How Data Catalog Automation Saves Hours of Manual Work
Manual work is still one of the biggest bottlenecks in data governance. Routine tasks may seem small on their own, but across dozens of datasets, reports, and workflows they quickly add up to hours of lost time. Data governance automation reduces this repetitive work and can save stewards up to two hours every week. With Dawiso’s customizable automation, governance processes become faster, more reliable, and far less dependent on manual updates. In this article, you will read about the automation feature and other features in Dawiso for automated data governance.
What is automated data governance?
Automated data governance is the practice of using automation to handle repetitive governance tasks. The amount of data created every day is growing at an unprecedented pace. According to the latest estimates, 402.74 million terabytes of data are created each day. This is making it harder for organizations to manage, control, and use effectively. Relying solely on manual governance processes is no longer realistic at this scale.
Traditionally, governance teams have tried to manage the challenge with checklists, approvals, and manual oversight. While necessary, these methods often created the perception that data governance is restrictive, focused more on control than on enabling value.
Much of the effort is still spent on repetitive manual work like tagging objects, assigning owners, updating workflows, or ensuring attributes are correctly maintained. These tasks are essential, but they consume valuable time and increase the risk of errors if done inconsistently.
Too often, analysts mistake being busy for being productive. Filling a day with manual checks, copy-pasting, and endless updates may look like hard work, but it doesn’t move the business forward. Real productivity in data governance comes from enabling insights, supporting decisions, and ensuring impact.
By adopting automated data governance, organizations can free data stewards and governance committees from routine updates and give them more time to focus on strategy, collaboration, and enabling data-driven decisions. The result is a governance process that is not only more reliable but also far more scalable.
The limits of manual data governance work
Every data steward knows the frustration of repetitive tasks. Tagging objects one by one. Updating workflow states manually. Assigning the right owners to dozens of terms. Chasing colleagues for approvals or compliance checks.
Individually, these tasks seem small… five minutes here, ten minutes there. But across dozens of datasets, reports, and dashboards, the hours quickly add up. Before long, routine governance work consumes a significant portion of the week.
Without automation, organizations rely heavily on people to remember and execute governance rules. This often leads to:
Inconsistent metadata updates
Delays in workflows and approvals
Repetitive checks for data quality and compliance
Reduced capacity for data-driven initiatives
Customizable automation in Dawiso
Dawiso’s automation engine is designed to eliminate repetitive tasks and enforce governance rules consistently.
Trigger or Schedule – Decide when a rule runs (e.g., event-based when an object changes, or scheduled daily at midnight).
Stream – Apply filters and conditions to target specific objects (e.g., only Business Terms in Draft state).
Action – This is where the business value lies. Define what happens next, such as updating attributes, changing workflow states, sending notifications, or copying objects across spaces.
This flexible approach ensures automation can be tailored to each organization’s governance model, saving hours every week while keeping metadata clean and reliable.
Automation in practice
You define when and how rules apply, which objects they affect, and what actions follow. Whether it’s sending notifications, updating workflow states, assigning ownership, or enforcing attribute rules, Dawiso adapts to your governance needs. Instead of a one-size-fits-all approach, you decide how automation supports your processes, making governance both scalable and tailored to your organization.
Actions:
Send notification
Workflow changes
Set (exact) value
Copy data to another space
1.) Notifications
Old way: Constantly checking for changes or sending manual reminders.
New way: Notifications are sent automatically to the right people at the right time.
Example:
When a Business Term is moved to “Review,” Dawiso automatically notifies the responsible steward for approval with a customized note.
SLA Deadline Monitoring (Financial Sector) – Financial institutions rely on timely data updates to meet internal and regulatory reporting standards (e.g., BCBS 239). Service Level Agreement (SLA) deadlines are often missed without anyone noticing, risking compliance violations. Automating SLA deadline monitoring ensures accountability and allows for quick remediation.
Transition to Review When Metadata Is Complete (Sales Strategy) – Sales teams often create and maintain strategic documents that require proper classification and review before they are finalized. But if all required metadata is not provided, the approval process can stall, leading to delays. With this automation, once all key metadata fields are completed (such as content, owner, labels, and classification), the document is automatically moved to the Review workflow state for validation.
Reopen Approved Objects After Comments or Changes – When changes to attributes of approved objects are made, or when new comments are added, the object should be reviewed again. Without automation, these updates could go unnoticed, compromising data quality or introducing undocumented changes into trusted sources.
This automation rule ensures that approved content remains trustworthy and that any changes are properly reviewed before being accepted again.
2.) Workflow changes
Old way: Manually moving objects through workflow states, one by one.
New way: Objects advance automatically when the right conditions are met.
Example:
If the description and owner fields are filled in, Dawiso moves the object from Draft to In Review without manual input.
Conditional workflows can also be automated, for example, when two reviewers approve a documentation change, the workflow automatically moves into the Approved state.
3.) Set values
Old way: Relying on everyone to apply attribute rules consistently.
