From usability to performance, analytics to architecture; as report developers, the user experience design (UX) of your data model is quickly becoming more important than the pretty pictures that sit on top of it. This session will concentrate on the design decisions needed to increase the usage of your reports.

1. Not quite what I was expecting
I went ahead and built my own report instead
Missed the mark
PAINFULLY SLOWDifficult to navigate
I’ll never use those features
Something like beforeI didn’t want you to change the way it’s always been done

2. IT’S NOT YOU.
IT’S YOUR DATA MODEL.

3. ALEX M POWERS
Microsoft Certified Solutions Associate: BI Reporting (Excel, Power BI)
Microsoft Technology Associate: Python (Introduction to Programming)
Microsoft Office Expert: Excel 2013, Excel 2010
Microsoft Office Specialist: Access 2013, Excel 2010
Microsoft #HowWeExcel Contest Winner
Co-Organizer of the St Louis Power BI User Group #STLPBIUG
Millennial Whisperer and Trainer | It’s Not About The Cell LLC: itsnotaboutthecell.com
Confused by the term “Pair of Socks”
Bad at Fortnite

4. USER EXPERIENCE (UX)
Refers to a person's emotions and attitudes about using a
product, system or service. It includes the practical,
experiential, affective, meaningful and valuable aspects
of human–computer interaction and product ownership.
Additionally, it includes a person’s perception of system aspects
such as ease of use, efficiency and utility.
Source (Wikipedia)

5. SESSION OBJECTIVES
1. Readability allows for discovery. (Ease of Use)
2. Performance considerations. (Efficiency)
3. Trust the abilities of your end users to learn. (Utility)

6. CLEAN THAT
_______ UP.

7. FIELD NAMES
Type Example
Camel Case orderDate
Pascal Case OrderDate
Snake Case ORDER_DATE

8. let
Source = #table(
type table [CUSTOMER_ID = Int64.Type, CUSTOMER_NAME = text, CUSTOMER_CITY = text, CUSTOMER_STATE = text],
{
{1, "Bob Smith", "Rockford", "IL"},
{2, "Randy Savage", "St Louis", "MO"},
{3, "Lucy Davis", "Lansing", "MI"}
}
),
// Replaces Underscore Field Name Values and Converts to Proper Casing
ReplaceUnderscores = Table.TransformColumnNames(Source,
each Text.Proper(
Replacer.ReplaceText( _ , "_", " "
)))
in
ReplaceUnderscores
In the above let expression we transform our column names replacing underscores with a space and proper
casing with an each loop. The underscore character is used to iterate thru each item in our column headers
collection.
Full Solution: Replaces Underscores

9. let
Source = #table(
type table [CustomerID = Int64.Type, CustomerName = text, CustomerCity = text, CustomerState = text],
{
{1, "Bob Smith", "Rockford", "IL"},
…
}
),
/*
Splits Column Header Characters When Transitioning From Lower Case to Upper Case to a list of Values
Wrapped in a Text Combine to Combine all List Values to a Scalar Text Value
*/
SplitByCharacter = Table.TransformColumnNames(Source,
each Text.Combine(
Splitter.SplitTextByCharacterTransition({"a".."z"}, {"A".."Z"})(_)
, " ")
)
in
SplitByCharacter
In the above let expression we transform our column names splitting each text into a list by character by
transition whenever lower-cased characters in a list are followed by upper-cased characters in a list. The
split lists of text are combined including a space between each list item.
Full Solution: Split Text By Character Position

10. PERFORMANCE
BEATS PRETTY
100% OF THE TIME
50% OF THE TIME

11. BEHIND THE CURTAIN
1. xVelocity Engine, Run Length Encoding, Auto Sort.
2. You Can Get Kicked Out of Your Own Party.
3. Segmentation. Compression. Memory Consumption.

12. xVELOCITY ENGINE
DICTIONARY
ENCODING
COLUMN
ENCODING

13. TABLEColor Value
Red 10
Blue 15
Green 13
Green 25
Yellow 31
Yellow 41
Red 11
Blue 14
Purple 21
Orange 6
Red 15
Follow Along: Colors

14. DICTIONARYID Color Value
0 Red 10
1 Blue 15
2 Green 13
2 Green 25
3 Yellow 31
3 Yellow 41
0 Red 11
1 Blue 14
4 Purple 21
5 Orange 6
0 Red 15
Dictionary ID Color
0 Red
1 Blue
2 Green
3 Yellow
4 Purple
5 Orange

