Budapest Data 2015

Heisenberg's uncertainty principle is any of a variety of mathematical inequalities asserting a fundamental limit to the precision with which certain pairs of physical properties of a particle known as complementary variables, such as position x and momentum p, can be known simultaneously.

In simple words, there are situations when achieving two seemingly independent goals becames impossible (or can only be achieved to a certain limit).
In this presentation I will investigate a few classic BI problems sharing similar nature. There are two approaches in these cases or dual goals. Those goals cannot be fulfilled without a trade-off. These problems manifest in most of the BI/DWH/data management systems.
I will highlight the reasons behind these classic dilemmas. We will check industry best practices and Teradata specific answers to handle these situations.

The data modelling paradoxon.
A data model can't be flexible AND user friendly/simple at the same time.

The Business' BI vs IT's BI paradoxon
Business can't wait for IT development cycles. IT doesn't support non-standardized, hard to operate solutions.

The Classic DWH vs Big Data paradoxon
Websites, meters, mobile apps, etc. generate more and ever-changing data that can't be handled with classic BI toolset. Fail fast is important. New emerging technologies and packs of fresh data scientists promise to solve the quest, but can they do the same with good-old DWH/BI?