Koleksi Perpustakaan

Prinsip prinsip Manajemen Keuangan 2 (ed. 12)

Big Data Analytics Strategies for the Smart Grid

By implementing a comprehensive data analytics program, utility companies can meet the continually evolving challenges of modern grids that are operationally efficient, while reconciling the demands of greenhouse gas legislation and establishing a meaningful return on investment from smart grid deployments. Readable and accessible, Big Data Analytics Strategies for the Smart Grid addresses the needs of applying big data technologies and approaches, including Big Data cybersecurity, to the critical infrastructure that makes up the electrical utility grid. It supplies industry stakeholders with an in-depth understanding of the engineering, business, and customer domains within the power delivery market. The book explores the unique needs of electrical utility grids, including operational technology, IT, storage, processing, and how to transform grid assets for the benefit of both the utility business and energy consumers. It not only provides specific examples that illustrate how analytics work and how they are best applied, but also describes how to avoid potential problems and pitfalls. Discussing security and data privacy, it explores the role of the utility in protecting their customers’ right to privacy while still engaging in forward-looking business practices. The book includes discussions of: SAS for asset management tools The AutoGrid approach to commercial analytics Space-Time Insight’s work at the California ISO (CAISO) This book is an ideal resource for mid- to upper-level utility executives who need to understand the business value of smart grid data analytics. It explains critical concepts in a manner that will better position executives to make the right decisions about building their analytics programs. At the same time, the book provides sufficient technical depth that it is useful for data analytics professionals who need to better understand the nuances of the engineering and business challenges unique to the utilities industry.

SQL Engines for Big Data Analytics

Master's Thesis from the year 2018 in the subject Computer Science - Internet, New Technologies, grade: 8, , course: Master of Computer Application, language: English, abstract: This thesis aims to describe how data analytics works for big data and how they are used in business. It gives an overview of existing technologies and ap-proaches to building data analytics infrastructures. It also defines points that should be taken into consideration while choosing the most suitable software solution for a particular use case. The research is done by studying architectural principles of big data sys-tems and investigating the market of data analytics software. The result of this work is a composite report including comparison of several technologies and a list of criteria considered. The final report can be used as a guideline for choosing the most suitable technology for implementing an analytical platform in a broad variety of organizations. With a growing amount of data generated, their changing and evolving, the concept of big data has become incredibly popular in last years. It provides a set of new approaches and techniques allowing to work e ciently with huge volumes of records. Nowadays, information is one of the most important resources; it can help with decision making and business processes optimization. However, to get actual insights and unlock a potential of data, it is necessary to process them and discover the information hidden inside it which is a goal of data analytics. Data analytic platforms allow to manipulate with raw data in order to find out what exactly they contain. These systems are complex and includes multiple components therefore their designing requires comprehensive analysis of available options.

Big Data Analytics

Unique insights to implement big data analytics and reap big returns to your bottom line Focusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data Analytics. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities. Reveals big data analytics as the next wave for businesses looking for competitive advantage Takes an in-depth look at the financial value of big data analytics Offers tools and best practices for working with big data Once the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries. From how to mine the data your company collects, to the data that is available on the outside, Big Data Analytics shows how you can leverage big data into a key component in your business's growth strategy.

Big Data Analytics

Big Data Analytics will assist managers in providing an overview of the drivers for introducing big data technology into the organization and for understanding the types of business problems best suited to big data analytics solutions, understanding the value drivers and benefits, strategic planning, developing a pilot, and eventually planning to integrate back into production within the enterprise. Guides the reader in assessing the opportunities and value proposition Overview of big data hardware and software architectures Presents a variety of technologies and how they fit into the big data ecosystem

Information Systems Management in the Big Data Era

This timely text/reference explores the business and technical issues involved in the management of information systems in the era of big data and beyond. Topics and features: presents review questions and discussion topics in each chapter for classroom group work and individual research assignments; discusses the potential use of a variety of big data tools and techniques in a business environment, explaining how these can fit within an information systems strategy; reviews existing theories and practices in information systems, and explores their continued relevance in the era of big data; describes the key technologies involved in information systems in general and big data in particular, placing these technologies in an historic context; suggests areas for further research in this fast moving domain; equips readers with an understanding of the important aspects of a data scientist’s job; provides hands-on experience to further assist in the understanding of the technologies involved.

