Data Governance Course in Insurance Companies
Online 300-hour course in data governance in insurance companies. 6 modules covering ISO 8000 (data quality), ISO/IEC 38505-1 (data governance) and Collibra. Assigned tutor and enrolment open.
Take the controls.
Run a real engineering trade-off from this programme. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Recognise data as a strategic asset of the insurer.
Build dictionaries, lineage and metadata for critical data.
Measure and improve data quality through metrics and controls.
Apply protection, access and regulatory compliance requirements.
Design the architecture and operations of data management.
Drive the culture and implementation of data governance.
Develop a final project on a data governance model.
Who it's for
- Chief Data Officer who wants to specialise in data governance in insurance companies.
- Data governance manager who wants to specialise in data governance in insurance companies.
- Data steward who wants to specialise in data governance in insurance companies.
- Data architect who wants to specialise in data governance in insurance companies.
- Data quality analyst who wants to specialise in data governance in insurance companies.
- Data strategy consultant who wants to specialise in data governance in insurance companies.
- Graduates in engineering or sciences who want to join insurance undertakings.
What you'll work with
- ISO 8000 (data quality)
- ISO/IEC 38505-1 (data governance)
- Directive 2009/138/EC (Solvency II, data quality)
- Regulation (EU) 2022/2554 (DORA)
- Regulation (EU) 2016/679 (GDPR)
- ISO/IEC 27001 (information security)
- BCBS 239 (risk data aggregation principles)
6 modules · 300 h
1.1Data domains: customer, policy, claim, asset and channel1.2Source systems and their historical fragmentation1.3The real cost of poor data quality1.4Which decisions are made today with unreliable data1.5Roles: data owner, custodian and consumer1.6Data committees and their place in the organisation's governance1.7Data governance maturity: levels and assessment1.8Relationship with model and technology governance1.9Business case for a data governance programme1.10Workshop: maturity assessment of an organisation
2.1Business glossary and a single definition of each term2.2Technical dictionary and mapping to the glossary2.3Data catalogue and discovery2.4Lineage: where each figure comes from and how it flows2.5Operational, business and technical metadata2.6Certified indicators and their owner2.7Documentation of transformations and rules2.8Catalogue tools and selection criteria2.9Maintaining the catalogue over time2.10Practice: lineage record for a key indicator
3.1Dimensions: accuracy, completeness, consistency, timeliness and uniqueness3.2Defining data quality rules by domain3.3Data profiling and anomaly detection3.4Data quality dashboard and alert thresholds3.5Management and resolution of quality incidents3.6Reconciliation with accounting and period-end closes3.7Customer deduplication and identity management3.8Third-party data and quality control3.9Improvement at source versus correction at destination3.10Practice: set of data quality rules for policies
4.1Information classification and sensitivity levels4.2Role-based access management and least privilege4.3Pseudonymisation and anonymisation: the limits of each4.4Development environments and test data4.5Retention and deletion: policy and execution4.6Record of processing activities and data subject rights4.7International transfers and cloud providers4.8Sector compliance and supervisory expectations4.9Data breach management4.10Practice: classification and access matrix
5.1Warehouse, lake and domain-based architecture5.2Batch and event-based integration5.3Semantic layer and reuse of definitions5.4Observability of data pipelines5.5Data versioning and reproducibility of calculations5.6Master data: customer, product and supplier5.7Platform cost and optimisation5.8Data for models: feature store5.9Technical documentation and handover to operations5.10Practice: design of a data domain flow
6.1Roadmap by domain and prioritisation6.2Executive sponsorship and change management6.3Training and data literacy6.4Incentives and owner accountability6.5Programme measurement: indicators and return6.6Relationship with internal audit6.7Common mistakes in data governance programmes6.8Sustainability of the programme beyond the project6.9Evolution towards advanced use of data and artificial intelligence6.10Final project: data governance framework for an insurer
How you'll learn
Fully 100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine the programme with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Collibra, Microsoft Purview, Informatica, SQL, Python (pandas), in free, academic or evaluation versions. Practicals cover real procedures for building a data dictionary and lineage, data quality assessments, and the design of role and ownership models. Assessment is continuous across each module, plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Recognise data as a strategic asset of the insurer.
Build dictionaries, lineage and metadata for critical data.
Measure and improve data quality through metrics and controls.
Apply protection, access and regulatory compliance requirements.
Design the architecture and operations of data management.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient Spanish to follow technical regulatory reading.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme comprises 300 hours in total, spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
The programme uses Collibra, Microsoft Purview, Informatica, SQL, Python (pandas). Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the integrating project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area can be credited towards the advanced programme. Please check the credit transfer policy when you enrol.
Related programmes
EN 4179 certification course
The EN 4179 Certification Course is an essential training program in the aerospace industry, focused on the qualification and certification of personnel for non-destructive testing (NDT).…
Course in Modal Analysis of Naval Hulls
Online 300-hour course in modal analysis of naval hulls. 6 modules with ISO 7626-1 Vibration and shock — Experimental determination of mechanical mobility, ISO 20283-2 Measurement…
Course in Suspension for Classic Motorcycles
The Course in Electronic Management for Sports Motorcycles explores the administration and tuning of advanced electronic systems in high-performance motorcycles. It focuses on understanding the ECU…
More in Aerospace, UAM, Drones & Space
All Aerospace, UAM, Drones & Space programmes → · How to apply · Fees & financing · Scholarships