Cross-Organizational Recommendations

Cross-organizational recommendation generation and privacy protection in Orthogramic

In modern enterprises, aligning strategic goals with actionable recommendations is critical for sustained success. Orthogramic provides a sophisticated recommendation engine that uses business architecture data to generate personalised, relevant, and prioritised recommendations for users across different organisations. While these recommendations are tailored to the unique context of each user and their organisation, comparing the effectiveness of recommendations between organisations offers an additional layer of insight. This cross-organisational comparison helps Orthogramic improve its recommendation system by identifying patterns and best practices that can be replicated across different sectors and industries.

However, as we enhance the recommendation engine's ability to compare data across organisations, protecting the privacy of each organisation is paramount. This document outlines how recommendations are generated between organisations and the measures taken to ensure the privacy and confidentiality of each organisation's data.

How recommendations are made across organisations

Orthogramic uses a data-driven approach to create and prioritise recommendations for users within an organisation. These recommendations are based on various factors such as strategic alignment, urgency, impact, resource availability, and user interaction patterns. When comparing the effectiveness of these recommendations across organisations, the system focuses on aggregated data rather than exposing any sensitive or proprietary information from one organisation to another.

Individual recommendations within an organisation

Each recommendation provided to a user is tailored based on their role, behaviour, and the strategic objectives of their organisation. Orthogramic’s recommendation engine evaluates several factors:

• Strategic alignment: How closely a recommendation aligns with the overall goals of the organisation.

• Urgency: The time-sensitive nature of the task at hand.

• Impact: The potential for the recommendation to contribute to the organisation’s success.

• Resource availability: Whether the necessary resources (financial, technological, human) are available to execute the recommendation.

• User behaviour: How the user typically responds to recommendations, including their speed of action and past engagement.

These factors are calculated to provide a prioritised list of recommendations that are both actionable and relevant to the user and their organisation.

Aggregating data across organisations

After recommendations are provided, Orthogramic tracks the outcomes, including whether the recommendations led to successful actions or results. This information is aggregated to assess the overall effectiveness of the recommendations.

For example, Organisation A might achieve a 75% success rate on recommendations related to operational efficiency, while Organisation B might achieve an 85% success rate on recommendations related to capability development. By aggregating these success rates, Orthogramic can refine the recommendation engine to offer more tailored guidance based on what has worked best for other organisations.

Important note: During this aggregation, Orthogramic does not share specific recommendations or results between organisations. Each organisation’s data is kept strictly separate, and the comparison focuses on anonymous, aggregated metrics like success rates and average impact scores.

Cross-organisational insights

Orthogramic enables cross-organisational insights without exposing sensitive data. These insights allow organisations to benefit from collective learning while maintaining the confidentiality of their unique business operations.

The process for generating cross-organisational insights includes:

• Aggregated metrics: Orthogramic calculates averages across multiple organisations to determine overall trends in recommendation success. For example, it might track the average success rate of all recommendations related to a specific business function, such as technology adoption or talent management.

• Best practices identification: By analysing successful recommendations across different organisations, Orthogramic identifies best practices. These best practices are then incorporated into the recommendation engine, allowing users to benefit from the lessons learned by other organisations without compromising privacy.

• Industry-specific adjustments: In some cases, industry-specific insights are used to tailor recommendations. For example, organisations in healthcare may receive different recommendations than those in manufacturing, but the underlying data is anonymised and aggregated across industries to protect privacy.

Privacy protection measures

While Orthogramic aims to provide the best possible recommendations through cross-organisational insights, ensuring data privacy and security is a top priority. Here’s how we protect the privacy of each organisation during the recommendation process:

Data anonymisation

When comparing recommendation outcomes between organisations, all identifiable data is anonymised. No organisation can view another organisation’s specific recommendations, performance metrics, or user interactions. Instead, the data is aggregated and anonymised, allowing Orthogramic to provide insights without exposing any sensitive or proprietary information.

Data segmentation

Each organisation’s data is stored in a separate, secure environment. This ensures that data from Organisation A cannot be accessed by Organisation B, and vice versa. The data is segmented at the database level, and strict access controls are enforced to prevent unauthorised access.

Role-based access control (RBAC)

Orthogramic implements role-based access control to ensure that only authorised users can access sensitive information. Users are granted access based on their role within the organisation, and they can only view recommendations and data that are relevant to their position. For example, a Chief Operating Officer (COO) may have access to higher-level strategic recommendations, while a department manager may only view operational recommendations.

Encryption

All data in Orthogramic, including recommendations and user behaviour data, is encrypted both at rest and in transit. This ensures that even if data were to be intercepted, it would remain unreadable without the appropriate decryption keys.

Compliance with privacy regulations

Orthogramic complies with industry-standard privacy regulations such as GDPR and HIPAA. This ensures that organisations using the platform can trust that their data is being handled in accordance with the highest standards of privacy protection.

Conclusion

Orthogramic provides a robust recommendation engine that leverages cross-organisational insights to improve the quality of recommendations for all users. While the system aggregates data to identify best practices and refine recommendations, it does so in a way that fully protects the privacy of each organisation. By anonymising data, segmenting storage, implementing role-based access control, and ensuring encryption, Orthogramic maintains a high standard of privacy and security, allowing organisations to benefit from collective insights without compromising their confidentiality.