The Influences of Big Data Analytics

The theoretical framework for this book was our ground-up theory of the Scope, Size, Speed, and Skill (4Ss) and Technological Situational Happenstances (TSHs) applied to Big data analytics. With in-depth research, we catechized the effects of the coalesced insights from big data influencing the architectures of incremental and radical business models. We discussed data inflation and the global impact of TSHs. We showed how deft leadership used insights gleaned from big data analytics to make strategic decisions. The big data syndrome led to Microsoft's acquisition of Nokia in our case study. Our study of APPLE Corporation's use of large datasets was explicitly analyzed. Leaderships' failure to incorporate those contextual elements afforded by insights gleaned from big data analytics, concomitant with the associated costs led to acute forms of irrational rationalism, groupthink, and faulty decision making. We explained the statistics used to essentially describe this paradigm shift, such as high dimensionality, incidental endogeneity, noise accumulation, spurious correlation, and computational costs. Significantly, machine learning challenged the status quo by effectively changing the existing technological landscape. To scholarly critics, how would supervised and un-supervised learning algorithms advance the trajectory of perspectives in applied knowledge under the umbrella of big data? Further, political and socio-economics tied to big data was examined. We recommended leaders should have a shared cognition on how to leverage analytics from large datasets for competitive advantages. Most significantly, leaders or managers should be cognizant of the inextricable synergies that seamlessly flow from adroitly implementing a strategy to profit from the speed, size, skill, and scope (i.e. the 4Ss) of the big data environment, conditioned by the leveraging of those transactional situational happenstances generated by increases in market volatility. We concluded the algorithmic processes of leveraging insights from big data have globally resulted in a disruption of current technological pathways.

Manajemen syari'ah

Islamic management in financial institutions in Indonesia.

Big Data Marketing

Leverage big data insights to improve customer experiences and insure business success Many of today's businesses find themselves caught in a snarl of internal data, paralyzed by internal silos, and executing antiquated marketing approaches. As a result, consumers are losing patience, shareholders are clamoring for growth and differentiation, and marketers are left struggling to untangle the massive mess. Big Data Marketing provides a strategic road map for executives who want to clear the chaos and start driving competitive advantage and top line growth. Using real-world examples, non-technical language, additional downloadable resources, and a healthy dose of humor, Big Data Marketing will help you discover the remedy offered by data-driven marketing. Explains how marketers can use data to learn what they need to know Details strategies to drive marketing relevance and Return On Marketing Investment (ROMI) Provides a five-step approach in the journey to a more data-driven marketing organization Author Lisa Arthur, the Chief Marketing Officer for Teradata Applications, the leader in integrated marketing software, meets with thousands of CMOs and marketing professionals annually through public speaking and events Big Data Marketing reveals patterns in your customers' behavior and proven ways to elevate customer experiences. Leverage these insights to insure your business's success.

Internet of Things and Big Data Analytics for Smart Generation

This book discusses emerging technologies in the field of the Internet of Things and big data, an area that will be scaled in next two decades. Written by a team of leading experts, it is the only book focusing on the broad areas of both the Internet of things and big data. The thirteen chapters present real-time experimental methods and theoretical explanations, as well as the implementation of these technologies through various applications. Offering a blend of theory and hands-on practices, the book enables graduate, postgraduate and research students who are involved in real-time project scaling techniques to understand projects and their execution. It is also useful for senior computer students, researchers and industry workers who are involved in experimenting with the Internet of Things and big data technologies, helping them to solve the real-time problem. Moreover, the chapters covering cutting-edge technologies help multidisciplinary researchers who are bridging the gap of two different outset real-time problems.

Manajemen Operasi Dasar (Kumpulan soal dan Contoh Cara Penyelesaiannya)

Manajemen Operasi sebagai bahan ajar di perguruan tinggi, khusus Strata-1 (S1), merupakan matakuliah yang banyak mempelajari aspek operasi dari menjalankan bisnis, baik bisnis manufaktur maupun jasa. Perlunya latihan soal-soal dalam topik-topik yang ada dalam manajemen operasi, seperti produktivitas, teori keputusan, ramalan, manajemen kualitas, dan kendali proses statistikal, kapasitas, lokasi, tata letak, dan waktu standar kerja yang penulis harapkan dapat membantu mahasiswa untuk dapat belajar lebih mendalam mengenai contoh-contoh soal serta cara penyelesaiannya. Ditambah pula soal-soal yang dapat digunakan sebagai bahan belajar atau latihan mandiri bagi mahasiswa. Buku ini disusun berdasarkan pengalaman penulis dalam mengajar matakuliah ini.

Kamus istilah manajemen

Big Data at Work

Go ahead, be skeptical about big data. The author was—at first. When the term “big data” first came on the scene, bestselling author Tom Davenport (Competing on Analytics, Analytics at Work) thought it was just another example of technology hype. But his research in the years that followed changed his mind. Now, in clear, conversational language, Davenport explains what big data means—and why everyone in business needs to know about it. Big Data at Work covers all the bases: what big data means from a technical, consumer, and management perspective; what its opportunities and costs are; where it can have real business impact; and which aspects of this hot topic have been oversold. This book will help you understand: • Why big data is important to you and your organization • What technology you need to manage it • How big data could change your job, your company, and your industry • How to hire, rent, or develop the kinds of people who make big data work • The key success factors in implementing any big data project • How big data is leading to a new approach to managing analytics With dozens of company examples, including UPS, GE, Amazon, United Healthcare, Citigroup, and many others, this book will help you seize all opportunities—from improving decisions, products, and services to strengthening customer relationships. It will show you how to put big data to work in your own organization so that you too can harness the power of this ever-evolving new resource.

