There is a vast amount of Linked Data on the web spread across a large number of datasets. One of the visions behind Linked Data is that the published data is conveniently reusable by others. This, however, depends on many details such as conformance of the data with commonly used vocabularies and adherence to best practices for data modeling. Therefore, when an expert wants to reuse existing datasets, he still needs to analyze them to discover how the data is modeled and what it actually contains. This may include analysis of what entities are there, how are they linked to other entities, which properties from which vocabularies are used, etc. What is missing is a convenient and fast way of seeing what could be usable in the chosen unknown dataset without reading through its RDF serialization. In this paper we describe use cases based on this problem and their realization using our Linked Data Visualization Model (LDVM) and its new implementation. LDVM is a formal base that exploits the Linked Data principles to ensure interoperability and compatibility of compliant analytic and visualization components. We demonstrate the use cases on examples from the Czech Linked Open Data cloud.