Data Stewardship

Introduction

What is data stewardship?

Data governance has traditionally been a passive function: metadata catalogued, rules defined, and issues addressed reactively. This approach does not hold up in a modern, AI-driven environment where data moves across teams, platforms, and automated processes faster than manual oversight can follow.

Data Stewardship in Qlik Talend Cloud provides structured, sprint-based workflows that replace ad-hoc remediation with defined ownership, prioritization, and accountability. Business and data stewards collaborate to validate flagged records, apply domain-context-specific corrections, and confirm resolution — ensuring that data decisions reflect real-world business meaning, not just technical correctness; it operationalizes trust in your data.

The core concept is agentic stewardship, gated by humans: AI assists with identifying and suggesting fixes for invalid records, but human experts retain control over acceptance and re-injection. This keeps AI workflows both responsible and reliable.

Data Stewardship vs Data Pipelines

Automated data pipelines can detect that a record is invalid — a missing field, an incorrect format, a value that fails a business rule. What they cannot determine is the business impact of that invalidity, or the correct remediation. That judgment requires domain knowledge.

Consider a healthcare organisation using AI to identify high-risk patients. The system flags thousands of records with missing allergy information. Without a stewardship workflow, the question of whether those allergies are genuinely absent or simply stored in a separate system goes unanswered. A data steward reviews the situation, confirms the business impact, and decides whether it is safe to proceed. That human step is what makes downstream AI trustworthy.

Data Stewardship provides the structured mechanism for that step at scale — across any data domain, any team, and any volume of exception records.

Working with Data Stewardship

The sprint model

Data Stewardship is organised around sprints. A sprint is the primary unit of work — it contains the source data to be remediated, the validation schema, ownership assignments, workflow configuration, and the storage location for sprint data.

During a sprint, all data is stored in the customer's own cloud data warehouse, not in Qlik Talend Cloud. This keeps data residency under the customer's control and avoids unnecessary data movement. Snowflake is the currently supported cloud data warehouse for sprint storage, with additional platforms on the roadmap.

A typical sprint follows this workflow:

1. Create — The Sprint Manager (data quality manager role) configures the sprint, defines the source data input, applies validation rules and semantic types, assigns records to data stewards (manually or automatically), and sets workflow parameters including locked or hidden fields.

2. Remediate — Data stewards work through their assigned records in a spreadsheet-style interface. Each record displays validation rule violations and semantic type failures. Stewards correct invalid values and mark records as ready for validation.

3. Validate and reinject — The Sprint Owner reviews remediated records and approves or rejects them. Approved records are re-injected into the original data source or any downstream destination — with a full audit log of all changes.

User roles

Four roles participate in a stewardship sprint:

• Data Engineer — configures source pipelines and quarantines exception records for stewardship.
• Sprint Manager — creates and configures sprints, defines validation rules, manages assignments and workflow settings.
• Data Steward — resolves assigned records, working only on the records and fields they have been given access to.
• Sprint Owner — validates and approves remediated records before reinjection.

Access control operates at both the record level and the column level. Data stewards can only see and resolve the records assigned to them. The Sprint Owner can lock columns to prevent modification, and sensitive columns can be hidden entirely for privacy compliance.

Remediation modes

The current GA release supports Resolution sprints. Additional modes are on the roadmap:

Arbitration mode is particularly valuable for AI use cases — it enables data stewards to label records in response to a business question, producing labelled training data directly within the governance workflow.

Data inputs and outputs

In the current GA release, sprint data can be populated from:

• A Talend Studio Job — enabling integration with existing data pipelines for automated exception record routing and reinjection.
• A CSV upload — for ad-hoc or migration scenarios where a pipeline is not yet in place.

On completion, validated records are re-injected via a Talend Studio Job back to the original source or any downstream system, or exported as a CSV file.

Pushdown resolution with Snowflake is supported in the GA release, meaning that remediation actions are applied directly within Snowflake rather than requiring data to move into or out of Qlik Talend Cloud.

