanjutkan dengan konsep teknologi Big Data yang telah dikembangkan oleh Google, diantaranya: Google File System, MapReduce, dan Big Table. Pada bab berikutnya, diuraikan konsep dan cara kerja Hadoop, HBase, dan Spark yang dikembangkan oleh Apache Software Foundation. Selain itu, dibahas juga tentang teknologi web crawling dan database NoSQL MongoDB. Pada bab terakhir, guna merealisasikan pemahaman yang lebih komprehensif, disajikan sejumlah contoh sederhana implementasi teknologi Big Data yang mencakup tahap pengumpulan data, penyimpanan, analisa, hingga presentasi atau visualisasi. Contoh-contoh implementasi tersebut dikerjakan dengan memberdayakan real world data ( bukan data simulasi ). Sebagai pelengkap, pada bagian lampiran disertakan tutorial tentang cara setup dan penggunaan Hadoop, MapReduce, HBase dan Spark, baik pada platform Windows maupun Linux yang disertai dengan contoh program aplikasi. Dengan konten yang mencakup aspek teori dan implementasi praktis, buku ini dapat menjadi referensi bagi berbagai kalangan: pelajar, akademisi, pelaku bisnis, maupun pemerintah, atau bahkan dapat menjadi semacam sumber inspirasi guna memicu lahirnya inovasi-inovasi kreatif terkait pemberdayaan data dan informasi, khususnya dalam konteks Big Data.

Perbankan. Penggunaan model machine learning untuk memprediksi profil dari pelanggan perbankan retail yang cocok terhadap suatu produk finansial. 6. Pemerintahan. Analisa pengeluaran berkaitan dengan geografi, waktu, dan kategori. 7.

Big Data in Practice

How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results

The best-selling author of Big Data is back, this time with a unique and in-depth insight into how specific companies use big data. Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario. Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking Develop your own big data strategy by accessing additional reading materials at the end of each chapter

In this book you find out succinctly how leading companies are getting real value from Big Data – highly recommended read!" —Arthur Lee, Vice President of Qlik Analytics at Qlik

Big Data in Banking

With Applications in Finance, Investment, Wealth & Asset Management + WS

Big Data in Banking: With Applications in Finance, Investments, Wealth & Asset Management gives you a deeper understanding of the economics and the technology behind big data applied within the world of Finance, Investments, Wealth and Asset Management, the theories behind it, as well as potential future uses. This book assists you to understand all the buzz and excitement around these innovative technologies. Part I introduces the background of Big Data in non-technical terms, and complements it with general applications within a company (e.g. Human Resources). Part II focuses on the technology and makes comparisons to High Frequency Trading and Trading Strategy development, Data Mining and Risk Management issues and opportunities. Part III covers Client Behaviour, Client Acquisition and retention strategies, as well as Robo Advisors and Investment Processes. Part IV, zooms in on Intellectual Property and Transfer Pricing. Mention is also made of the tension between Ethics, Privacy, Transparency and Trust. It Includes cutting edge proposals to create a Big Data Strategy, how to deal with Applications (build vs. buy). This part concludes by discussing potential future uses of Big Data, Digitization and Data Analytics. A small appendix with basic statistics is provided for people that need more information about this area.

Clear and concise, this accessible guide consolidates a wealth of information into a non-technical overview of the issues and opportunities Big Data brings to the banking industry.

Big Data Imperatives

Enterprise ‘Big Data’ Warehouse, ‘BI’ Implementations and Analytics

Big Data Imperatives, focuses on resolving the key questions on everyone's mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications? Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use. Big Data Imperatives describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other. This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible. This book can also be used as a handbook for practitioners; helping them on methodology,technical architecture, analytics techniques and best practices. At the same time, this book intends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data.

At the same time, this book intends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data.

