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Abstrak :
Information management ; gaining a competitive advantage with data is about making smart decisions to make the most of company information. Expert author William McKnight develops the value proposition for information in the enterprise and succinctly outlines the numerous forms of data storage. Information Management will enlighten you, challenge your preconceived notions, and help activate information in the enterprise. Get the big picture on managing data so that your team can make smart decisions by understanding how everything from workload allocation to data stores fits together. The practical, hands-on guidance in this book includes : Part 1: The importance of information management and analytics to business, and how data warehouses are used. Part 2: The technologies and data that advance an organization, and extend data warehouses and related functionality. Part 3: Big Data and NoSQL, and how technologies like Hadoop enable management of new forms of data. Part 4: Pulls it all together, while addressing topics of agile development, modern business intelligence, and organizational change management.

Abstrak :
In line with rapid business process digitalization in the Directorate General of Taxes, the size of the data stored in the institution has grown exponentially. However, there is a problem with generating value out of the valuable data assets. Correspondingly, this research provides machine-learning-based predictive analytics as a solution to the question of how to use taxpayers' trigger data as a decision support system to discover and realize unexplored tax potential. More specifically, this research presents predictive analytics models that can accurately predict which potential taxpayers are likely to pay their due. We developed three machine learning models: logistic regression, random forest, and decision tree. We analyzed 5,562 tax revenue potential data samples with eight predictors: trigger data nominal value, distance to tax office, type of taxpayer, media of tax report, type of tax, report status, registered year of taxpayer, and area coverage. Our study shows that the random forest model provided the best prediction performance. The resultant weight of each attribute indicated that the status of the tax report was the top tier of variable importance in predicting tax revenue potential. The analytics can help tax officers determine potential taxpayers with the highest likelihood to pay their due. Given the size of the data records, this approach can provide tax administrators with a powerful tool to increase work efficiency, combat tax evasion, and provide better customer service.

Abstrak :
The book includes selected high-quality research papers presented at the Third International Congress on Information and Communication Technology held at Brunel University, London on February 27–28, 2018. It discusses emerging topics pertaining to information and communication technology (ICT) for managerial applications, e-governance, e-agriculture, e-education and computing technologies, the Internet of Things (IOT), and e-mining. Written by experts and researchers working on ICT, the book is suitable for new researchers involved in advanced studies.