1. Should This Be Normalized?
When Database Normalization Seems Abnormal

2. About Me Professional side Personal side
Data modeler/architect at Community Care of North Carolina
Worked with SQL Server for 8 years (started with 2008 R2)
Started as an web/data analyst and QA person, then a database developer, then shifted between analysis and architecture since
Personal side
From Raleigh via Philadelphia
Avid runner (2x marathoner, age group Cary Pancakes & Beer 5k)
Autism spectrum advocate
Lover of obscure pop culture references
About Me
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3. What is this about? Normalization vs. denormalization
Primer on the normal forms and how they work
First, second/third, Boyce-Codd
A forum on when normalization actually works in a BI context
Audience participation!
Hint: all questions will ultimately have the same answer
What is this about?

4. A definition
What is normalization anyway?

5. The structuring of a relational database to increase integrity and reduce redundancy
Concept introduced by Edgar F. Codd in 1970 while working on data storage
Involves facts and dimensions to look up transactions and references
Normalization

6. Normalization The Advantages The Disadvantages
Less duplication means database size is smaller
In many cases, the first point leads to data models optimized for applications & products
Only need to join necessary tables when querying
New data can easily be inserted
Many fact tables may contain codes upon codes, so frequent joins to lookup tables are needed
As the types progress and dimensions increase, performance will be affected
What about all those aggregates?

7. Normal forms
First, second, third, and Boyce-Codd

9. We have a limited set of race data in a file
We have a limited set of race data in a file. A string of race participants is included with each event instance. If we are going to process future results, we’ll have to see what works with our current system so the runners won’t complain about seeing how they did. Let’s look through the types. When should this be normalized?
The Problem

10. First Normal Form “The key”
Elimination of repeating groups and columns
No two rows are identical
The records have the same number of fields
Use the one-to-many relationship to develop without multiple columns
First Normal Form

11. Second Normal Form “The whole key”
Everything from First Normal Form still applies
Duplicate data sets are removed
Determinants are based on the primary key
Cardinality reduction
Second Normal Form

12. Third Normal Form “Nothing but the key”
Everything from first and second normal forms apply
Essentially an extension of second normal form
Figuring out if a determinant is not an entity
If A relates to C, C cannot determine B
Applies best for prototypes in a BI environment
Third Normal Form

13. Boyce-Codd Normal Form
Now the transitive dependencies are gone
Every row has a unique identity
If A determines B, it’s because A is a key!
You can usually go straight from first to BCNF by looking at determinants
Race: RaceName, RaceState
Distance: DistanceCode, RaceDistance
Sponsor: SponsorCo
Participant: ParticipantName, ParticipantAddress, ParticipantCity, ParticipantState, ParticipantZip
Candidate key: ChipTime (RaceID, ParticipantID, DistanceID)
Boyce-Codd Normal Form

14. Time to ask the question…
Should This Be Normalized?
Time to ask the question…

15. Why denormalize? The Advantages The Disadvantages
Reporting environments often require great performance for frequent pulls
Some calculations can be readily applied
Analytics and data science teams may have an easier time connecting variables
The three types of write anomalies are included
If more write operations are included, everything could actually take longer
Do we know all the rules or do we need to document more?

16. Further use cases
A forum on (de)normalization, where we run through scenarios

17. A free text field includes city and state and whether the address is permanent
This allows for tracking business geography
Should this be normalized?
For applications? Reporting?
What should we consider?
Abbreviated city names
Reporting on the phone number
If a house is on the census
Address in the box

18. You have a table with phone numbers, split into area code, and first 3 then 4 digits
The audience is customer service, directly accessing the database through an application
Should this be normalized?
The phone number
Country
Code
Area
Office
Prefix
Line
Number 1
215
834
5858
972
976
0227 44
0114
807
6591
305
117
7076

19. A customer CRM has history of patient transactions with previous names and addresses included
The PowerBI gurus want to use this for a model on turnover
Does denormalization apply here?
What should we consider?
Access to PHI data
Storage space
Scalability
Partitions
Customer history

20. The cardinality makes a difference
Inverse relationship to normalization
Preferences for simple star schemas
The context of normalization for “Power” models
Do you want to normalize dates?
Numbers?
Experimental models are concerned more with the rows than the columns
[obligatory slide about Tabular, Power Pivot, Power View, and Power BI]

21. IT DEPENDS.
It’s all about the entity’s data plan

22. More Questions and Answers?

23. Thanks for coming!
Ceedubvoss.com