Slide deck of the Unicorns and Dinosaurs data modeling session at South Cost Summit 2023.

1. Data Modeling in
Dataverse: Where
Dinosaurs and
Unicorns Meet 🦖🦖💻
Chris ‘Dino-man’ Huntingford 🦕🦕
Rebekka ‘Unicorn Lady’ Aalbers 🦕

2. Headline Sponsor Community Sponsor
Supporting Sponsors
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3. Chris ‘Dino-man’ Huntingford 🦖🦖
Rebekka ‘Unicorn Lady’ Aalbers 🦖
🦕
🦕

4. Agenda
Introduction
The
fundamentals of
data modelling
Data modelling
Rules
The use case
Getting hands-
on
Wrap up

5. Fundamentals of
data modeling

6. User & Data Driven Design
The data first is the process that works for us. We understand that Data is the most important part of the platform
and has the most risk associated to it, therefore we treat it with respect.
What data do you need? And Where?
User Story – The Journey – Wire Frames
Intelligence – Back end (Table / Entity side) – Power Automate
Theming / Look & Feel
Data
Before
pretty
Screens
Interaction Layer – App Type / Automation / Bot
Use Case – What is the problem we need to solve?
What’s the story?
Data Model - ERD
WHO (User Personas) will access the Data
Security Design
Interaction Layer – Screens/Pages /Process
Interaction Layer – Forms and Views (Galleries)
Intelligence – Front End (Power FX)
The Look & Feel, Theming and presentation is ultimately refined and groomed
at this point. The use of the custom components and controls in the app layer
will assist and aid this process.
Interviews and requirements gathering of what is needed by the business. Its
important to work with the people closest to the challenge.
Data is the next part of the process. If you understand the type of data that
needs to be stored and WHO will need access to this data, you can start
building your ERD and security model.
Design your security layers before your ERD. Security should inform your ERD.
You may need to work on these both together as they work hand in hand. You
should not retrofit security later.
Design your story. What is the journey for the user? Design Thinking
Often the journey of the user will dictate the application type. Leveraging
Dataverse, you have a choice between Model-Driven apps and Canvas apps.
This is an integral part of the process as it will ultimately define User
Experience and the more detailed Screens / Pages, forms and views required.
Predefined components should be leveraged to assist Look and feel where
possible.
The UX and base app are great but typically forms over data; the real
intelligence comes from the configuration of controls added to forms, views /
Galleries, buttons etc… How will the app behave?
Accessibility

7. Why do we data model?
Data is vial to solution development and is often a digital representation of the physical
world. We do not spend our lives as a flat structure and in fact there is ONLY 1 of you, 1 of
me. This type of approach maximises the use of a single point of data multiple times.
AN EXAMPLE :

8. What is Normalised data?

9. What is Normalised data?
A normalized database is one that's structured to reduce redundancy and improve data
integrity. It follows the principles of database normalization, which involves decomposing
tables and eliminating functional dependencies to create a more efficient database design.

10. Why do we data model
Fantasy Fields Moonlit Grove Misty Unic orn, Herbivore, Misty A ura Ella
Fantasy Fields Cretac eous Cliffs Sky Pterodac tyl, Carnivore, Winged Sophia
Fantasy Fields Jungle Mirage Whisper V eloc iraptor, Carnivore, Intelligent Ethan
Mystic Haven V olc ano V iew Blaze T-Rex, Carnivore, Fire-Breathing (Modified) Jac k
Mystic Haven Northern Plains Boulder Tric eratops, Herbivore, Thic k Skin Emma
Mystic Haven V olc ano V iew Gator Spinosaurus, Carnivore, Long Snout Jac k
Mystic Haven Celestial Summit Glitter Unic orn, Herbivore, Glittering Hooves Sarah
Mystic Haven Celestial Summit Pegasus Unic orn, Herbivore, Feathered Wings Sarah
Mystic Haven Northern Plains Spike Tric eratops, Herbivore, Friendly Emma
Mystic Haven Celestial Summit Starbeam Unic orn, Herbivore, Iridesc ent Mane Sarah
Mystic Haven Celestial Summit Willow Unic orn, Herbivore, Willow-like Mane Sarah
MythoRealm Carnivore Cove A qua Ic hthyosaurus, Carnivore, A quatic Mike
MythoRealm Herbivore Heights Leaf Stegosaurus, Herbivore, Spiked Tail Kevin
MythoRealm Crystal Springs Moonshine Unic orn, Herbivore, Luminesc ent Horn Olivia
MythoRealm Carnivore Cove Rex T-Rex, Carnivore, A ggressive Mike
MythoRealm Crystal Springs Starlite Unic orn, Herbivore, Star-shaped Markings Olivia
MythoRealm Herbivore Heights Thunder Brac hiosaurus, Herbivore, Long Nec k Kevin

