KuppingerCole's Advisory stands out due to our regular communication with vendors and key clients, providing us with in-depth insight into the issues and knowledge required to address real-world challenges.
Compare solution offerings and follow predefined best practices or adapt them to the individual requirements of your company.
Meet our team of analysts and advisors who are highly skilled and experienced professionals dedicated to helping you make informed decisions and achieve your goals.
Meet our business team committed to helping you achieve success. We understand that running a business can be challenging, but with the right team in your corner, anything is possible.
When we look at common identity management deployments, one of the very typical recurring challenges we observe in these it's the quality of identity information. Data is incomplete. Data is inconsistent and redundant, and it's hard to get a crib on data. So virtually all. So we have in the space of what is called IGA, identity governance, adminis restoration support, some mapping of data and some optimization, but they frequently are very limited.
And thus there's increasingly the idea and the implementation of data integration platforms for the purposes of identity management, between the sources of data such as HR systems. So the human resources systems or HCM human capital management systems or directories in other data sources. So there might be data sources where you manage your business partners and your suppliers they're made by data sources where you have customers in or where you have robots in for robotic process automation.
So where you manage different types of identities and various components within identity and access management, such the directory service such as the I G tool, the identity governance and administration tool, data integration platforms usually are built for more general purpose data integration. Formally they have frequently known as ETL platforms, ETL sense for extract extract data from the source, transform changes and load it into a target data. Integration is the more modern name, but an ETL describes pretty precisely what the entire thing is about. So it really supports it.
And for instance, transformation in switching the last name and first name you might have specifically in global organizations, you might have some data coming in with last name. First name are switched because this is common in certain geographies, it might be modifying data. So phone numbers frequently are storing so many different formats. And it's also about finally complimenting. So it might be that you trigger for instance of workflow involving humans. So what are the main capabilities here?
The main capabilities we see for identity data into creation is the ability to import data from a broad variety of source systems is including, but not limited to HR systems. HR is a very common source and many organizations, specifically, large organizations have a lot of HR systems with data can come from. So they are facing a major challenge here. It is very extended and elaborated capabilities for mapping data. It's about also comprehensive capabilities for transformation and modification of data. So how can you do that based on rules based on even programmatic access.
So the workflows as well for involving humans and improving data quality are an important capability and all this, as I've already touched controlled by rules that you user configure or by Promatic modification and transformation, whatever you need. And they should come with a flexible set of connectors for interfacing with both the source and the target systems.
