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"Closed Circuit TeleVision (CCTV) cameras have been increasingly deployed pervasively in public spaces including retail centres and shopping malls. Intelligent video analytics aims to automatically analyze content of massive amount of public space video data and has been one of the most active areas of computer vision research in the last two decades. Current focus of video analytics research has been largely on detecting alarm events and abnormal behaviours for public safety and security applications. However, increasingly CCTV installations have also been exploited for gathering and analyzing business intelligence information, in order to enhance marketing and operational efficiency. For example, in retail environments, surveillance cameras can be utilised to collect statistical information about shopping behaviour and preference for marketing (e.g., how many people entered a shop; how many females/males or which age groups of people showed interests to a particular product; how long did they stay in the shop; and what are the frequent paths), and to measure operational efficiency for improving customer experience. Video analytics has the enormous potential for non-security oriented commercial applications. This book presents the latest developments on video analytics for business intelligence applications. It provides both academic and commercial practitioners an understanding of the state-of-the-art and a resource for potential applications and successful practice."

"Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors’ tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, big data is belittled, projects flounder, are late and go over budget. Business intelligence guidebook : from data integration to analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers.After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget, turning the deluge of data into actionable information that fuels business knowledge. Finally, you’ll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success."

"Meningkatnya kebutuhan untuk pengambilan keputusan bisnis yang efektif dan tepat waktu pada pasar yang kompetitif telah mendorong perusahaan untuk mengadopsi sistem Business Intelligence (BI). Keberhasilan adopsi teknologi BI memungkinkan organisasi memiliki efektivitas bisnis dan investasi Teknologi Informasi (TI) yang lebih baik. Pada organisasi yang baru mengadopsi teknologi BI, proyek implementasi sistem BI seringkali mengalami kegagalan. Pada organisasi yang telah mengimplementasikan sistem BI, seringkali juga gagal dalam mendapatkan manfaat penuh dari sistem BI. Pengukuran tingkat kematangan adalah suatu metode yang populer untuk mengukur sebuah organisasi melalui perspektif proses, sumber daya manusia, dan data dalam penerapan suatu sistem tertentu. Tujuan dari penelitian ini adalah untuk mengukur sejauh mana sistem BI diterapkan di sebuah perusahaan otomotif Indonesia melalui kacamata kematangan organisasi dan faktor mana yang perlu ditingkatkan. Dalam hal ini, model kematangan Business Intelligence (biMM) yang dikembangkan oleh Dinter diadopsi untuk menetapkan tingkat kematangan BI di sebuah perusahaan otomotif Indonesia. Studi ini pada akhirnya menjawab tingkat kematangan implementasi sistem BI di sebuah perusahaan otomotif di Indonesia dan faktor-faktor yang perlu ditingkatkan beserta rekomendasi terhadap hal tersebut.

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The increasing need for effective and timely business decision making in a competitive market has driven companies to adopt Business Intelligence (BI) systems. Successful adoption of BI technology enables organizations to have better business effectiveness and Information Technology (IT) investments. In organizations that have just adopted BI technology, BI system implementation projects often fail. In organizations that have implemented BI systems, they often fail to get the full benefits of BI systems. Maturity level measurement is a popular method for measuring an organization through the perspective of processes, human resources, and data in the implementation of a particular system. The purpose of this study is to measure the extent to which the BI system is implemented in an Indonesian automotive company through the lens of organizational maturity and which factors need to be improved. In this case, the Business Intelligence (biMM) maturity model developed by Dinter is adopted to determine the maturity level of BI in an Indonesian automotive company. This study ultimately answers the maturity level of BI system implementation in an automotive company in Indonesia and the factors that need to be improved along with recommendations for this."

"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."

"Asuransi merupakan industri yang berbasiskan pada layanaan produk baik asuransi jiwa maupun asuransi umum. Pembuatan produk sangat mengandalkan prediksi untuk penentuan tarif premi, khusus untuk asuransi jiwa prediksi mencakup: prediksi mortalitas, morbiditas, tingkat investasi dan biaya-biaya yang diambil dari premi yang dibayarkan nasabah. Volume data yang besar dan berada pada tempat yang terpisah menyulitkan untuk proses analisis dan membutuhkan waktu yang relatif lama. Untuk membantu melakukan analisis dan prediksi diatas salah satunya dengan menggunakan Business Intelligence yang merupakan salah satu tools untuk melakukan prediksi dibantu dengan analisis dari data mining yang sumber datanya diambil dari data warehouse berbasis model dimensional dan merupakan kumpulan data operasional yang terintegrasi. Pemodelan data warehouse sendiri tidak terlepas dari analisis proses bisnis dan analisis data operasional yang tersimpan. Untuk analisis proses bisnis digunakan tehnik value chain yang dapat menggambarkan aktifitas utama organisasi dan aktifitas pendukungnya dibantu dengan tehnik analisis balanced scorecard untuk menentukan indikator-indikator utamanya. Data yang digunakan terdiri dari analisis dimensi produk asuransi yang dijual, polis yang diterbitkan dan klaim yang terjadi, selanjutnya dibantu dengan visualisasi pola yang dihasilkan data mining dapat diambil kesimpulan mengenai pola-pola yang terjadi dimasa mendatang tentang penjualan produk, klaim dan polis yang terbit.Insurance is one of industries which is based on product services, life insurance and general insurance. Products are created rely on prediction in determining prime cost. Especially for life insurance, the prediction consists of that is contains of mortality rate, morbidity rate, investment rate and cost earned from customers prime. In insurance industry, data volume is big and located in separated systems. This condition will make product services analysis difficult and need a lot of time. To help enhance the analysis and prediction, Business Intelligence. It could be used as a tool which do the prediction with data mining analysis which source came from data warehouse. The data warehouse is based on dimentional model and it is a set of integrated operational data. Data warehouse?s model itself was unseparated from business process analysis and stored operational data analysis. The value chain technique was used to analyze business process that can describe organizations general activity and its supported activity. Balanced scorecard was used to determine the main indicators. Data used in this study came from the result of sold insurance product dimension analysis, published policy and claim. Next, data mining is used for classification visualization resulted. The result of analysis was integrated-data that was individual life, group life dan health onto data warehouse. The integrated data was classified with predictive value technique that created sold-product model, claim model, and published policy model."