The client, an independent investment firm, relied on numerous systems and managed vast data sources for investment planning, modeling, and stakeholder management. These data sources included both internal and external data. However, challenges related to data consistency, accuracy, and compliance arose, leading to concerns among the leadership regarding the quality of the data they relied upon.

The client faced several challenges related to data quality:

  • Customer information across multiple systems: Customer data was scattered across various systems, leading to concerns about consistency and conformity issues.
  • Accuracy issues with external data: The ingestion of external data from different agencies resulted in occasional inaccuracies, creating operational disruptions and challenges.
  • Compliance concerns: Inaccurate data raised compliance concerns, posing risks to regulatory requirements and the firm’s reputation.


To address these challenges and enhance data quality, the following solutions were implemented:

  • Routine as a central data quality engine: Routine, an AI-powered data quality solution, was implemented as a central engine to continuously identify data issues across all data sources. It triggered alerts based on predefined thresholds, enabling proactive identification and resolution of data issues.
  • Routine as the gatekeeper for external data sources: Routine was enabled as the gatekeeper for all external data sources being ingested into the internal systems. This allowed for data validation, verification, and cleansing before the data was utilized, ensuring high-quality and accurate information.

Discovery – A comprehensive assessment was conducted on various data sources, including CRM, portfolio management, investment planning, and compliance management. Risks, issues, and anomalies were identified. Custom rules and cross-system matching techniques were implemented in Routine to mitigate known issues, ensure data consistency, and enhance the user experience.

Continuous Monitoring – Setup Monitoring of Data: Routine was used to set up continuous monitoring of incoming and outgoing data. This allowed for the identification of data issues before ingesting the data internally or sending it out externally. The trading and compliance teams were involved in the monitoring process, ensuring data accuracy and compliance.

Transformation – Enable Data Movement: Routine’s ETL (Extract, Transform, Load) capabilities were leveraged to enable the movement of data from one system to another. This facilitated the validation and transfer of customer data between transactional and planning systems to the CRM. Additionally, it enabled the integration of the HRMS system with the CRM system for seamless data flow and accurate stakeholder reporting.


The implementation of Routine and data governance solutions led to significant improvements for the independent investment firm:

  • Enhanced Data Quality: Routine’s continuous monitoring and identification of data issues improved data accuracy, consistency, and conformity across systems. This mitigated risks, improved decision-making, and ensured compliance with regulatory requirements.
  • Streamlined Data Processes: The use of Routine as a central data quality engine and gatekeeper for external data sources streamlined data ingestion and reduced the potential for operational disruptions caused by inaccurate data. This enhanced efficiency and productivity within the organization.
  • Improved Stakeholder Reporting: By enabling the movement of data between systems and ensuring data accuracy, the firm was able to generate more accurate and comprehensive stakeholder reports, enhancing the overall stakeholder experience.

The implementation of Routine and the data governance solutions helped the independent investment firm enhance data quality, mitigate risks, ensure compliance, and streamline their data processes. This laid the foundation for improved decision-making, operational efficiency, and stakeholder satisfaction.

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