You keep a data catalog up to date without a large steward team by splitting the work. Bring in technical metadata automatically from your sources and Power BI. Limit manual work to the definitions and owners of what people actually use. And give that work to the people who already own the numbers, not a separate team. A catalog fails because of maintenance, not missing features.
Why most data catalogs are outdated after a year
The usual response is to add more features and more stewards. That rarely solves the problem. A catalog becomes unusable not because it does too little, but because it is no longer accurate.
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.
In practice, the catalog has become a snapshot. The project has closed, but no one was ever assigned responsibility for maintenance. That is the real problem.
What you can automate, and what you cannot
Not all metadata needs the same amount of human input. Much of it can come straight from your sources. What remains is the meaning: what a number represents and who makes decisions about it.
| 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. Anything a machine can read should be read by a machine. People spend their time on the bottom two rows.
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Being comprehensive feels responsible, but it is the fastest route to an outdated catalog.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 maintenance stays manageable. Do not ask an owner to learn a portal. Ask them to confirm or amend one sentence at a scheduled time.
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.
Where dScribe fits
Defining a KPI is still manual work. Someone from the business decides what gross margin means in your organization, and no tool can take over that decision. dScribe does reduce the work with smart suggestions. These help you define new terms and KPIs and link them to the reports and datasets where they appear to be used. They also flag duplicate definitions and help document lineage. Instead of starting with a blank page, the owner confirms or adjusts a suggestion.
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 catalog that is complete at launch is usually inaccurate a year later. A catalog that starts small and is maintained by the right people stays accurate. Automate what a machine can read. Put the owner in charge of meaning. And show it where people look, so errors do not go unnoticed for long.
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?
Partly. Tables, columns, lineage and usage can be collected automatically. A person still needs to define what a KPI means and who makes decisions about it, even if AI provides a first draft.
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.