11. This means
Sanctuary Area Animal Characteristics Care T
aker
Fantasy Fields M oonlit Grov e Dazzle Unic orn, Herbiv ore, Rainbow T
ail Ella
Fantasy Fields Jungle M irage Fred Allosaurus, Carniv ore, S
harp Claw s Ethan
Fantasy Fields M oonlit Grov e M idnight Unic orn, Herbiv ore, Dark Fur Ella
Fantasy Fields M oonlit Grov e M isty Unic orn, Herbiv ore, M isty Aura Ella
Fantasy Fields Cretac eous Cliffs S
ky Pterodac tyl, Carniv ore, Winged S
ophia
Fantasy Fields Jungle M irage Whisper Veloc iraptor, Carniv ore, Intelligent Ethan
M ystic Hav en Volc ano View Blaze
T
-Rex, Carniv ore, Fire-Breathing
(M odified) Jac k
M ystic Hav en Northern Plains Boulder T
ric eratops, Herbiv ore, T
hic k S
kin Emma
M ystic Hav en Volc ano View Gator S
pinosaurus, Carniv ore, Long S
nout Jac k
M ystic Hav en Celestial S
ummit Glitter Unic orn, Herbiv ore, Glittering Hoov es S
arah
M ystic Hav en Celestial S
ummit Pegasus Unic orn, Herbiv ore, Feathered Wings S
arah
M ystic Hav en Northern Plains S
pike T
ric eratops, Herbiv ore, Friendly Emma
M ystic Hav en Celestial S
ummit S
tarbeam Unic orn, Herbiv ore, Iridesc ent M ane S
arah
M ystic Hav en Celestial S
ummit Willow Unic orn, Herbiv ore, Willow -like M ane S
arah
M ythoRealm Carniv ore Cov e Aqua Ic hthyosaurus, Carniv ore, Aquatic M ike
M ythoRealm Herbiv ore Heights Leaf S
tegosaurus, Herbiv ore, S
piked T
ail Kev in
M ythoRealm Crystal S
prings M oonshine Unic orn, Herbiv ore, Luminesc ent Horn Oliv ia
M ythoRealm Carniv ore Cov e Rex T
-Rex, Carniv ore, Aggressiv e M ike
M ythoRealm Crystal S
prings S
tarlite Unic orn, Herbiv ore, S
tar-shaped M arkings Oliv ia
M ythoRealm Herbiv ore Heights T
hunder Brac hiosaurus, Herbiv ore, Long Nec k Kev in
GETTING RID OF
REPEATING
GROUPS
ELIMINATING
REDUNDANCIES

12. Data modeling results – Micro
servicing Your Data
• Promote reuse of data
• Focus on capture once, re-use many
• Promote data accuracy
• Minimise the risk on data being inaccurate
• Drive hierarchical and more defined security.
• Layer security across a model and protect certain parts of your data.

13. Data modelling helps you layer your
data

14. Data Influencers
Self service
reporting
needs

15. Data modeling
– The rules

16. Terminology
• Table: A structured set of data held in rows and columns.
• Columns: A vertical set of data within a table that holds a specific attribute.
• Rows: A horizontal set of data that represents a single record within a table.
Sanctuary Area Animal Characteristics Care Taker
Fantasy Fields M oonlit Grove Dazzle Unicorn, Herbivore, Rainbow Tail Ella
Fantasy Fields Jungle M irage Fred Allosaurus, Carnivore, Sharp Claw s Ethan
Fantasy Fields M oonlit Grove M idnight Unicorn, Herbivore, Dark Fur Ella
Fantasy Fields M oonlit Grove M isty Unicorn, Herbivore, M isty Aura Ella
Fantasy Fields Cretaceous Cliffs Sky Pterodactyl, Carnivore, Winged Sophia
Fantasy Fields Jungle M irage Whisper Velociraptor, Carnivore, Intelligent Ethan
M ystic Haven Volcano View Blaze T-Rex, Carnivore, Fire-Breathing (M odified) Jack
M ystic Haven Northern Plains Boulder Triceratops, Herbivore, Thick Skin Emma
M ystic Haven Volcano View Gator Spinosaurus, Carnivore, Long Snout Jack
M ystic Haven Celestial Summit Glitter Unicorn, Herbivore, Glittering Hooves Sarah
M ystic Haven Celestial Summit Pegasus Unicorn, Herbivore, Feathered Wings Sarah
M ystic Haven Northern Plains Spike Triceratops, Herbivore, Friendly Emma
M ystic Haven Celestial Summit Starbeam Unicorn, Herbivore, Iridescent M ane Sarah
M ystic Haven Celestial Summit Willow Unicorn, Herbivore, Willow -like M ane Sarah
M ythoRealm Carnivore Cove Aqua Ichthyosaurus, Carnivore, Aquatic M ike
M ythoRealm Herbivore Heights Leaf Stegosaurus, Herbivore, Spiked Tail Kevin
M ythoRealm Crystal Springs M oonshine Unicorn, Herbivore, Luminescent Horn Olivia
M ythoRealm Carnivore Cove Rex T-Rex, Carnivore, Aggressive M ike
M ythoRealm Crystal Springs Starlite Unicorn, Herbivore, Star-shaped M arkings Olivia
M ythoRealm Herbivore Heights Thunder Brachiosaurus, Herbivore, Long Neck Kevin
Table
Column
Row

