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"Corporations and governmental agencies of all sizes are embracing a new generation of enterprise-scale business intelligence (BI) and data warehousing (DW), and very often appoint a single senior-level individual to serve as the enterprise BI/DW program manager. This book is the essential guide to the incremental and iterative build-out of a successful enterprise-scale BI/DW program comprised of multiple underlying projects, and what the enterprise program Manager must successfully accomplish to orchestrate the many moving parts in the quest for true enterprise-scale business intelligence and data warehousing.Author Alan Simon has served as an enterprise business intelligence and data warehousing program management advisor to many of his clients, and spent an entire year with a single client as the adjunct consulting director for a $10 million enterprise data warehousing (EDW) initiative. He brings a wealth of knowledge about best practices, risk management, organizational culture alignment, and other Critical Success Factors (CSFs) to the discipline of enterprise-scale business intelligence and data warehousing."

"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."

"Learn about the emergence and evolution of IT in the enterprise, see how machine learning is transforming business intelligence, and discover various cognitive artificial intelligence solutions that complement and extend machine learning. In this book, author Rohit Kumar explores the challenges when these concepts intersect in IT systems by presenting detailed descriptions and business scenarios. He starts with the basics of how artificial intelligence started and how cognitive computing developed out of it. He'll explain every aspect of machine learning in detail, the reasons for changing business models to adopt it, and why your business needs it. Along the way you'll become comfortable with the intricacies of natural language processing, predictive analytics, and cognitive computing. Each technique is covered in detail so you can confidently integrate it into your enterprise as it is needed. This practical guide gives you a roadmap for transformin g your business with cognitive computing, giving you the ability to work confidently in an ever-changing enterprise environment. You will: See the history of AI and how machine learning and cognitive computing evolved Discover why cognitive computing is so important and why your business needs it Master the details of modern AI as it applies to enterprises Map the path ahead in terms of your IT-business integration Avoid common road blocks in the process of adopting cognitive computing in your business."

"Data warehousing in the age of the big data will help you and your organization make the most of unstructured data with your existing data warehouse.As big data continues to revolutionize how we use data, it doesn't have to create more confusion. Expert author Krish Krishnan helps you make sense of how big data fits into the world of data warehousing in clear and concise detail. The book is presented in three distinct parts. Part 1 discusses big data, its technologies and use cases from early adopters. Part 2 addresses data warehousing, its shortcomings, and new architecture options, workloads, and integration techniques for Big Data and the data warehouse. Part 3 deals with data governance, data visualization, information life-cycle management, data scientists, and implementing a big data–ready data warehouse. Extensive appendixes include case studies from vendor implementations and a special segment on how we can build a healthcare information factory."

"[ABSTRAKPenelitian ini mengangkat isu masalah aksesibilitas informasi Pajak Daerah dan penetapan target penerimaan. Isu aksesibilitas disebabkan oleh kompleksitas prosesdalam mengumpulkan dan mengkonsolidasi data dari beberapa sumber yangtersebar pada unit-unit pelayanan. Di sisi lain Dinas Pelayanan Pajak (DPP) harusmenetapkan target penerimaan berdasarkan data tahun sebelumnya denganmenggunakan metode tertentu. Tujuan penelitian ini untuk menjawab permasalahantersebut dengan melakukan perancangan data warehouse, mengimplementasikandalam bentuk prototipe, memproses cube untuk kepentingan analisis multi dimensional, membuat business intelligence dashboard, dan data mining untuk proyeksi penerimaan Pajak Daerah di masa mendatang. Metodologi yangdigunakan untuk merancang data warehouse adalah metodologi yang dikemukakanoleh Ralph Kimball. Hasil dari penelitian ini adalah rancangan dan implementasiprototipe data warehouse, business intelligence dashboard, dan proyeksi penerimaan Pajak Daerah di masa mendatang yang dapat menjawab kebutuhan informasi DPP.

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ABSTRACTThis research addresses both local taxes information accessibility and revenue target setting issues. The accessibility issue arise from the complexity of compilingprocess since these data have to be gathered and consolidated from several sourcesacross many tax offices. Simultaneously the Local Tax Authority (Dinas PelayananPajak-DPP) has to set annual revenue target which usually derived from time seriesdata by implementing a certain revenue forecasting method. The purposes of thisresearch is to solve the accessibility issue and provide a scientific forecastingmethod by designing data warehouse, implementing its prototype, processing thecubes for multi dimensional analysis, providing a business intelligence dashboard,and mining the data which used in the forecasting process. This research uses datawarehouse design methodology provided by Ralph Kimball. The outcomes of thisresearch are data warehouse design and prototype, business intelligence dashboard, and local taxes revenue forecasting method to provide the information as needed by DPP. ;This research addresses both local taxes information accessibility and revenue target setting issues. The accessibility issue arise from the complexity of compilingprocess since these data have to be gathered and consolidated from several sourcesacross many tax offices. Simultaneously the Local Tax Authority (Dinas PelayananPajak-DPP) has to set annual revenue target which usually derived from time seriesdata by implementing a certain revenue forecasting method. The purposes of thisresearch is to solve the accessibility issue and provide a scientific forecastingmethod by designing data warehouse, implementing its prototype, processing thecubes for multi dimensional analysis, providing a business intelligence dashboard,and mining the data which used in the forecasting process. This research uses datawarehouse design methodology provided by Ralph Kimball. The outcomes of thisresearch are data warehouse design and prototype, business intelligence dashboard, and local taxes revenue forecasting method to provide the information as needed by DPP. , This research addresses both local taxes information accessibility and revenue target setting issues. The accessibility issue arise from the complexity of compilingprocess since these data have to be gathered and consolidated from several sourcesacross many tax offices. Simultaneously the Local Tax Authority (Dinas PelayananPajak-DPP) has to set annual revenue target which usually derived from time seriesdata by implementing a certain revenue forecasting method. The purposes of thisresearch is to solve the accessibility issue and provide a scientific forecastingmethod by designing data warehouse, implementing its prototype, processing thecubes for multi dimensional analysis, providing a business intelligence dashboard,and mining the data which used in the forecasting process. This research uses datawarehouse design methodology provided by Ralph Kimball. The outcomes of thisresearch are data warehouse design and prototype, business intelligence dashboard, and local taxes revenue forecasting method to provide the information as needed by DPP. ]"

