The document provides a configuration guide for setting up a custom portlet to gather individual risk information for a specific event like the COVID-19 pandemic, including creating picklists, object definitions, configuring the user interface, and setting permissions. Steps include creating picklists for different risk factors, defining custom objects to store individual and detail records, building the configuration UI, and granting roles access to manage the objects. The goal is to help organizations track individual risk and response plans related to events like the COVID-19 pandemic.
COVID-19 SAP SuccessFactors Candidate Relationship Management (CRM) Configura...Rizing HCM
Companies are facing unique and unprecedented staffing issues. Because of the current economic and social climate, there is an immediate need to reduce payroll expense, but also a critical need to maintain a bench of talented workers. When business reopens, it may not return to “business as usual” but could be slightly or dramatically different, and employers will need to respond quickly with a nimble workforce to fill these unpredictable needs.
This guide walks through the configuration of Candidate Relationship Management (CRM) features which can be used to manage the relationship with terminated employees and their rehiring as business begins to recover from an event such as the 2020 COVID-19 pandemic.
In this guide, we show an example of a healthcare facility where a nurse may have been laid off from a department which does elective surgery, but the healthcare facility may have urgent needs in other areas. We will set up a form to determine if this nurse may have skills which could be used in these other areas. As you complete this configuration for your business, you may need to change details, such as the questions asked and picklist values, to support your business and industry.
GDPR Compliance: Transparent Handing of Personally Identifiable Information i...confluent
By nature Event-driven systems transform data and propagate it across multiple services. This characteristic makes the GDPR compliance challenging. Immutable Kafka logs make it impossible to explicitly delete a published message that may contain Personally Identifiable Information (PII). A general solution has been to choose a short-enough retention duration for such topics so that the data is eventually removed within the allowed time limit. As for consumers of the data, one typically has to audit and trace where the data is propagated, and request each of the consuming services to purge their copy. Even then PII may still continue to exist, for example in backups, intermediate stating environments like S3 buckets, and ad-hoc copies of the data used for business analytics, data science, etc.
This talk presents a way to build GDPR compliance into the message propagation protocol itself, and utilise crypto-shredding to in effect render all copies of PII decipherable on demand. The talk explains how a message schema such as ProtocolBuffer can be extended to allow publishers of data to mark data as PII. It shows how GDPR compliance can be integrated into existing APIs that were not designed with GDPR in mind, with minimum disruption. It illustrates how the marked data is encrypted before it is stored in Kafka and guarantee that the data remains encrypted throughout its entire propagation journey. The talk shows how the key management system works transparently across thousands of services to control access to data with different granularity and protect against cross referencing to avoid unauthorized access to data.
COVID-19 SAP SuccessFactors Candidate Relationship Management (CRM) Configura...Rizing HCM
Companies are facing unique and unprecedented staffing issues. Because of the current economic and social climate, there is an immediate need to reduce payroll expense, but also a critical need to maintain a bench of talented workers. When business reopens, it may not return to “business as usual” but could be slightly or dramatically different, and employers will need to respond quickly with a nimble workforce to fill these unpredictable needs.
This guide walks through the configuration of Candidate Relationship Management (CRM) features which can be used to manage the relationship with terminated employees and their rehiring as business begins to recover from an event such as the 2020 COVID-19 pandemic.
In this guide, we show an example of a healthcare facility where a nurse may have been laid off from a department which does elective surgery, but the healthcare facility may have urgent needs in other areas. We will set up a form to determine if this nurse may have skills which could be used in these other areas. As you complete this configuration for your business, you may need to change details, such as the questions asked and picklist values, to support your business and industry.
GDPR Compliance: Transparent Handing of Personally Identifiable Information i...confluent
By nature Event-driven systems transform data and propagate it across multiple services. This characteristic makes the GDPR compliance challenging. Immutable Kafka logs make it impossible to explicitly delete a published message that may contain Personally Identifiable Information (PII). A general solution has been to choose a short-enough retention duration for such topics so that the data is eventually removed within the allowed time limit. As for consumers of the data, one typically has to audit and trace where the data is propagated, and request each of the consuming services to purge their copy. Even then PII may still continue to exist, for example in backups, intermediate stating environments like S3 buckets, and ad-hoc copies of the data used for business analytics, data science, etc.
This talk presents a way to build GDPR compliance into the message propagation protocol itself, and utilise crypto-shredding to in effect render all copies of PII decipherable on demand. The talk explains how a message schema such as ProtocolBuffer can be extended to allow publishers of data to mark data as PII. It shows how GDPR compliance can be integrated into existing APIs that were not designed with GDPR in mind, with minimum disruption. It illustrates how the marked data is encrypted before it is stored in Kafka and guarantee that the data remains encrypted throughout its entire propagation journey. The talk shows how the key management system works transparently across thousands of services to control access to data with different granularity and protect against cross referencing to avoid unauthorized access to data.
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