To understand in simple terms, a data catalog is a well-organized inventory of the data assets in an organization. This catalog makes use of metadata catalog for helping the organizations handle their data. This helps the data experts to organize, collect, enrich and access metadata that will support better data discovery & data governance.
What’s a data catalog?
The data catalog rightly maintains the inventory of various data assets through description, discovery, as well as the organization of datasets. This catalog offers context to allow data scientists, data analysts, stewards, and consumers to find & understand the right dataset for purpose of extracting the business value.
What’re metadata catalogs helpful for?
An organized data catalog with the metadata is very useful to create an only source of the truth for all the company’s data. The metadata catalog will help the team to manage, discover, as well as understand your data assets in one place. It is very important as consumers of data might be increasing quickly. The companies are investing to set up the data lakes, data initiatives as well as creating self-service analytics ecosystems. It leads to several versions of the truth—several data sets, isolated knowledge, and versions.
Challenges the Data Catalog Will Address
With plenty of data available today, finding the best data platform has become very tough than it was before. Simultaneously, there are many rules & regulations than before—with the GDPR being one of them. Thus, not just is data access becoming one big challenge, but data governance has also become one challenge. You must understand the type of data you have at present, who will move it, what this is used for, or how it has to get protected. However, you also need to avoid putting a lot of layers & wrappers over the data—as data has become useless if it is very tough to get used to. Sadly, there are a lot of challenges in finding & accessing the right data. It includes:
- Data lakes now turning in data swamps
- Wasted effort to find or access real data
- Tough to know structure & “dark data”
- No business vocabulary
- Not any way to capture the tribal and missing knowledge
- Tough to assess quality, provenance, and trustworthiness
- Manual & ad-hoc prep efforts
- Tough to reuse the right knowledge & data assets
Technical metadata
Structural metadata or technical metadata describes how this data gets organized or displayed to your customers by describing the data structure objects—like columns, tables, indexes, rows, as well as connections. The technical metadata tells the data experts how they have to work with a data—for instance, in case they will work in the same way, or in case they want to transform this for the analysis and integration.
Process metadata
This type of metadata (administrative metadata) will describe the circumstances of any data asset’s formation and how, when, or by whom this is used, accessed, changed, or updated. It must describe who has got the permission for accessing & using this data in the right way.
The process metadata offers the right information about the asset’s history & lineage that will help the data analyst to determine if an asset is recent for any task at hand, in case this arrives from a reliable source, or updated by trustworthy individuals & more. Process metadata will be used for troubleshooting queries. Increasingly, the process metadata can be mined for the information on the software users and customers, like what tool they are using or the level of service that they are experiencing.
Metadata can also be used to increase data management today. Everything from self-service data creation to content-based control, the automated data onboarding, alerting and Monitoring anomalies, provisioning & scaling resources, and more, and more can be augmented with help of the metadata. Data catalog makes use of metadata for helping you to achieve everything with data management.
Final Words
Organizations are trying hard to stay data-driven and they want much better and faster data analytics, without even sacrificing data governance. That’s what makes data management very important & challenging for you.
