While most ETL tools available in the market today possess a catalog module – which allows users to access standardized data from a central location and view backward lineage – some fail to provide insight into how and where data is being used as well as the future impact of making changes. Thus, these ETL tools must also offer a reporting catalog that can provide users with a central place to track all of their reports and dashboards, which can be based on PowerBI, Tableau, SSRS, etc.
Apart from simply listing out all reports in one place, the reporting catalog should allow users to store any metadata relating to these reports. This metadata would include information like owners of the reports, the last published date, the URL, the sources feeding the reports, etc. Additionally, the reporting catalog should be able to store information across hierarchies in any report or dashboard, the information relating to the various tabs and individual elements in each.
These metadata items can be static or dynamically defined by the user. Moreover, the metadata can be different for PowerBI reports in comparison to an Excel spreadsheet. Regardless of the level of support, this catalog should allow users to quickly search for the specific report they need – making an intelligent system absolutely critical to handle scale. Imagine a scenario where thousands of reports are being searched by the system and it takes many minutes to retrieve that user report.
Now that we’ve covered the basic features that should be present in any reporting catalog, it is important to understand what exactly sets a state-of-the-art reporting catalog apart from one that is standard. Starting with three main features, a state-of-the-art reporting catalog should be able to catalog automatically, show full backward and forward lineage for every attribute in the system, and provide users with a detailed audit of every action taken.
When users build SSRS reports, for example, the reporting catalog should automatically scan and add any new reports, and it should also be able to accommodate any modifications and deletions of reports. Report level and metadata information should be captured automatically and updated within the catalog.
In the case of forward lineage, users should be able to pick an attribute, say quantity, and view all reports that are consuming that attribute and how. Some reports might be using it to calculate market value whereas some might be directly using the quantity. Having that level of insight allows users to understand the impact of each data point in their downstream systems.
Finally, no state-of-the-art reporting catalog is complete without a detailed audit of every action taken. Every action – addition of reports, change of metadata, deletion of reports, deletion of metadata, etc. – should be tracked across all dimensions – when the action occurred, who took the action, the result of the action, etc.
Although data discovery has been solved using a data catalog, the possession of a reporting catalog can help solve the problem of data impact discovery. Learn how Enterprise Data Management – an award-winning, best-in-class solution – is assisting managers through its robust data catalog and data lineage.
