• Ready for review

    • ORIEL-TIS BRIEFING (FEB 2024)

    Owned by Stan Ewenike (Unlicensed), created
    Last updated: 21 Feb 2024
    3 min read

    This briefing presents a synthesis of the Oriel-TIS integration, encapsulating the challenges and delineating strategic pathways for enhancement. It reflects on the necessity to harmonize automation with human oversight, uphold data integrity, and reconcile varying local office practices.

    1  Process Overview | 2 High Level As-Is Activity Flow | 3 Pain Points | 4 Options For Improvement | 5  Recommendations | 6  Future Considerations | 7  Next steps | 8  Conclusion

     Process Overview

    The Oriel-TIS integration entails manual data handling across diverse local offices, with discrepancies in practices leading to inefficiencies. Attempts at process streamlining and automation varies by region, and the manual mapping to TIS is a significant challenge.

     

    High Level As-Is Activity Flow

    The integration is characterized by the extraction and processing of reports, with noted pain points including access constraints, manual record creation, and TIS template user-unfriendliness. Data mapping and accuracy are hampered by inconsistencies in terminologies and data structure changes.

     

    Pain Points

    1. Manual processes: Manual data entry and upload to TIS are prone to errors and inefficient

    2. Complicated data mapping: Discrepancies in data language, terminology, and field structure between Oriel and TIS complicate direct mapping. Data integrity comes into question

    3. Absence of direct data mapping: Lack of direct data mapping between Oriel and TIS, particularly for post and programme details causes high manual workload, leading to delayed or near-deadline processing

    4. Data mapping disruptions: Changes in Oriel's field structure over time create data mapping iconsistencies

    5. Access to data: Limited superuser privileges in Oriel creates bottlenecks

    6. Template Unfriendliness: TIS templates are not user-friendly, hindering the record creation process

    7. Data quality: Data integrity is threatened by manual processing and misaligned terminologies

     

    Options For Improvement

    1. Do nothing: maintain status quo. Not very nice!

    2. Enhanced ETL: Improving ETL processes to efficiently handle and store Oriel data in a query-able format for easy querying. Create subset of relevant data for TIS, enriched with timestamps for change tracking.

    3. Data mapping: Developing automated mapping tools to reduce manual data handling.

    4. Data integration: Developing a more robust data integration process to ensure that programme and curriculum details are accurately mapped from Oriel to TIS.

    5. Integrated Process/System Development: Using Oriel procurement to create a more integrated system for data flow between Oriel and TIS.

    6. Tableau integration: Exploring the use of Tableau for data management and quality tracking. Using Tableau to automate data splitting for different audiences and to create reports to map Oriel data to TIS bulk upload format, identify and track upload issues, and data quality

    7. Alignment: Aligning Lists of Values (LoV) to minimize manual data matching efforts, by ensuring Oriel and TIS use the same lists of values for fields.

    8. Standardization: Standardizing program names and numbers across systems for better alignment.

    9. Automation: Implementing full or semi-automated data ingestion from Oriel to TIS. This can streamline the process and reduce manual errors.

     

     Recommendations

    1. Progressive automation: Progressively automate the most labour-intensive tasks, such as data field mapping and person record creation.

    2. Stakeholder involvement: Foster stakeholder involvement to tailor solutions to local office needs

    3. Data quality: Prioritize data validation and data quality checks to ensure integrity inaccuracies

    4. Incremental improvement: Commit to a phased implementation, allowing for iterative testing, feedback, and adjustment, whilst minimizing disruptions

    5. Training and support: Pursue comprehensive training and support for HEE Admins and local office users on the new system and processes to ease the transition.

    6. API development: Explore development of an Oriel – TIS API for real-time data exchange and updates

    7. Change management: Implement a change management plan to address the industry-wide impact of the onboarding process. Consider collaborative tooling to allow local offices to add their input directly, improving the alignment and accuracy of data.

     

     Future Considerations

    1. Continuous stakeholder engagement to align with local practices

    2. Commitment to data governance for managing access, quality, and integrity

    3. Consideration of a more integrated Oriel-TIS system for seamless data flow

    4. Possible data gaps and how to address missing programme information

     

     Next steps

    Set a status

     

     Conclusion

    Given the complexity and the scale of the manual efforts currently required, there is a strong case for incremental automation, focusing first on the most time-consuming and error-prone tasks, such as the manual mapping of data fields and the creation of person records. Considerations for moving forward should include stakeholder engagement to ensure the solutions meet local office needs, investing in change management and training, and establishing feedback mechanisms to refine the processes based on use.

    The success of this work will rely heavily on clear communication, proper training, and a phased approach that allows for gradual adaptation by the local offices. The development of comprehensive documentation and user guides will also be critical. It would also be advisable to create a detailed timeline with milestones for each phase to track progress and manage expectations

    Slack: https://hee-nhs-tis.slack.com/
    Jira issues: https://hee-tis.atlassian.net/issues/?filter=14213