New way: Attributes are updated automatically according to governance rules.
Examples:
When a data product is flagged as Obsolete, its maturity attribute changes from Production to Deprecated.
When scanning a new database, Dawiso can automatically tag all related objects (input tables, source objects) with GDPR-sensitive classification if one table contains PII.
Add Default Description – When users create new objects, they often skip documentation until much later. To prevent blank fields and improve visibility, a default description and tracking label can be applied immediately.
Ownership can be assigned by domain, ensuring all terms in a specific area automatically have the correct owner.
Internal guidelines for documentation numbering can be enforced, generating IDs such as CZ-18-08-2025-001.
4.) Copy data across spaces
Old way: Recreating the same object in multiple spaces for different teams or divisions.
New way: Objects are copied automatically wherever they are needed.
Example: When an object is created in a global space, Dawiso automatically copies it into all relevant divisional spaces to maintain consistency.
Other features in Dawiso for automated data governance
Automation in Dawiso isn’t limited to the mentioned configurable rules. Many other features across the platform also help you eliminate manual work and keep governance processes efficient. These capabilities work hand in hand with the customizable automation engine, giving you flexibility to decide how far you want automation to go.
1.) Auto-constructed interactive data lineage
Tracking lineage manually is slow, complex, and prone to mistakes. Dawiso automatically parses SQL and builds interactive lineage diagrams, giving you a complete picture of data flows without requiring manual mapping. This is especially valuable in regulated industries like finance, where lineage is critical for demonstrating compliance.
Example: If a business user wants to update a dataset but worries about downstream impact, the auto-constructed lineage shows instantly which dashboards, reports, or tables depend on it without the need to consult engineering teams.
Dawiso Data Lineage
2.) Auto-linking across documentation
Dawiso’s auto-linking feature instantly connects terms, KPIs, and objects across your documentation. Any defined term mentioned in the text is underlined and linked to its glossary entry or related object. It even recognizes variations and suggests the right link automatically.
Example: Mention a KPI in your report, and Dawiso links it to its official definition in the Business Glossary. One click gives users the full context, no manual linking required.
Auto-linking across documentation
3.) Automated data categorization in data modeling
Structuring large datasets often requires days of manual sorting. Dawiso’s AI-powered domain generation analyzes naming conventions and metadata patterns to group tables and columns into logical categories automatically.
Example: Columns like Customer_Id, ClientId, and CustomerIdentification are recognized as belonging to the “Customer ID” domain, saving man-days of manual work and ensuring consistency across the model.
4.) AI-assisted writing
Creating and maintaining documentation can be slow. Dawiso’s AI tools (Help Me Write and AI Summary) generate and refine definitions, descriptions, and summaries in natural language.
Example: Contributors can draft Business Glossary terms or report definitions instantly, overcoming writer’s block and ensuring consistent tone and style across the platform.
5.) Inheritance of relations
Ownership and stewardship don’t need to be set object by object. Dawiso allows user and object relations to cascade automatically.
Example: Assign an owner to a Business Glossary domain, and the same steward is automatically applied to all terms (child objects) under it, no manual repetition required.
6.) Bulk editing
Instead of updating items one by one, Dawiso lets you apply changes across groups of objects directly from the overview table.
Example: Select multiple terms and update their status to Approved in a single action, reducing dozens of steps to one.
7.) Smart filtering
Filters in Dawiso adapt dynamically, offering only relevant options based on previous selections.
Example: If you choose to assign ownership only within the marketing team, the next filter step will suggest only colleagues from marketing, not the entire organization.
8.) Automated post-processing after data load
After metadata is ingested, Dawiso can run automated post-processing steps to update attributes, workflows, or relations, without requiring manual review.
9.) Data Factory Automation (DFA)
For advanced scenarios, Dawiso Packages support automation at the database level. Predefined SQL scripts can be tied to data loads, triggering specific actions once ingestion completes.
Example: After new data is loaded, a script can update workflow states, populate attributes, or connect objects according to defined rules. While this requires Dawiso expertise to set up, it enables highly tailored automation directly in the database layer.
Why automation is the future of data governance
As organizations generate and manage more data than ever before, relying on manual governance processes is no longer sustainable. The more people need to remember, update, and enforce by hand, the greater the risk of inconsistency, error, and wasted time.
Automation in data governance solves this challenge by ensuring that repetitive tasks are handled reliably and at scale. From customizable rule-based workflows to auto-constructed lineage, AI-powered documentation, and bulk editing, Dawiso gives you the tools to keep governance efficient without losing control.
By reducing the burden of manual updates, automation enables analysts, stewards, and business users to focus on what truly matters: improving data quality, ensuring compliance, and delivering insights. The result is governance that is consistent, collaborative, and scalable across the organization.
With Dawiso, automation doesn’t make you “lazy”, it makes you efficient. And in data governance, efficiency is the key to scaling practices without adding unnecessary overhead.
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