Data Attribute Challenges

B.

The main problem when it comes to data discovery is the quality of the data that analysis is being performed against.

Data that is malformed, incorrectly stored or labeled, or incomplete makes it very difficult to use analytical tools against.

The process of discovering data involves identifying where data resides, what type of data it is, and how it can be accessed. During this process, several attributes of data can pose challenges, such as labels, quality, volume, and format. Among these, the attribute that poses the biggest challenge for data discovery is volume.

Volume refers to the sheer amount of data that exists within an organization's infrastructure. With the proliferation of data sources and the increasing use of cloud computing, the volume of data has grown exponentially, making it difficult to locate and manage. The challenge of discovering data in a large data volume arises from the fact that data can be stored in disparate locations, such as on-premise data centers, cloud-based storage, or in third-party applications. Moreover, the data can be structured, semi-structured, or unstructured, further complicating the process of discovering it.

In addition to the challenge of locating data, volume also poses challenges in terms of processing and analyzing it. As data volumes grow, processing and analyzing the data becomes increasingly complex and time-consuming, requiring specialized tools and techniques.

Therefore, when it comes to data discovery, the volume of data poses the biggest challenge. Organizations must have an effective strategy in place to discover, manage, and analyze large volumes of data to derive insights and make informed decisions.