Big Data Revolution

Exploit the power and potential of Big Data to revolutionizebusiness outcomes Big Data Revolution is a guide to improving performance,making better decisions, and transforming business through theeffective use of Big Data. In this collaborative work by an IBMVice President of Big Data Products and an Oxford Research Fellow,this book presents inside stories that demonstrate the power andpotential of Big Data within the business realm. Readers are guidedthrough tried-and-true methodologies for getting more out of data,and using it to the utmost advantage. This book describes the majortrends emerging in the field, the pitfalls and triumphs beingexperienced, and the many considerations surrounding Big Data, allwhile guiding readers toward better decision making from theperspective of a data scientist. Companies are generating data faster than ever before, andmanaging that data has become a major challenge. With the rightstrategy, Big Data can be a powerful tool for creating effectivebusiness solutions – but deep understanding is key whenapplying it to individual business needs. Big DataRevolution provides the insight executives need to incorporateBig Data into a better business strategy, improving outcomes withinnovation and efficient use of technology. Examine the major emerging patterns in Big Data Consider the debate surrounding the ethical use of data Recognize patterns and improve personal and organizationalperformance Make more informed decisions with quantifiable results In an information society, it is becoming increasingly importantto make sense of data in an economically viable way. It can drivenew revenue streams and give companies a competitive advantage,providing a way forward for businesses navigating an increasinglycomplex marketplace. Big Data Revolution provides expertinsight on the tool that can revolutionize industries.

Real-Time Big Data Analytics

Design, process, and analyze large sets of complex data in real time About This Book Get acquainted with transformations and database-level interactions, and ensure the reliability of messages processed using Storm Implement strategies to solve the challenges of real-time data processing Load datasets, build queries, and make recommendations using Spark SQL Who This Book Is For If you are a Big Data architect, developer, or a programmer who wants to develop applications/frameworks to implement real-time analytics using open source technologies, then this book is for you. What You Will Learn Explore big data technologies and frameworks Work through practical challenges and use cases of real-time analytics versus batch analytics Develop real-word use cases for processing and analyzing data in real-time using the programming paradigm of Apache Storm Handle and process real-time transactional data Optimize and tune Apache Storm for varied workloads and production deployments Process and stream data with Amazon Kinesis and Elastic MapReduce Perform interactive and exploratory data analytics using Spark SQL Develop common enterprise architectures/applications for real-time and batch analytics In Detail Enterprise has been striving hard to deal with the challenges of data arriving in real time or near real time. Although there are technologies such as Storm and Spark (and many more) that solve the challenges of real-time data, using the appropriate technology/framework for the right business use case is the key to success. This book provides you with the skills required to quickly design, implement and deploy your real-time analytics using real-world examples of big data use cases. From the beginning of the book, we will cover the basics of varied real-time data processing frameworks and technologies. We will discuss and explain the differences between batch and real-time processing in detail, and will also explore the techniques and programming concepts using Apache Storm. Moving on, we'll familiarize you with “Amazon Kinesis” for real-time data processing on cloud. We will further develop your understanding of real-time analytics through a comprehensive review of Apache Spark along with the high-level architecture and the building blocks of a Spark program. You will learn how to transform your data, get an output from transformations, and persist your results using Spark RDDs, using an interface called Spark SQL to work with Spark. At the end of this book, we will introduce Spark Streaming, the streaming library of Spark, and will walk you through the emerging Lambda Architecture (LA), which provides a hybrid platform for big data processing by combining real-time and precomputed batch data to provide a near real-time view of incoming data. Style and approach This step-by-step is an easy-to-follow, detailed tutorial, filled with practical examples of basic and advanced features. Each topic is explained sequentially and supported by real-world examples and executable code snippets.

