Dharma Teja Atluri is an executive architect and artificial intelligence/machine learning evangelist at IBM. He has more than 18 years of experience working in advanced analytics with both SAP and non-SAP product lines. He has provided strategic direction to clients globally regarding the adoption of SAP and non-SAP advanced analytics products for artificial intelligence/machine learning operationalization, data management, information management, and analytics. He has also carried out multiple platform comparison initiatives for reporting, ETL, data warehousing, and data science products across IBM, Microsoft Azure, Google, Amazon Web Services, and SAP. He has led the SAP analytics (reporting and enterprise information management) portfolio for IBM India, and designed client architectures for analytics with SAP and IBM capabilities. Dharma is an IBM master certified data scientist, architect, and technical specialist, and also an IBM thought leader certified consultant. His most recent SAP Data Intelligence sprint was featured for global consumption by clients and nominated for SAP Innovation Awards. He can be reached at https://www.linkedin.com/in/dharma.
Devraj Bardhan is an accomplished global leader for SAP Innovations at IBM. He has led several large transformation projects, driving business growth agenda through innovation and digital efficiencies. He is an established subject matter expert (SME) for SAP S/4HANA, SAP Ariba, and SAP Business Technology Platform, growing IBM's digital transformation capability by designing and implementing global templates and intelligent workflows. He is part of the global SAP Center of Competence at IBM and works very closely with SAP incubating the next big idea. Devraj can be reached via Twitter @devbard or via www.linkedin.com/in/bardhan.
Santanu Ghosh is an SAP analytics practitioner working as a consultant for more than 15 years in the data warehouse space. He has worked with SAP Business Warehouse, SAP HANA, SAP BusinessObjects BI, and SAP Analytics Cloud. Recently, he has been working with the SAP Data Intelligence platform, delivering customer use cases and business content for the automotive industry. Santanu is a data science and machine learning enthusiast, and is pursuing a degree in data science. He has worked on multiple predictive analytics and machine learning projects over the past four years for a variety of industries, leveraging SAP Data Intelligence, SAP Predictive Analytics, SAP HANA Predictive Analysis Library, and SAP HANA Automated Predictive Library. He is also experienced in programming using Python and R languages.
Snehasish Ghosh is an enterprise information management consultant at IBM with more than 13 years of experience working with SAP’s analytics and information management product portfolio. He has extensive experience in data integration and migration, data quality, and reporting. He has implemented a variety of SAP and non-SAP solutions, including SAP S/4HANA, SAP Business Warehouse, Amazon Web Services, and Salesforce. He has also worked with SAP enterprise information management solutions, including SAP Data Intelligence, SAP Information Steward, SAP Data Services, and SAP HANA smart data integration and smart data quality.
Arindom Saha is an SAP business intelligence consultant with more than 11 years of experience working with the SAP analytics portfolio. He has extensive experience in SAP and non-SAP reporting and visualization products. He has implemented a variety of analytics solutions on different products, including SAP BusinessObjects BI, SAP HANA, SAP Business Warehouse, and Oracle RDBMS. He also has experience working with SAP Lumira, SAP Analytics Cloud, SAP Data Intelligence, and Tableau. His recent experience with SAP Data Intelligence has been contributing business content and working closely with cloud and on-premise providers to install and provision SAP Data Intelligence 3.1 as part of ongoing global initiatives.
Devraj Bardhan, Axel Baumgartl, Nga-Sze Choi, Mark Dudgeon, Piotr Górecki, Asidhara Lahiri, Bert Meijerink, Andrew Worsley-Tonks
648 pages, hardcover
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