Keep your data catalog up to date by removing repetitive maintenance, not by adding more stewards. dScribe is a self-driven data catalog: it gathers metadata and uses smart suggestions to reduce manual documentation and linking. Your team reviews the suggestions and decides what the data means, rather than building and maintaining everything by hand.
Why most data catalogs are outdated after a year
The usual response is to add more features and more stewards. A better starting point is to remove the repetitive work that makes the catalog hard to maintain. Copying metadata, writing every description from scratch and finding links between terms and reports should not consume your team’s time.
For example, a project team documents thousands of tables at launch. A year later, columns have been renamed, an owner has left, and half the descriptions refer to a source that no longer exists. The first user to notice will not come back.
The problem is not only who owns maintenance, but how much work maintenance requires. dScribe’s self-driven approach combines metadata collection with suggestions for human review. Instead of keeping a separate inventory and documenting every connection by hand, your team works from the metadata and suggestions already in the catalog.
What you can automate, and what you cannot
dScribe separates work the catalog can do from decisions people need to make. Metadata comes from connected sources, while smart suggestions help with definitions, duplicates and links. People still decide what a KPI means and review the proposed context, but they do not have to start from a blank page.
| Type of metadata | How it is collected | Who maintains it |
|---|---|---|
| Tables, columns, data types | Automatically, through a source connector | No one, it refreshes automatically |
| Lineage from source to report | Mostly automatically, where the sources expose it | The data team fills in the gaps |
| Usage: which reports are opened | Automatically, from platform logs | No one |
| Field descriptions | AI can write a first draft | A person approves it |
| KPI definitions and owners | Partly manual, with suggestions for definitions and links to reports | The owner of the metric |
The principle is simple: let the catalog do the collection and preparation. Use your team’s time to confirm, correct and resolve business questions, not to copy information or recreate links.
Start with what people read, not everything that exists
Being comprehensive feels responsible, but it is the fastest route to an outdated catalog. Every table you document is another table you have to maintain.
Start by looking at usage. Which Power BI reports are opened every week? Which KPIs come up in executive committee meetings? Start there. A staging table that no one reads can remain undocumented until someone asks about it.
In practice, this narrows your initial scope to a few reports and the KPIs they contain. That is a few weeks of work, not a multiyear project.
Give maintenance to the owner, not a separate team
A small team of data stewards can never keep every definition in a large company up to date. They do not know the business well enough, and they do not hear when something changes.
The owner of a metric does. If Finance changes the definition of gross margin, the financial controller is the first to know. Make that person the owner of the definition in the catalog. A steward provides guidance, ensures agreed standards are followed and picks up anything that falls through the cracks.
This only works if the owner has less work to do, not just another task list. With dScribe’s smart suggestions, the starting point is a proposed definition or link to review. The owner confirms or adjusts it, rather than having to write and connect everything from scratch.
Make inaccuracies visible
An outdated definition that no one notices is more dangerous than a missing one. Give every definition an owner and a date showing when it was last confirmed.
Show definitions where people work, too: in the report, next to the number. Someone who spots an incorrect definition will report it. A definition hidden away in a separate portal never gets corrected because no one reads it.
A practical framework: Automate, Choose, Assign, Show, Review
- Automate: bring in tables, columns, lineage and usage automatically from your sources.
- Choose: document the reports and KPIs people actually read first.
- Assign: give each definition one named owner, someone from the business, not a team.
- Show: display definitions and owners in the report, where errors get noticed.
- Review: have the owner confirm each definition at regular intervals, for example quarterly.
How dScribe removes manual catalog work
dScribe is a self-driven data catalog, not simply a place to store documentation your team has written. It crawls metadata to build an inventory and uses smart suggestions to help define terms and KPIs, identify duplicate definitions, link business context to reports and datasets, and document lineage. That removes much of the manual collection, drafting and linking work that would otherwise fall to data stewards.
The result is a different role for your team: review and refine instead of collect and write. Business owners still decide what gross margin means in your organization, and suggested links need checking. Self-driven does not mean unreviewed. It means dScribe does more of the preparation, so people can focus on the decisions that need their expertise.
dScribe can also crawl metadata from older systems and build an inventory. You can see which reports use a particular measure definition. You can also see which reports and fields are being used, so you know where to start. In Power BI, the browser plugin displays definitions and owners within the report itself, where readers can see them and report errors.
Final thoughts
A smaller team can keep a catalog useful when the catalog carries more of the routine work. dScribe’s self-driven approach reduces manual collection, documentation and linking through metadata crawling and smart suggestions. Start with the reports people use, let owners review the meaning, and show that context in Power BI. The goal is less upkeep for your team, not simply more people doing the same manual tasks.
Frequently asked questions
How many data stewards do you need for a data catalog?
Fewer than you think, if the people who own the numbers maintain their own definitions. Stewards then guide the process rather than writing everything themselves. How many you need depends mainly on how many domains you tackle at once.
Can a data catalog maintain itself?
A self-driven catalog can take on much of the routine work. dScribe gathers metadata and suggests definitions, duplicates and links, reducing what stewards have to create by hand. Business owners still review suggestions and decide what KPIs mean; self-driven does not mean that human judgment disappears.
Can AI write the descriptions in my data catalog?
AI can produce a useful first draft of field descriptions based on names and context. Have a person review it before publication. For disputed KPI definitions, the owner always makes the decision.
Why does no one use our data catalog?
Usually because it is not where people work, or because too much of the information is outdated. Someone who encounters an incorrect description twice stops looking. Show definitions in the report and keep the scope small enough to stay up to date.
Do you need to document everything before launching a catalog?
No. Start with the reports and KPIs people read most. A small, accurate scope builds more trust than a comprehensive catalog full of outdated descriptions.