PENGANTAR MANAJEMEN

Dalam kehidupan manusia pentingnya pengelolaan pelaksanaan kegiatan secara teratur dan terlaksana sehingga kegiatan yang akan dilaksanakan dapat terlaksana dengan baik begitupula sebaliknya apabila tidak diikuti dengan kegiatan yang teratur dan terlaksana, maka kegiatan tersebut tidak bisa terselesaikan dengan lancar. Seperti halnya kegiatan di kehidupan sehari-hari apabila kegiatan dilaksanakan dengan pengelolaan yang baik tentunya akan terlaksana begitupun di kehidupan masyarakat yang dikelola dengan baik juga demikian dapat diselesaikan dengan baik, sebagai contoh kegiatan rutinitas sehari-hari apabila tidak dilaksanakan dengan pengelolaan secara teratur maka akan menumpuk dan akan semakin banyak tugas rutinitas yang dikerjakan sedangkan di kehidupan masyarakat itu dicontohkan dari kegiatan sosial kerja bakti di laksanakan di masyarakat yang terbagi beberapa bagian pengelolaan kelompok pelaksana dari bagian administrasi sampai bagian keuangan yang sudah dibagi teratur sehingga dapat melaksanakan kegiatan tersebut dengan baik dapat secara berkelanjutan pelaksanaannya. Ini merupakan bagian dari pengelolaan pelaksanaan kegiatan Manajemen yang teratur dan terlaksana yang bisa dijadikan pedoman dalam melaksanakan suatu aktivitas.

Oracle Big Data Handbook

"Cowritten by members of Oracle's big data team, [this book] provides complete coverage of Oracle's comprehensive, integrated set of products for acquiring, organizing, analyzing, and leveraging unstructured data. The book discusses the strategies and technologies essential for a successful big data implementation, including Apache Hadoop, Oracle Big Data Appliance, Oracle Big Data Connectors, Oracle NoSQL Database, Oracle Endeca, Oracle Advanced Analytics, and Oracle's open source R offerings"--Page 4 of cover.

MANAJEMEN KREDIT (Teori dan Konsep Bagi Bank Umum)

Buku Manajemen Kredit ini merupakan pengembangan dari buku Manajemen Bank yang pernah ditulis oleh Penulis pada edisi sebelumnya. Buku ini dapat menjadi rujukan referensi bagi mahasiswa perbankan yang ingin mendalami tentang bisnis perkreditan bank dan akademisi, tak lupa pula dapat ditujukan bagi praktisi dan pegawai bank dalam mendapatkan tambahan referensi tentang dunia bisnis perkreditan bagi bank umum dan BPR. Buku Manajemen Kredit ini berisi tentang Pengertian Kredit dan Jenis-jenisnya ( bab 1 ), Prinsip Dasar dan Kebijakan Pemberian Kredit ( bab 2 ), Perencanaan Kredit ( bab 3), Prosedur Umum Perkreditan ( bab 4 ) serta Administrasi kredit ( bab 5 ). Disamping kandungan isi buku diatas, buku manajemen kredit ini juga terdiri atas : analisa kredit ( bab 6), Mon itoring dan Pengawasan Kredit ( bab 7 ), Penanganan Kredit Bermasalah ( bab 8 ). Sedangkan pada bagian akhir buku ini berisi tentang, jaminan kredit ( bab 9), Suku Bunga Kredit ( bab 10 ) serta Asuransi Kredit dan Jaminan Kredit ( bab 11).

Ethics of Big Data

What are your organization’s policies for generating and using huge datasets full of personal information? This book examines ethical questions raised by the big data phenomenon, and explains why enterprises need to reconsider business decisions concerning privacy and identity. Authors Kord Davis and Doug Patterson provide methods and techniques to help your business engage in a transparent and productive ethical inquiry into your current data practices. Both individuals and organizations have legitimate interests in understanding how data is handled. Your use of data can directly affect brand quality and revenue—as Target, Apple, Netflix, and dozens of other companies have discovered. With this book, you’ll learn how to align your actions with explicit company values and preserve the trust of customers, partners, and stakeholders. Review your data-handling practices and examine whether they reflect core organizational values Express coherent and consistent positions on your organization’s use of big data Define tactical plans to close gaps between values and practices—and discover how to maintain alignment as conditions change over time Maintain a balance between the benefits of innovation and the risks of unintended consequences

MANAJEMEN RESIKO Untuk Bank Perkreditan Rakyat Dan Lembaga Keuangan Mikro

Kompetisi dalam industri perbankan, khususnya Bank Perkreditan Rakyat, cenderung semakin ketat di tengah tuntutan pemegang saham yang meningkat juga. Kondisi tersebut bisa memancing para bankir untuk bertindak agresif dan menurunkan standar penerapan manajemen risiko. Untuk menyikapi kondisi tersebut, maka bankir harus dibekali dengan pengetahuan dan keterampilan manajemen risiko yang semakin tinggi sehingga target pertumbuhan bisnis dapat dicapai dengan koridor manajemen risiko yang memadai. Di sisi lain, transaksi bisnis Bank Perkreditan Rakyat juga semakin kompleks disertai dengan beragam produk yang tentunya memiliki tingkat risiko yang berbeda pula. Buku Manajemen Risiko ini disusun dengan tujuan agar para bankir yang bertugas di bidang manajemen risiko memiliki pengetahuan (knowledge), keterampilan (skill), dan sikap kerja (attitude) sesuai kebutuhan bank.