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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."

"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."

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Badan Kebijakan Fiskal (BKF) sebagai unit eselon I Kementerian Keuangan yang memiliki tugas dan fungsi sebagai perumus kebijakan terkait APBN, memiliki ketergantungan yang sangat tinggi terhadap data APBN. Data APBN yang dibutuhkan oleh BKF tersebar di beberapa unit eselon I Kementerian Keuangan. Keterbatasan ini menjadikan BKF kurang maksimal dalam menghasilkan rumuskan kebijakan. Solusi pengembangan data warehouse APBN digagas untuk mengumpulkan seluruh data APBN dan menjalin sinergi antar unit eselon I Kementerian Keuangan. Metodologi pengembangan data warehouse yang digunakan adalah metodologi Kimball / bottom up. Proses pengumpulan kebutuhan didasarkan pada kebutuhan unit kerja BKF dan rencana strategis organisasi. Alat bantu penelitian adalah Microsoft SQL Data Tools yang digunakan dalam proses ETL. Hasil penelitian ini berupa data warehouse yang berisi data APBN yang digunakan sebagai bahan analisis perumusan kebijakan serta melakukan pemantauan terkait data APBN yang dimiliki Kementerian Keuangan.

 

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The Fiscal Policy Agency (FPA) as the unit of the Ministry of Finance which has function as a policy formulator related to the state budget, has a very high interest in the state budget data. State budget data needed by BKF is distributed in several units of the Ministry of Finance. These limitations make BKF less optimal in producing policy formulations. The state budget data warehouse design was initiated to collect all state budget data and establish synergy between units of the Ministry of Finance. The data warehouse development methodology used is the Kimball methodology. The process of gathering needs is based on the needs of the BKF work unit and the organization's strategic plan. The research tool is the Microsoft SQL Data Tool used for the ETL process. The results of this study are a data warehouse that contains state budget data that is used as material for policy analysis as well as conducting data related to the state budget needed by the Ministry of Finance.

 

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