When you are selecting a data catalog, this decision is similar (and equally complicated) to the purchase of any other tangible or intangible element. It is obvious that any data catalog would help you understand and analyze previous data sets. However, the ease of getting to the final outcome (the quality) of the data catalog marks the difference.
In the following article, we will discuss how you can utilize various key markers to evaluate the quality of the data catalog. Let’s move forward and explore more about data catalogs.
What Is the Function of a Data Catalog?
The original purpose of a data catalog is to help a data analyst understand data. With better visibility into the past and existing information sets, the usefulness of this data improves. As a result, the quality of the findings also improve. Simply put, a data catalog is your one-stop solution for data curation and governance.
Today, data catalogs are being utilized not only for handling data inventory of organizations but also for enhancing analysis outcomes, quality, and handling data assets. In fact, compliance teams necessarily check cataloging to maintain critical guidelines of GDPR and other regulations. Traditionally, data cataloging was restricted to analyzing and understanding data. But now, it has moved towards a community-centric and extensive organizational collaboration approach, which has made cataloging essential for data management.
14 Tips to Choose The Best Data Catalog
When you are selecting a data catalog, it is necessary to ensure that this catalog meets the requirements and fits the culture of your organization. To help you achieve this, we have discussed 14 tips below. Read on.
Data Set Cataloging
The first thing that you should expect your data catalog to do is to support data discovery, including new dataset discovery and the initial making of the catalog. With the help of machine learning, your data catalog should fetch metadata, perform automated tagging, and achieve semantic inference. This is imperative to acquire optimum value from cataloging automation. It can reduce manual efforts and errors.
Data Set Search
The data catalog should include the ability to search – something which is the basic requirement. Your team should be able to search with keywords, facets, and other related business terms. An NLP-powered catalog can make this task easier for non-tech teams or users.
Note: The search option should always have a mask to secure datasets that a certain user is not authorized to view or access.
Any data catalog should have the ability to offer preparation of operations to users. These operations should be integrated into datasets for data blending, formatting, and improvement. This means that the catalog should support multiple operational associations with – data operations to data and many-to-many.
For instance, one of the mandatory operations would be to secure PII or personally identifiable information of users.
Data Set Recommendation
Recommendations are great for finding data quickly. This is why having a data catalog with recommendations can help you improve the connection between dataset, workflow, and data preparation. This recommendation engine should be equipped to automatically detect dataset relationships and overlapping features of datasets.
Evaluation of Data Set
Finding datasets is the first leg of the bigger picture. This means that the data catalog should also allow the data analyst to see profiles of data, preview data, find ratings, understand customer reviews, evaluate the quality of information, and check annotation by the curator.
Access to Data
After checking the data evaluation, data access should be analyzed. There exist multiple types of datasets, which could be connected to the catalog. For instance, tagged files, RDBMS, flat files, graph databases, document stores, text documents, geospatial data, etc. Along with access to these datasets, protections should be placed to ensure compliance and security.
Catalog of Metadata
Always ensure that the metadata collected in your data catalog is rich in quality.
- What type of data is sourced related to datasets?
- What knowledge do we have of processes and data lineage?
- Does the data contain details of SMEs, curators, etc.?
Asking these questions will give you a clear idea about the quality of metadata cataloging. Once these details are cataloged, it is necessary to ensure the right usage of metadata.
- Who is using it?
- What are the use cases of this usage?
- What is the frequency of use?
This can help you move towards intelligent recommendations.
Valuation of Data
One of the widely accepted facts about data catalogs is data valuation. The catalog should offer value for data datasets. This means that the information you receive should be able to create some value for the business, and the catalog itself should contribute to the estimation of value.
Proper security governance is necessary to ensure authorization and authentication. Allowing users to securely access data which they are authorized to see and authenticating access to the catalog for complete data security remain a top function of the process of cataloging.
Here, consider the levels of security constraints: row or record level, or field or column level.
Data Lineage or Tracing
The data catalog should offer transparency to the user to check data lineage. This means the ability to check the source of data, how it was generated, and where it is coming from. It is not uncommon to have breaks in lineage, such as when the dataset is extracted from ETL tools. When your catalog is able to fill these gaps, you can derive the source of the dataset to understand a dataset fully.
One of the amazing features that we get with the right data catalog is the ability to maintain compliance. It, in fact, should maintain compliance according to the changing regulations. Hence, when you are selecting a data catalog, look for a catalog powered by machine learning, which will automatically determine metadata and profile assets. This will also contain pre-written procedures for access restrictions and masking.
When your catalog doesn’t offer quality data, your reports and other models are of no use. For this reason, quality data helps you achieve business-ready datasets. So, the catalog should be integrated to achieve quality data from disparate sources to seamlessly improve the output in the form of reports.
It is necessary to understand that your catalog will not perform the cleansing, but it can offer you discrepancy and deficiency points, which are likely to create a bottleneck in the quality. You can use this to make amends.
Data interoperability simply means the ability to integrate with various tools. This indicates the manner in which your data catalog will integrate with your visualization tools and data preparation software.
Data Catalog Deployment
Once you have considered all the above factors, check the technical infrastructure support that you need. Whether your culture supports cloud, hybrid, and on-premise deployments, or if you want web-based or server-based implementations. After analyzing all these deployment requirements, run a final check with the data catalog vendor to move in the right direction.
There are multiple factors that help in deciding the right data catalog. Only after considering all the above requirements, you would be able to arrive at the right point, from where you can evaluate your budget and finalize a data catalog. Before you make that decision, don’t forget to take note of the consulting offered, along with the future plans of the vendor for transformation. Once you are satisfied with all these factors, you would be able to select the right data catalog.