Guide to Big Data Applications

This handbook brings together a variety of approaches to the uses of big data in multiple fields, primarily science, medicine, and business. This single resource features contributions from researchers around the world from a variety of fields, where they share their findings and experience. This book is intended to help spur further innovation in big data. The research is presented in a way that allows readers, regardless of their field of study, to learn from how applications have proven successful and how similar applications could be used in their own field. Contributions stem from researchers in fields such as physics, biology, energy, healthcare, and business. The contributors also discuss important topics such as fraud detection, privacy implications, legal perspectives, and ethical handling of big data.

Big Data alchemy: How can banks maximize the value of their customer data? Cap Gemini Consulting. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), ...

Big Data Strategies for Agile Business

Agile is a set of values, principles, techniques, and frameworks for the adaptable, incremental, and efficient delivery of work. Big Data is a rapidly growing field that encompasses crucial aspects of data such as its volume, velocity, variety, and veracity. This book outlines a strategic approach to Big Data that will render a business Agile. It discusses the important competencies required to streamline and focus on the analytics and presents a roadmap for implementing such analytics in business.

Although A-Bank is a hypothetical bank described here to demonstrate the application of BDFAB, a comparison with and a ... The staff are eager to capitalize on the advanced technologies of Big Data to obtain and build on corresponding ...

ANALISIS BIG DATA

Analisis Big Data sangat diperlukan bagi masyarakat dalam perkembangan zaman saat ini dikarenakan big data memberikan kemudahan dan kecepatan akses pada aliran data transaksi. Dengan menggunakan big data, akuntan dalam suatu organisasi dapat mengakses informasi transaksi dengan lebih cepat dan dapat bekerja dalam transaksi berskala besar.

Contoh Kasus Big Data Dalam Industri BANK BRI Bank BRI adalah salah satu bank milik pemerintah terbesar di Indonesia. Bank BRI hadir untuk memberikan pelayanan yang terbaik kepada Masyarakat Indonesia, menjangkau dari yang tak ...

Security, Privacy, and Forensics Issues in Big Data

With the proliferation of devices connected to the internet and connected to each other, the volume of data collected, stored, and processed is increasing every day, which brings new challenges in terms of information security. As big data expands with the help of public clouds, traditional security solutions tailored to private computing infrastructures and confined to a well-defined security perimeter, such as firewalls and demilitarized zones (DMZs), are no longer effective. New security functions are required to work over the heterogenous composition of diverse hardware, operating systems, and network domains. Security, Privacy, and Forensics Issues in Big Data is an essential research book that examines recent advancements in big data and the impact that these advancements have on information security and privacy measures needed for these networks. Highlighting a range of topics including cryptography, data analytics, and threat detection, this is an excellent reference source for students, software developers and engineers, security analysts, IT consultants, academicians, researchers, and professionals.

Different big data technologies are introduced in many areas of banking right from mobile banking, revealing the signs of fraudulent happenings in internet banking, to the management of cash. These technologies contribute to a higher ...

Big Data Concepts, Theories, and Applications

This book covers three major parts of Big Data: concepts, theories and applications. Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions for Big Data in research and practice. It also focuses on high level concepts such as definitions of Big Data from different angles; surveys in research and applications; and existing tools, mechanisms, and systems in practice. Each chapter is independent from the other chapters, allowing users to read any chapter directly. After examining the practical side of Big Data, this book presents theoretical perspectives. The theoretical research ranges from Big Data representation, modeling and topology to distribution and dimension reducing. Chapters also investigate the many disciplines that involve Big Data, such as statistics, data mining, machine learning, networking, algorithms, security and differential geometry. The last section of this book introduces Big Data applications from different communities, such as business, engineering and science. Big Data Concepts, Theories and Applications is designed as a reference for researchers and advanced level students in computer science, electrical engineering and mathematics. Practitioners who focus on information systems, big data, data mining, business analysis and other related fields will also find this material valuable.

interactions between the customers and the banks, and in the meantime led to an increase in virtual interactions and increasing volume of customer data. The data that banks hold about their customers is much bigger in volume and much ...