  The relevance and importance of contextualizing data analytics is described.
Qualitative characteristics might form the context of quantitative analysis.
Topics that are at issue include: contrast, baselining, secondary data sources,
supplementary data sources, dynamic and heterogeneous data. In geometric data
analysis, especially with the Correspondence Analysis platform, various case
studies are both experimented with, and are reviewed. In such aspects as
paradigms followed, and technical implementation, implicitly and explicitly, an
important point made is the major relevance of such work for both burgeoning
analytical needs and for new analytical areas including Big Data analytics, and
so on. For the general reader, it is aimed to display and describe, first of
all, the analytical outcomes that are subject to analysis here, and then
proceed to detail the more quantitative outcomes that fully support the
analytics carried out.