15. DICTIONARYID Color Value
0 Red 10
1 Blue 15
2 Green 13
2 Green 25
3 Yellow 31
3 Yellow 41
0 Red 11
1 Blue 14
4 Purple 21
5 Orange 6
0 Red 15
Dictionary ID Color
0 Red
1 Blue
2 Green
3 Yellow
4 Purple
5 Orange

16. COLUMN ENCODING
ID Value
0 10
1 15
2 13
2 25
3 31
3 41
0 11
1 14
4 21
5 6
0 15
Dictionary ID Color
0 Red
1 Blue
2 Green
3 Yellow
4 Purple
5 Orange

17. EVALUATION
ID Value
0 10
1 15
2 13
2 25
3 31
3 41
0 11
1 14
4 21
5 6
0 15
Dictionary ID Color Value (SUM)
0 Red 36
1 Blue 29
2 Green 38
3 Yellow 72
4 Purple 21
5 Orange 6

18. OPTIMAL SORT ORDER
ID Value
0 10
0 11
0 15
1 15
1 14
2 13
2 25
3 31
3 41
4 21
5 6
Dictionary ID Color
0 Red
1 Blue
2 Green
3 Yellow
4 Purple
5 Orange

19. RUN LENGTH ENCODING
ID Value
0 10
0 11
0 15
1 15
1 14
2 13
2 25
3 31
3 41
4 21
5 6
Dictionary ID Color Start Count
0 Red 1 3
1 Blue 4 2
2 Green 6 2
3 Yellow 8 2
4 Purple 10 1
5 Orange 11 1
Dictionary ID Color
0 Red
1 Blue
2 Green
3 Yellow
4 Purple
5 Orange

20. ID Value
0 10
0 11
0 15
1 15
1 14
2 13
2 25
3 31
3 41
4 21
5 6
EVALUATION
Dictionary ID Color Value (SUM)
0 Red 36
1 Blue 29
2 Green 38
3 Yellow 72
4 Purple 21
5 Orange 6

21. BEHIND THE CURTAIN
1. xVelocity Engine, Run Length Encoding, Auto Sort.
2. You Can Get Kicked Out of Your Own Party.
3. Segmentation. Compression. Memory Consumption.

22. CARDINALITY
1. High-cardinality: columns with values that are very
uncommon or unique.
• DateTime
• Unique IDs (Not Needed For Relationships)
• Text (Freeform Fields)
• Numbers (Floating Point Precision)
2. Low-cardinality: columns with relatively few unique values.
• Date (365 Days a Year)
• Time (86,400 Seconds)
• True / False
• Currency (4 Digits after Decimal)

23. BEHIND THE CURTAIN
1. xVelocity Engine, Run Length Encoding, Auto Sort.
2. You Can Get Kicked Out of Your Own Party.
3. Segmentation. Compression.

24. RESOURCE CONSUMPTION
1M 1M 1M … Model
Compressed
Segment
Compressed
Segment
Data Model
(Hierarchies,
Calculated Columns,
Relationships)
Compressed
Segment
Compressed
Segment
Compressed
Segment
…
CPU
MEMORY
CPU

25. SEGMENTATION
In the above Vertipaq Analyzer we review a DateTime field with high-cardinality. The Rows count is equal to
the Cardinality count and is loaded in an uncompressed state to the xVelocity Engine. At the rate of 1M rows
per segment, 9 uncompressed segments are loaded as a single partition into memory.
Full Solution: DateTime Series Analyzer

26. SEGMENTATION
In the above Vertipaq Analyzer we review a Time and Date field with low-cardinality. The Order Time field
contains 86,400 unique values (equal to the number of seconds in a day) and the Order Date 103 unique
values. At the rate of 1M rows per segment, 85 compressed segments are loaded as a single partition into
memory.
Full Solution: Date and Time Series Analyzer

27. COMPRESSION
8,900,608 ROWS 88,743,740 ROWS

28. TAKEAWAYS
1. Only bring in what you need.
2. DateTime = BAD; unless necessary.
3. Calculated Column = POST compression.
• Perform Calculations Close to the Source (SQL or within Power Query)

29. SWITCH ( TRUE(),
YOUR , BATTLES
)

30. END USE
1. Be cautious with preview features.
2. Don’t assume your audience can’t learn.
3. Consistency. Consistency. Consistency.
4. Don’t risk your brand over someone else’s need for a pie chart.
5. Champion modern analytics within your organization.
• By 2020, 50% of analytical queries either will be generated via search, natural language processing or voice, or will be
automatically generated. (Gartner)

31. CLOSING REMARKS
DELIVER BETTER THAN THE ASK.

32. QUESTIONS?
SUGGESTIONS?
YOU THINK I’M CRAZY?!

33. Connect Online
@notaboutthecell
/in/alexmpowers
It’s Not About The Cell