Metadata Madness - Why metadata management is important.
Metadata management is important for building educational data systems that can serve various customer needs. A well-managed metadata store enables many people to generate richer and more insightful data reports. The process of metadata management includes several steps:
1. Defining the structure of the metadata store
2. Populating the metadata store with data
3. Maintaining the metadata store over time
4. Using the metadata store to generate reports and insights
We typically begin managing metadata by designing the data architecture of the store. One of the key things to be done in this step is to ensure that you use unique identifiers in every table and avoid using actual names of things to perform any joins. The latter often poses serious problems later on in terms of data integrity. When the datastore is populated, it becomes ready for consumption in downstream data systems and APIs. The maintenance of metadata can be done in multiple ways. Either you can design APIs to modify or add metadata in a restricted way, or you can directly allow developers to edit the database. Oftentimes, data science teams end up designing the metadata structure and initial table of the metadata. In this case, they pass over tables in CSVs that need to be imported into the database. It is also possible that there are later updates to metadata where new data come into the same format.
When metadata are ready to use, they enable end-users to get more value out of data. For example, let us say we are analyzing data of 1000 products across various categories. If we do not know the categories, we can only look at data about individual products and compare them individually. To get a system-level view of how different categories perform, we have to maintain the metadata that maps each product to its category. Product management teams often develop the categorization, and it needs to be maintained over time when there are new products and data. Similarly, when we want to know how well a campaign did, we need to know which products were in the campaign. The mapping of products to campaigns has to be maintained as metadata. All these examples show that metadata management is a key part of developing a data system that can provide actionable insights.
When designing a new data report in an existing platform, it is important to think about what kind of metadata it will require. For example, a report on products might need:
- Names of products
- Categories of products
- Date when the product was launched
- Campaigns the product was a part of
If we do not have this metadata available, we will not be able to generate the report. In some cases, it might be possible to get the data from other sources, but it may not be possible in some cases. Therefore, it is important to think about metadata management early on in the data system design process.
In conclusion, metadata is very important to keep and maintain for building data systems that give more usable and impactful data reports.