17. Relationships
Dataverse Types:
• 1:N – 1 row from table A can be related to
multiple rows from table B
• N:1 – multiple rows from table A can be
related to 1 row from table B
• N:N – multiple rows from table A can be
related to multiple rows from table B
• Manual N:N – multiple rows from table A
can be related to multiple rows from table
B via a custom intersection table C.
A –1:N– C –N:1– B
1
1
1
N
1
1
1
N
N
N
N
N
N
N
1 1
N
N
N
N
1 1
N
N
N
N

18. Relationships
Types available in other relational DBs – means extra work in Dataverse
• 1:0-1 – 1 row from table A can be related to 0 or 1 row from table B
• 1:1 – 1 row from table A is related to 1 row from table B
• 1:N+ – 1 row from table A is related to 1 or more rows from table B
• N+:1 – 1 or more rows from table A are related to 1 row from table B

19. Visualization of relationships
Table A
Table B
Table A
Table B
Table A
Table B
1
N
1
*
Table A
Table B
Pick the one you like and stick with it!

20. Referential integrity
• Restrict delete – aka Block killing of parents!
• Cascading delete – aka Kill the children along with the parent
Purpose: reduction of orphaned data.
Out of scope for this session:
• Restrict update
• Cascading update

21. Visualization of referential integrity
Table A
Table B
Table A
Table B
Table A
Table B
1
N
1
*
Table A
Table B
R
D
C
D

23. IoDaU - Internet of Dinosaurs and
Unicorns
1. We love dinosaurs and unicorns! They are awesome!
2. Our goal is to create a sanctuary where we can look after Dinosaurs and Unicorns
together.
3. Unicorns are majestic creatures and require a lot of space to flounce about.
4. Dinosaurs are morons and pretty gross! (but are hilarious)
5. Carniverous dinosaurs absolutely CANNOT share flouncing areas (enclosures) with
unicorns. They will just eat them all and extinct the species.
6. Caretakers require a particular set of skills to look after each animal.

24. SO what do we know?
1. We need a table to store Animals
2. We will classify the animals in a single table.
3. A hierarchy of enclosures will be required in each sanctuary.
4. Animals will be assigned to enclosures.
5. Caretakers will be assigned to enclosures based on skills.
6. Animals will be assigned Characteristics

25. Drawing the
data model

26. What do we know?
1. We need a table to store Animals
2. We will classify the animals in a single table.
3. A hierarchy of enclosures will be required in each sanctuary.
4. Animals will be assigned to enclosures.
5. Caretakers will be assigned to enclosures based on skills.
6. Animals will be assigned Characteristics

27. We need a table to store Animals
+ We will classify the animals in a single table.

28. A hierarchy of enclosures will be
required in each sanctuary.

29. A hierarchy of enclosures will be
required in each sanctuary.

30. Animals will be
assigned to
enclosures.

31. Animals will be
assigned to
enclosures.

32. Animals will be
assigned to
enclosures.

33. Animals will be
assigned to
enclosures.

34. Caretakers will
be assigned to
enclosures
based on skills.

35. Caretakers will
be assigned to
enclosures
based on skills.

36. Caretakers
will be
assigned to
enclosures
based on
skills.

37. Caretakers
will be
assigned to
enclosures
based on
skills.

38. Animals will
be assigned
Characteristi
cs

39. Animals will be assigned
Characteristics

40. Et Voilà!

41. Let’s review
the live
model

42. Wrap-up and Summarise
• Data First!! Yes, you may make a pretty thing but Data is paramount to the success of
your solution.
• Think about your data! Can you microservice ANY of it? Can it be made “reusable”. The
power of Normalised data is a well thought out data model.
• Go and read about this! Get some more info! Study up! Data modeling is an art form:
Get started using Microsoft Dataverse - Training | Microsoft Learn
Data Modeling 101 - The Agile Data (AD) Method

43. Thank you
Don’t forget the feedback!!