Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training Data Quality - Guaranteeing Data Reliability in 2026 training are: Advanced SQL mastery, Python for data processing, experience in data pipelines and ETL. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Data Quality - Guaranteeing Data Reliability in 2026 training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training Data Quality - Guaranteeing Data Reliab... | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

Configure your training

Response within 24hNo commitment100% free

8 spots remaining for the next session

FormatParticipantsDateDetails

Which format do you prefer?

Methods, approaches, and teaching resources

The Training Data Quality - Guaranteeing Data Reliability in 2026 training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training Data Quality - Guaranteeing Data Reliability in 2026 training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training Data Quality - Guaranteeing Data Reliability in 2026 training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Master advanced profiling techniques to assess data quality in the enterprise

Develop automated certifying data validation pipelines

Implement complex business rules for professional data governance

Optimize anomaly detection with AI for expert data quality skills

Design scalable remediation strategies in production contexts

Deploy open-source data quality frameworks in cloud environments

Evaluate the business impact of data quality for certifying projects

Training program

Module 1Data Quality Profiling: Assess Completeness, Accuracy, and Compliance (Great Expectations, SQL Profiling)

Immersion in in-depth analysis of real enterprise datasets, use of tools like Great Expectations to profile completeness, uniqueness, and validity, practical exercises on degraded data warehouse cases, automatic identification of error patterns via custom Python scripts, production of a data quality diagnostic report ready for stakeholders, with focus on key metrics for rapid decision-making.

Module 2Business Rules for Data Quality: Implement Automated Validations (Talend DQ, Custom Rules)

Design and deployment of advanced business rules tailored to your sector, hands-on with Talend Data Quality to validate formats, ranges, and inter-table consistency, collaborative workshops on real ETL flows, automation of checks via CI/CD pipelines with Soda or Deequ, tests on massive cloud data volumes, generation of interactive dashboards to monitor data quality in real time, directly enhancing operational efficiency.

Module 3Anomalies and Data Quality Remediation: AI and Machine Learning (Isolation Forest, Autoencoders)

Exploration of AI algorithms to detect outliers and drifts in datasets, practical implementation of Isolation Forest and autoencoders in Python with scikit-learn and TensorFlow, concrete cases on enterprise transactional logs, simulation of data quality degradation scenarios, development of automated remediation plans including data cleansing and enrichment, ROI evaluation via before/after metrics, turning risks into business opportunities.

Module 4Data Quality Governance: Scalable Deployment 2026 (Data Mesh, Collibra Integration)

Advanced strategies for sustainable data quality governance in 2026, integration with Data Mesh and tools like Collibra for cataloging and stewardship, exercises on AWS/GCP cloud architectures, setup of continuous monitoring with proactive alerts, final audit of a complete ongoing project, definition of data quality KPIs aligned with enterprise objectives, delivery of a personalized playbook for immediate deployment, ensuring certifying data maturity.