Validation rules and semantic types

Data Stewardship uses Qlik's existing validation rules and semantic types to identify invalid records and guide stewards during remediation. Rather than requiring each sprint to define its own quality criteria from scratch, stewardship reuses the rules already established in the customer's data pipeline.

Semantic types provide contextual metadata about what a field is — for example, an email address, a phone number, a date, or a national identifier. When a value fails its semantic type, the sprint surfaces this to the data steward alongside a description of the violation and, in upcoming releases, an AI-suggested correction.

Validation rules and semantic types can be supplemented with custom business rules configured by the Sprint Manager at sprint creation time.

AI-assisted remediation

Current capability

In the GA release, AI is not yet active during the remediation step. Data stewards review and correct records manually, guided by validation rule violations and semantic type information.

Roadmap: agentic stewardship

Q2 2026 — AI-assisted resolution: The AI model analyses invalid records, infers the likely correct value based on validation rules, semantic types, and context from other records in the dataset (for example, deriving the expected email format from other members of the same company), and proposes a correction. Data stewards review the suggested fix and accept or reject it. This keeps AI in the workflow without removing human judgment from the loop.

Architecture

Where data lives

A key architectural principle of Data Stewardship is that sprint data never enters Qlik Talend Cloud storage. All records being remediated are stored in the customer's own cloud data warehouse (currently Snowflake) for the duration of the sprint. This means:

• Data residency remains under the customer's control.
• No additional data movement risk is introduced by the stewardship process.
• Remediation actions (via Snowflake pushdown) are applied directly in the customer's environment.

The Data Stewardship capability is accessed from the Qlik Cloud menu and operates within the Qlik Talend Data Integration activity centre, alongside the catalog, data products, and data pipelines.

Integration with the data pipeline

Data Stewardship is designed to complement, not replace, automated data pipelines. The typical integration point is between the bronze and silver layers of a medallion architecture:

1. Source data arrives and is processed through transformation and standardisation (bronze layer).
2. A data quality check identifies exception records that fail validation rules.
3. Exception records are routed to a Data Stewardship sprint for human-in-the-loop remediation.
4. Validated records are re-injected into the silver layer (or directly to the golden layer or downstream catalog) with a full audit trail.

This positions stewardship as the human escalation path when automation reaches its limits — not a replacement for automated quality, but the structured mechanism for resolving what automation cannot.

Security and access control

Data Stewardship inherits the security model of the Qlik Talend Cloud platform. For platform-level authentication, authorization, and encryption, see the Qlik Cloud Platform Evaluation Guide.

At the sprint level, Data Stewardship provides the following access controls:

Record-level security — Data stewards can only view and modify the records assigned to them. Records assigned to other stewards are not visible. This ensures that sensitive records are handled only by the appropriate individuals.

Column-level security — The Sprint Owner can lock specific columns to prevent modification during remediation. Columns can also be hidden entirely, which is relevant for datasets containing personally identifiable information (PII) or other sensitive fields that stewards should not be able to view.

Role separation — The four user roles (Data Engineer, Sprint Manager, Data Steward, Sprint Owner) enforce separation of duties throughout the stewardship workflow. Only the Sprint Owner can approve validated records for reinjection.

Audit log — A complete log of all changes made during a sprint is maintained and travels with the re-injected records. This supports compliance requirements for regulated industries where data lineage and change history must be demonstrable.

Governance

Positioning within the governance landscape

Data Stewardship complements the broader governance capabilities of Qlik Talend Cloud rather than duplicating them. Its positioning relative to the adjacent Table Recipe capability is a useful reference:

Table Recipe is designed for point-and-click data preparation on a single table, typically by a business analyst working independently. Data Stewardship is designed for operational data quality remediation at scale, where multiple domain experts collaborate under structured governance with defined ownership and SLAs.

Connection to data products and the catalog

Data Stewardship connects to the Qlik data product and catalog ecosystem. In the Q3 2026 roadmap, the ability to send records from Qlik Catalog directly to a Data Stewardship sprint — and re-inject validated records back to a dataset or job — will close the loop between catalog-based data quality visibility and operational remediation.

Sprint activity will also appear as a node in lineage, providing end-to-end traceability from source to consumption through the stewardship step.