%0 Conference Paper %1 10.1007/978-3-031-14343-4_16 %A Ehrlinger, Lisa %A Lettner, Christian %A Fragner, Werner %A Gsellmann, Günter %A Nestelberger, Susanne %A Rauchenzauner, Franz %A Schützeneder, Stefan %A Tiefengrabner, Martin %A Zeindl, Jürgen %B Database and Expert Systems Applications - DEXA 2022 Workshops %C Cham %D 2022 %E Kotsis, Gabriele %E Tjoa, A. Min %E Khalil, Ismail %E Moser, Bernhard %E Taudes, Alfred %E Mashkoor, Atif %E Sametinger, Johannes %E Martinez-Gil, Jorge %E Sobieczky, Florian %E Fischer, Lukas %E Ramler, Rudolf %E Khan, Maqbool %E Czech, Gerald %I Springer International Publishing %K applications data industrial integration management mappings %P 167--178 %R https://doi.org/10.1007/978-3-031-14343-4_16 %T Data Integration, Management, and Quality: From Basic Research to Industrial Application %U https://link.springer.com/chapter/10.1007/978-3-031-14343-4_16 %X Data integration, data management, and data quality assurance are essential tasks in any data science project. However, these tasks are often not treated with the same priority as core data analytics tasks, such as the training of statistical models. One reason is that data analytics generate directly reportable results and data management is only the precondition without clear notion about its corporate value. Yet, the success of both aspects is strongly connected and in practice many data science projects fail since too little emphasis is put on the integration, management, and quality assurance of the data to be analyzed. %@ 978-3-031-14343-4

@inproceedings{10.1007/978-3-031-14343-4_16, abstract = {Data integration, data management, and data quality assurance are essential tasks in any data science project. However, these tasks are often not treated with the same priority as core data analytics tasks, such as the training of statistical models. One reason is that data analytics generate directly reportable results and data management is only the precondition without clear notion about its corporate value. Yet, the success of both aspects is strongly connected and in practice many data science projects fail since too little emphasis is put on the integration, management, and quality assurance of the data to be analyzed.}, added-at = {2023-04-26T08:22:17.000+0200}, address = {Cham}, author = {Ehrlinger, Lisa and Lettner, Christian and Fragner, Werner and Gsellmann, G{\"u}nter and Nestelberger, Susanne and Rauchenzauner, Franz and Sch{\"u}tzeneder, Stefan and Tiefengrabner, Martin and Zeindl, J{\"u}rgen}, biburl = {https://www.bibsonomy.org/bibtex/2835a0e0b2d6772bf91189552fec1e800/scch}, booktitle = {Database and Expert Systems Applications - DEXA 2022 Workshops}, doi = {https://doi.org/10.1007/978-3-031-14343-4_16}, editor = {Kotsis, Gabriele and Tjoa, A. Min and Khalil, Ismail and Moser, Bernhard and Taudes, Alfred and Mashkoor, Atif and Sametinger, Johannes and Martinez-Gil, Jorge and Sobieczky, Florian and Fischer, Lukas and Ramler, Rudolf and Khan, Maqbool and Czech, Gerald}, interhash = {666a653afd050a19d60cfb9e58e5e575}, intrahash = {835a0e0b2d6772bf91189552fec1e800}, isbn = {978-3-031-14343-4}, keywords = {applications data industrial integration management mappings}, pages = {167--178}, publisher = {Springer International Publishing}, timestamp = {2023-04-26T08:22:17.000+0200}, title = {Data Integration, Management, and Quality: From Basic Research to Industrial Application}, url = {https://link.springer.com/chapter/10.1007/978-3-031-14343-4_16}, year = 2022 }