Network Data Sources in Organizations

In the field of knowledge management (KM), analyzing relationships within an organization is crucial to understanding how information flows, how collaboration occurs, and where knowledge is created, stored, or lost. Several types of relationships can be examined in this context:

• Formal relationships – These are defined by the organizational hierarchy and include reporting lines, departmental structures, and officially assigned roles. Analyzing these helps to understand how knowledge is supposed to flow within the company's structure.

• Informal relationships – These consist of spontaneous, unofficial interactions between employees across departments or hierarchy levels. Informal networks often reveal the true pathways of knowledge sharing, mentorship, or problem-solving.

• Collaborative and project-based relationships – These emerge when employees work together on specific tasks or projects, either within teams or cross-functionally. Understanding these relationships helps to identify key knowledge brokers and potential bottlenecks.

• Expertise networks – These networks reflect who seeks expertise from whom, showing dependencies and flows of specialized knowledge. Mapping them helps in identifying subject matter experts and critical nodes in the knowledge ecosystem.

• Social relationships – Social connections at work, including friendships or mutual trust relationships, often influence how openly and frequently knowledge is shared.

Sources of Network Data in the Enterprise

To map and analyze these relationships, network data can be collected from various internal systems and tools used daily by employees. Some of the most common sources include:

• Surveys and questionnaires
  Structured surveys can be designed to ask employees directly about their communication patterns, collaboration habits, and whom they consider as knowledge sources or influencers.

• IT communication systems
  Platforms such as:

  • Email systems – Provide metadata on frequency and volume of communication between users (without analyzing email content).

  • Microsoft Teams, Slack, Zoom – Offer insights into collaboration frequency, participation in meetings, or message exchanges within and across teams.

• HR systems
  HR databases contain organizational charts, job descriptions, team assignments, and employee roles. These can help contextualize formal relationships and compare them with actual collaboration networks.

• CRM systems
  Customer Relationship Management tools (e.g., Salesforce) can reveal how sales, support, and marketing teams interact with clients and internally coordinate knowledge about customer needs and experiences.

• Project management tools
  Platforms like Jira, Asana, or Trello track task assignments, timelines, dependencies, and collaboration patterns within projects, offering valuable insights into operational knowledge flows.

Privacy Concerns, GDPR, and the Need for Anonymization

Collecting and analyzing relational or network data within a company, while valuable, involves handling sensitive information. This brings several ethical and legal considerations:

• Personal data and GDPR compliance
  Communication logs, collaboration metadata, or survey responses often qualify as personal data under the General Data Protection Regulation (GDPR). This means:

  • Employees must be informed transparently about what data is collected, how it will be used, and for what purpose.

  • In some cases, obtaining explicit consent is required.

• Need for data anonymization or pseudonymization
  To reduce privacy risks, data should be anonymized (removing identifying details completely) or pseudonymized (masking identities while allowing data linkage through secure means). This is especially important in network visualizations or when data is shared beyond internal analytical teams.

• Risk of misuse or overreach
  There is a risk that network analysis could be used to monitor employee performance or behavior inappropriately. Clear ethical guidelines should be established to ensure that such analysis supports knowledge management and organizational improvement rather than surveillance.

• Data security and access control
  Only authorized personnel should have access to sensitive network data. Robust data governance frameworks must be implemented to protect against unauthorized access or data breaches.

Conclusion

Understanding and analyzing different types of relationships in an organization can significantly enhance knowledge management practices. By leveraging data from surveys, IT systems, HR tools, and project platforms, companies can uncover valuable insights into how knowledge is shared and applied. However, these practices must be balanced with strong privacy safeguards, compliance with GDPR, and ethical responsibility, ensuring that employee trust and legal boundaries are respected at every stage of the process.