Understanding Standardized Content Tagging for Intelligent Process Automation

As organizations continue to accumulate vast amounts of unstructured data from various sources, the need for effective data management and search capabilities has never been greater. Traditional approaches that rely on manual classification and metadata tagging are no longer scalable. A standardized content tagging taxonomy provides the foundation to automate key data processes like retention, search, and augmentation through intelligent agents. By understanding the types and attributes of content, organizations can more effectively govern, discover, and leverage their data assets.

Below, we explore how a content tagging taxonomy supports three core areas: data management, enterprise search, and intelligent automation. We provide an example taxonomy structure and demonstrate how cognitive technologies can automatically classify and extract insights from documents based on the defined schema. We highlight the benefits of this approach for traditional systems and next-generation intelligent applications.

Data Management

One of the most prominent challenges organizations face is effectively managing the lifecycle of their data assets according to retention and purge policies. With data spread across multiple repositories and growing exponentially yearly, humans alone can’t track, classify, and remove expired content at scale. A standardized content tagging taxonomy provides the machine-readable framework for automated data governance.

Data can be automatically classified and tagged during ingestion by defining high-level categories and document types that map to standard compliance and business needs. For example, a legal document taxonomy may include top-level tags for Contracts, Policies, and Compliance Reports with sub-types for NDAs, Service Agreements, and Data Privacy Documents. Any document that contains specific attributes like parties, dates, or confidentiality clauses can then be recognized and labeled accordingly.

With content organized according to the predefined taxonomy, retention periods and purge rules can be dynamically enforced based on tag combinations. For instance, all Service Agreements could be set to retain for seven years after expiration, while Compliance Reports remain for only three years. By understanding content semantics, these policies can be applied consistently across email archives, file shares, databases, and other silos with minimal human oversight. Over time, expired content will be automatically removed, freeing up costly storage resources, reducing legal risk, and increasing system performance.

Enterprise Search

Another area greatly enhanced by a standardized taxonomy is enterprise-wide search capabilities. By classifying documents upfront, search indexes understand intrinsic attributes that guide users directly to the most relevant information. For example, searching for “ABC Corp contract” would return only agreements between that organization and others rather than all documents that merely mention those terms.

Additional metadata extracted during the tagging process, such as parties, dates, and key terms, enrich search relevance. Cognitive technologies even summarize contents, generate topic clusters, and produce a high-dimensional embedding vector for each document to capture nuanced relationships between concepts. This level of semantic understanding allows for more precise search results across billions of documents.

Intelligent Automation

The biggest opportunity lies in leveraging a content taxonomy to power the next generation of intelligent automation solutions. By defining how different document types should be structured, cognitive agents are trained to automatically extract critical insights from any ingested content.

For instance, agents process Service Agreements according to the predefined schema would know to look for fields like Contract Number, Parties, Effective Date, Expiration Date, Payment Terms, etc, and then populate your CRM, procurement, or contract lifecycle system with this critical data without manual data entry.

Additional agents monitor for specific trigger events like approaching expiration dates to initiate renewal workflows or address non-compliance issues. The taxonomy provides a common framework for agents to interact with and leverage insights from diverse content sources for automated process orchestration.

Example Implementation

To demonstrate how this may work in practice, let’s examine a sample legal document taxonomy:

• Legal Data
  • Contracts
  • Services Agreements
  • NDAs
  • Partnership Agreements
  • Policies
  • Data Privacy Policies
  • Information Security Policies
  • Compliance Reports
  • Audit Reports
  • Risk Assessment Reports

Now consider a scanned paper contract ingested into the system. Using optical character recognition, it is converted to machine-readable text. The cognitive engine analyzes language patterns and structural elements to recognize it as a legal document containing specific fields like parties, dates, and confidentiality clauses.

It is automatically classified under the “Legal” top-level tag with a sub-type of “Service Agreement.”  Key entities, such as company names, individuals, locations, and dates, are extracted.  The full text is indexed, while specific fields are structured to populate a CRM. An agent monitoring for expiring contracts sees this one will end in 6 months and schedules renewal reminders.

Users can now find this agreement between the two parties for enterprise search by filtering for the extracted metadata rather than full-text search. The taxonomy allows for more precise classification, retrieval, and automated actions on the content over its entire lifecycle.

Automation today

As organizational data volume and sources continue diversifying, traditional approaches are no longer feasible for effective governance, discovery, and use. A standardized content tagging taxonomy provides the schema for cognitive technologies to understand, process, and leverage this information at scale through machine-readable structures.

Critical data management functions around retention, search, and intelligent automation are achieved by classifying content upfront according to expected document types and attributes. This reduces costs while improving compliance, discovery, and insights. The taxonomy serves as a common framework for both humans and intelligent agents to interact with diverse information sources in a consistent, semantically aware manner. Overall, it establishes the machine-readable foundation for next-generation data-driven organizations.