"The volume on Data Management, Analytics and Innovations presents the latest high-quality technical contributions and research results in the areas of data management and smart computing, big data management, artificial intelligence and data analytics along with advances in network technologies. It deals with the state-of-the-art topics and provides challenges and solutions for future development. Original, unpublished research work highlighting specific research domains from all viewpoints are contributed from scientists throughout the globe. This volume is mainly designed for professional audience, composed of researchers and practitioners in academia and industry."

"Privasi data menjadi perhatian krusial dalam lanskap bisnis saat ini, terutama dengan Big Data dan Analytics (BD&A) serta kecerdasan buatan (AI). Diulas melalui empat artikel, lanskap analitika bisnis yang terus berkembang membahas aspek sejarah, tantangan implementasi, dan perannya yang transformatif. Sambil menyoroti manfaat BD&A dan AI, esai menekankan kebutuhan mendesak akan kesadaran dan langkah-langkah proaktif untuk mengatasi isu privasi data. Esai ini menekankan dampak negatif dari pengumpulan data yang luas dan menganjurkan perlindungan informasi pribadi melalui regulasi yang ketat. Diskusinya menekankan kesiapan organisasi dan pengembangan kepemimpinan untuk mengatasi tantangan dalam adopsi BD&A sambil memastikan perlindungan data yang sensitif. Esai ini menyimpulkan dengan mengajak untuk lebih mendalami privasi data melalui studi kasus di masa depan untuk mengurangi risiko dalam penanganan informasi rahasia di lingkungan digital yang dinamis.

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Data privacy is a critical concern in today's business landscape, particularly with Big Data and Analytics (BD&A) and artificial intelligence (AI). Explored through four articles, the evolving business analytics landscape addresses historical aspects, implementation challenges, and its transformative role. While highlighting the benefits of BD&A and AI, the essay emphasizes the urgent need for awareness and proactive measures to address data privacy issues. It underscores the drawbacks of extensive data collection and advocates for safeguarding personal information through stringent regulations. The discussion stresses organizational readiness and leadership development to navigate challenges in BD&A adoption while ensuring sensitive data protection. The essay concludes by calling for deeper exploration of data privacy in future case studies to mitigate risks in handling confidential information in the dynamic digital environment."

"Penelitian ini mengangkat permasalahan mengenai belum adanya suatu bentuk pelaporan tentang status penjualan dan rincian penjualan dari sales group secara cepat dan ringkas. Tujuan penelitian ini ialah mengimplementasikan data warehouse dan aplikasi dashboard serta proses OLAP guna pelaporan status penjualan. Metodologi penelitian diawali dengan proses perumusan masalah dan pertanyaan penelitian, tinjauan pustaka, pengumpulan data, analisis kebutuhan informasi, arsitektur data warehouse, perancangan data warehouse, perancangan dashboard informasi dan ditutup dengan kesimpulan dan saran. Metodologi substansi untuk perancangan data warehouse akan mengikuti proses dari Ralph Kimball untuk industri retail. Hasil penelitian ini adalah data warehouse terhadap data penjualan lengkap dengan aplikasi pelaporan berbasis web dari proses OLAP dan aplikasi dashboard yang menampilkan perkembangan penjualan sales group dari waktu ke waktu dalam tampilan grafik.

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This study starts with the issue about the lack of the reporting form that can give information about sales status and details of sale from sales group in a fast and concise way. The purpose of this study is to implement a data warehouse and dashboard application, also with OLAP process to formulate sales status report. The research methodology in this study starts with the process of formulating of the problem and research question, literature review, data collection, needs analysis, data warehouse architecture, data warehouse design, business intelligence dashboard design and ended with conclusions and suggestions. The substance methodology for designing the data warehouse follows the process of Ralph Kimball’s data warehouse design methodology for retail industry. The result of this study are data warehouse of sales data, web-based reporting application from OLAP process and also a dashboard application that shows sales growth over time in a graphical display."