There are various difficulties of data management intensified at a steady pace over past years. Management complexities of the big data, hosting, analytics, as well as tightening regulations cannot be ignored. Effective management has actually become the top priority for many organizations, however, getting a bit is challenging for many firms. The data catalog tools fill important roles in overcoming such challenges.
The data catalogs were created to help the data analysts to find & understand data in a better way. Before the data catalogs, the majority of the data analysts had to work blind, with no visibility in the current data sets and contents, or the usefulness and quality of each. Thus, they had spent a lot of the time finding the right data, understanding the data, as well as recreating the right data sets that existed. The data catalogs were made to address such issues.
1. Choose the right solution that will connect to the widest range of the data sources
The Enterprise Data Catalog must run on the structured and semi-structured data, doesn’t matter where this is: on the cloud, on the data warehouse, or the data lake. Moreover, the data catalog should catalog the enterprise data, and not only parts. Choose the vendor that can support many data source forms as well as has the native connectors to such sources so if data must get moved subsequently for supporting the data transformation, bulk offload APIs will be supported that will limit the operational impact that data movement may have.
It is really good to have a complete list of data sources and check against any list of the sources & applications that this vendor offers. How the vendor connects with the data source is very important; as some need query tracking code getting loaded on every data source to get a catalog, it has benefits but makes the cloud apps a bit problematic.
2. SQL Query Analysis
An important part of the context that the data catalog will offer generally comes from an ability to parse the usage log & track the behavior of the people, which are accessing their data sets. At the bare minimum, the data catalog must offer metrics as to how many times the data set is queried or by whom. Thus, you will select the data catalog, which offers behavioral statistics like which schemas, columns, tables, filters, or queries are popular.
Data catalogs surface the machine-learned usage styles all along with technical metadata that will offer the complete picture. You just look, for an ability to trace this data lineage. Lineage is very important as it will show you various steps in your data pipeline, which may have a strong impact on the analysis. The ability of the data catalog for adding lineage will contribute highly to the accuracy and speed of data analysis.
3. Choose Catalog fueled by AI
The AI-assisted catalog must profile their data automatically. It must tell you exactly where PII data gets stored throughout the enterprise, an important step in compliance. But, it will decide common cleansing issues like numerical normalization, duplications, statistical outliers, and more, all lead to valuable insights in its discovery procedure. Artificial Intelligence will uncover what has been “tribal knowledge” on the data. Suppose you’re empowering the business users to use data, which the tribal knowledge should get conveyed very easily to business users & the best method to do this is through artificial intelligence.
4. Check Out Various Services
Nuances & details of the data catalog implementation will sometimes be very challenging, and the consulting services will prove highly valuable, particularly when you are working with the non-traditional types of data. The data catalog users will require introductory training, or data curators might need depth of the right training. Make sure you ask what types of training or consulting solutions are accessible for you. Also, you must look for user groups and online forums as sources of problem-solving and knowledge.
5. Using Evaluation Criteria
Certain criteria aren’t very important. In case practical, you need to prioritize on criteria as well as assign some weighting factors that will align them with the organization’s needs or goals. Suppose you are not very sure about prioritizing then you can divide it into categories: should have, good to have, or not important.