KICKer (Keel Intelligent Classification Kit), AI- and ML- based product, nominated for the 2020 SAP Innovation Awards
Data Classification is a complex, scrutinized, time- and resource-consuming process that involves the development of effective and systematic workflow. In this post, you will learn how we automated this process, which helped us to overcome many obstacles, save time and ensure its successful realization.
Data governance, ERP system, Big Data, Automation, Machine Learning (ML) and Artificial Intelligence (AI) – all these terms are brought together into Keel Intelligent Classification Kit (KICKer) application.
KICKer was developed in SAP Cloud Platform and was extended to the SAP S/4 HANA. It is easy to customize this web application to any custom requirements thanks to the usage of SAP Cloud Platform cockpit and SAPUI5 Library. The following scheme illustrates the architecture of the application.
KICKer effortlessly classifies unstructured material data in a matter of seconds.
High-quality training datasets allow the application to quickly and precisely determine and assign a classification tag to a set of components. Once this is completed, it instantly updates the data in SAP. With the accumulation of correct data, the results increase in their reliability. As a result, more time can be spent on verification of the classification accuracy. Shown below is the screenshot of the application.
To sum up, the automatic classification eliminates human error and outperforms the manual work thanks to its efficacy, speed and real-time synchronization with SAP.
Thanks to SAP advanced technologies and tools, we are able to build applications and useful solutions that enable smooth business processes running.
Today, we are proud to be nominated for the 2020 SAP Innovation Awards. It means that our solution has a great potential of helping more companies with their data classification processes. Find out more in our pitch deck: https://www.sap.com/idea-place/sap-innovation-awards/submission-details-2020.html?idea_id=1238&infl=afd6db27-d1d4-4517-88e0-bc5bed35153a