> ## Documentation Index
> Fetch the complete documentation index at: https://developers.techwolf.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Data Handling
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Key to any data-driven approach is to have clarity around the data and how it
is handled. This page gives an answer to several data related questions.
## Supported Data Types and Sources
The Skill Engine has the ability to process both structured and unstructured
data - that means that it is compatible with a wide range of data sources.
Below, you can find a table with possible data source types (and some examples
of these), as well as the data that can typically be used from them. The list
is non-exhaustive, so get in touch if you have questions about other options!
| Source | Example Data Types |
| ---------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
| HR Information System (e.g. SAP SuccessFactors, Oracle HCM Cloud, Workday…) | Employee resume, Employee working history, Employee education, Employee location, Team & division information |
| Learning Management System or Learing Experience Platform (e.g. Degreed, Docebo, Cornerstone…) | Available courses, Employee course history, Employee learning goals |
| Knowledge Base or Project Management Tool (e.g. Microsoft Teams…) | Issue descriptions, Articles written by employees |
| Communication Tool (e.g. Yammer, email…) | Messages exchanged |
The accepted format is detailed in the [API Specification](/reference/) -
typically, a minimal connector layer (as described on
the [architecture page](/technology/architecture)) is used to bridge the gap
between your systems and the Skill Engine API.
In addition to this fixed format, the Skill Engine API also allows you to
leverage your metadata through the custom properties system - this way,
you can filter and analyze Employees, Vacancies, and Courses based on any
variable you want. For example, you can use this system to compare the skills
of entire teams, see the distribution of a specific Skill Cluster over years of
service or age... Metadata is allowed as a string (for categorical types) or as
a number (for continuous types and quantities).
## Data Processing
The Skill Engine processes documents through a process called skill
extraction (visualized below). In this process, the Skill Engine reads
through both structured and unstructured data, interpreting the skills
connected to it and aggregating them into structured skill profiles. If skills
are the atoms, you could see skill profiles as the molecules, with the bonds
between the atoms representing the interaction between skills. Skill extraction
is carried out through artificial intelligence, depending on state-of-the-art
language models. While accurately representing the skillset of people, jobs,
and courses, skill profiles are anonymous.
## Stored Data
The Skill Engine stores your data in
a [logically separated data store](./architecture).
Data stored is defined by a crucial principle: only the information that is
needed to provide results downstream is retained. For example, resume
files, which typically contain a wide range of sensitive personal information,
are dropped immediately after their skill profile has been extracted, leaving
only a pseudonymized profile. The same holds for any other unstructured data (
e.g. documents), which are never stored inside the Skill Engine.
The metadata of an entity is retained until either the entity or the custom
property containing the metadata is removed, since the metadata must be
available for flexible queries.