Data is just that -- data--or is it? Data has been around since the Big Bang. Data was recognized, manipulated, and at many levels understood by thinking men and women in the sciences, in mathematics, and in philosophy down through the ages. From the birth of the scientific method (±1600 BCE), Data was subjected to empirical analysis, testing, verification, and strictly limited in its use in terms of meaning of dare, Latin for "to give, present, what is given- (the main forms being: Do, Dare, Dedi, Datus)." Think Euclidean proofs in geometry, where it is "given that this follows that, which is the logical progression and orderly presentation of reasoning from the axiom to the proposition or theorem, e.g., the Pythagorean theorem. Well and good in the evolving realm of abstract and applied Empiricism: data was understood, manageable, and used according to strict rules; inferences were based on those rules and were subjected to the scientific rigors of proof by replication and objective recording of results until all doubt about the data used in the experiments resulted in concurrence that the data was proved out.But it is a very different story today in the world of data as it is used-or misused-- in and by the "Enterprise." Think Big Data. Think roughly 2.5 quintillion bytes of new data added to the massive volume already created and digitized since the digital computerization of data began in the late 1950s. Think layers of programming needed to "manage, store, mine, and present" big data for human review, for machine review. Think complexity and vastness on a scale that no human can grasp in practical terms, in a way that fosters positive action based on sound assumptions, on empirical structures that produced "a proof" that was accepted as truth by scientists from the Greeks to the scientists of today. But business men and women, the Chiefs in Charge of the Enterprise, they are not trained scientists. They rely on the "Data Chain of Custody and Governance" by teams of data caretakers who have become more and more overwhelmed by the volume, complexity, and human-errors that are at the base of the data pyramid. That base of human error-machine error is always human error-leads to problems so deep and unfathomable that even the best programmers, data engineers, data scientists, and data theoreticians cannot come up with methods that prevent data corruption on massive and extremely costly scales.This short book explores some of the reasons why it is now more than ever axiomatic: live by data, die by data, for it is the primordial and infinite swamp.