What is semantic data discovery?

Data modeling visually represents how data is sorted and connected to the real world. This is a conceptual model that can be shared across even non-technical departments and stakeholders to show them how data managed within that model will be tagged and interpreted in the data discovery process.

The purpose of a semantic data model is to allow those working with and managing data to establish a clearly defined set of shared rules and structures that everyone understands and follows. This is much the same way that established grammar rules guide us in determining how words and phrases are used together to convey meaning.

A semantic data model includes criteria for structuring data in a specific and logical way, which can vary across organizations. For instance, data models will explicitly state the relationships between and among data elements. Many semantic data models exist as an abstraction which defines how the stored datasets relate to the world at large. This can make it easier to develop application programs and streamline data maintenance while also preserving data consistency whenever sets are updated.

Whereas a semantic data model exists as a conceptual starting point, semantic data standards are the technical standards for how these datasets will be interpreted across organizations, departments, regions, or jurisdictions.

The sharing and application of these technical semantic data standards is critical to creating a successful and consistent categorization of data. These standards establish the logical parameters with which data is tagged. As opposed to a visual model, these standards are generally created within spreadsheet programs using precise technical parameters.

With the volume and types of data continuing to expand every minute, semantic data discovery gives organizations a method for leveraging the full value of their data. Semantic data technologies can help organizations uncover previously overlooked information buried in their voluminous data stockpiles.

Semantic data discovery uses advanced algorithms and machine learning to automate the process of analyzing and classifying data based on a semantic data model. A robust semantic data discovery tool can quickly scan and analyze data, including its values and characteristics. It then compares these against other metrics and proposes semantic meaning and relationships between that data and other datasets.

Semantic data discovery tools can sort data from many regions and in many languages. What results from this discovery and tagging process is metadata, which is assigned to each dataset. This metadata is then reviewed by data scientists or specialists to ensure its consistency and veracity.

Semantic data analysis refers to the process of identifying the meaning and tone of unstructured data. Consider the difficulty a computer may have interpreting the difference in meaning between the word “orange” (the color) and “orange” (the fruit).

Semantic analysis is the process of squeezing this meaning from text and correctly interpreting documents and other data sources. This analysis also includes the attribution of meaning to both signs and symbols.

There are numerous benefits to organizations which invest in the modeling and application of semantic data discovery to their datasets.

• Enhanced transparency and security. Organizations which have completed data discovery will have greater clarity as to where their data is housed and what it contains. In the event of a breach or attack, this increased transparency allows organizations to react more quickly.
• Surfacing meaning from data – for everyone. Once tagged and organized, it will be easier for everyday users, from business leaders to stakeholders, to derive meaning from data faster.
• Faster data processing and adjustment. After the completion of semantic data discovery, it becomes easier to quickly modify data flows depending on the needs of the organization.
• Improved data governance. Unifying and standardizing the way data is tagged and managed across an organization can reduce risks associated with data migration. Additionally, it can decrease duplication of efforts among teams when analyzing data relationships.

To begin the implementation of a semantic data discovery process requires following a few critical steps, starting with the definition of your organization’s needs.

1. Define your organization’s business needs and priorities. Understanding the use cases for your data or key questions it needs to address will guide how you define both the success of your effort and how data should be managed and categorized. This is the most crucial step in the process. Without a clear understanding of data use cases and objectives, the work that follows may be rendered moot.
2. Create a semantic data model that represents your datasets. Where is your data housed? At most organizations, it resides in numerous places across data warehouses and content management systems or hybrid cloud applications. An effective data model will map these source systems to give a full picture of the existing state of the data. This establishes a starting point for streamlining data across the enterprise.
3. Apply semantic standards for streamlined communications across systems.
   

   • Establish a data description framework (such as taxonomies and metadata) that adds organizational context to your data. This enables data analysts to understand the actual meaning of the data.
   • Create a set of rules to ensure that this data is not only understood by humans, but by applications, as well.
   • Use a schema (which is a logic template or framework) to map out relationships between all of the organization’s data.
4. Invest in semantic tools. Semantic data discovery tools can assist with customizing and streamlining the above process for your organization.
5. Integrate internal and external facing applications. Once this process is complete, your organization can integrate internal and external applications to continue collecting and tagging new data as well as accessing it.

Spirion offers highly accurate, automated semantic data discovery tools so enterprises can secure sensitive information at its source. We assist organizations in establishing and managing this process to ensure consistent language is used for sensitive data across all platforms and integrations involved in their security infrastructure. Spirion tags are universally readable, enhancing security and DLP strategies by eliminating confusion; systems are able to talk to each other for maximum protection.

Contact Spirion to discover more about our approach to semantic data discovery and how it can benefit your organization.