Data Access and Search

Next to Data Security & Privacy as well as Data Quality Management, there is a huge importance in Data Access and Search. This topic focuses on finding and accessing data in your data assets. Most large enterprises have a lot of data at their finger tips, but different business units don’t know where and how to find it. In this tutorial, we will have a look at how to solve this issue.

What are the ingredients for successful Data Access and Search?

There are several pre-conditions that need to be fulfilled in order to make data accessible. One of the pre-conditions is to have data security and privacy solved. If you want to make data accessible in large-scale, it is very important to ensure that only those users can access the data they should access. As a result of this, all users should see data assets in the company via a data catalog, but not the data itself. In this catalog, people should have the possibility to browse different data assets available in the company and start asking more questions.

A good data catalog constantly checks the data for updates to the catalog itself and to possible modifications. In addition to these requirements mentioned before, the data catalog checks for different data quality measures as described in the previous tutorial.

What should be inside a data catalog?

Based on the above mentioned things, a data catalog contains a lot of data about data. Next to different data assets available, each data asset should be described and offer several informations about it:

• Titel. Title of the dataset
• Description. What this dataset is about.
• Categories. Tags, to enable search.
• Business Unit. Unit, maintaining the dataset (z.b. Marketing)
• Data Owner. Person, in charge of maintaining the dataset.
• Data Producer. System that produces the data
• Data Steward. Person taking care of the dataset, if not data owner itself.
• Timespan. This indicates a date when to when the data was recorded.
• Data refresh interval. If not in real-time available, indication how often the data gets refreshed
• Quality metrics. Indications on data quality.
• Data Access or Sample Data. Information on how to access the data or a sample dataset to explore the data
• Transformations. When and how was the data transformed?

How does a data catalog looks like?

This items above are samples for the contents of a data catalog entry. A good data catalog makes it easy for users to find and search within the metadata. The following sample shows the data catalog from the US government:

This tutorial is part of the Data Governance Tutorial. You can learn more about Data Governance by going through this tutorial. On Cloudvane, there are many more tutorials about (Big) Data, Data Science and alike, read about them in the Big Data Tutorials here. If you look for great datasets to play with, I would recommend you Kaggle.