Editing Third normal form (section)

==Considerations for use in reporting environments==
While 3NF was ideal for machine processing, the segmented nature of the data model can be difficult to intuitively consume by a human user. [[Analytics]] via query, reporting, and dashboards were often facilitated by a different type of data model that provided pre-calculated analysis such as trend lines, period-to-date calculations (month-to-date, quarter-to-date, year-to-date), cumulative calculations, basic statistics (average, standard deviation, moving averages) and previous period comparisons (year ago, month ago, week ago) e.g. [[dimensional modeling]] and beyond dimensional modeling, flattening of stars via [[Hadoop]] and [[data science]].<ref>{{cite news |title=Comparisons between Data Warehouse modelling techniques – Roelant Vos |newspaper=Roelant Vos |date=12 February 2013 |url=http://roelantvos.com/blog/?p=740 |access-date=5 March 2018}}</ref><ref>{{cite web |date=23 September 2014 |title=Hadoop Data Modeling Lessons {{!}} EMC |website=InFocus Blog {{!}} Dell EMC Services |url=https://infocus.dellemc.com/william_schmarzo/hadoop-data-modeling-lessons-vin-diesel/|access-date=5 March 2018}}</ref> Hadley Wickham's "tidy data" framework is 3NF, with "the constraints framed in statistical language".<ref>{{Cite journal |last=Wickham |first=Hadley |date=2014-09-12 |title=Tidy Data |url=https://doi.org/10.18637/jss.v059.i10 |journal=Journal of Statistical Software |language=en |volume=59 |pages=1–23 |doi=10.18637/jss.v059.i10 |issn=1548-7660|doi-access=free }}</ref>