Data modelling is one of the most important steps in preparing data for BI analysis. Anyone working with data - either as an analyst or as a passive 'consumer' - should take a few moments to check out this slideshow and learn the bare basics of data modelling for business intelligence. To learn more about data modeling, watch our free webinar at http://bit.ly/1N095Sn

1. © Sisense Inc, 2015
MODELING DATA FOR BUSINESS INTELLIGENCE: KEY
CONCEPTS, TIPS & TRICKS
Presented by Sisense: Business
Intelligence Software for Complex Data

2. To enable data analysts to produce a new report, dashboard or just get a
new analytic question answered in real-time, or at least in-time.
The Goal of Data Modeling in BI:
© Sisense Inc, 2015

3. To Get There, Data Needs to Be:
ACCURATE
Records should be reliable
and reflect the reality of
the business
UP-TO-DATE
Data has to be complete
and pertain to the relevant
period
READY FOR ANALYSIS
Structured in a way that
lets you get answers to
new questions
© Sisense Inc, 2015

4. CEO: "We need to
increase our sales!"
MRKT MNGR: “What
other offerings can we
sell to customers?"
IT MNGR: While upgrading
platforms and implementing a
new CRM system, estimates that
the information will be available
in 20-30 days...
MRKT MNGR: A
month? Don't we
already have this
data in our system?
IT MNGR: Yes the data is
there but it DOESNT HAVE
THE RIGHT STRUCTURE to
answer those questions
MRKT MNGR: Keeps
thinking: If the data is there,
why is it so difficult to get
answers?
IT MNGR: Keeps
thinking: The
marketing manager
asks for weird things
with no time at all!
CEO: Just wants
to sell more
Most sold products?
Most successful product bundles?
Typical Business Challenge

5. DS DS DS
2. ETL (EXTRACT, TRANSFORM, LOAD):
Transform the data into a
workable format
3. Centralize the transformed
data to create a single source
of truth
5. Analyze: start asking
questions and visualizing
the answers
1. Locate and gather the relevant
data sources
4. Query/import: make the
data available and accessible
to analysts
Data Modelling Steps
DISPERSED
DATASETS
QUERY
LANGUAGE
STRUCTURE
SIZE
GROWTH
RATE
DETAIL
QUERY
LANGUAGE
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…and Challenges

6. MAP DATA
WHAT DATA DO I NEED?
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7. WHAT DATA DO I NEED? - MAP THE DATA
Facts Filter & OrderDimensions
Key business entities (subjects)
that we want to analyze
Performance measurements
A set of conditions and order
that specify the data subset
that we want to look at

8. 8
DIMENSIONS
Dimensions Are Mostly Categorical –
Each Has A Discrete Set Of Values
• Place – UK/USA
• Person - Customer
• Object - Products
• Time and Date - Year
• Process- Packaging
• Hierarchy – Country> City>Zip
FACTS
A set of conditions that specify the data subset
AND order in which to see the aggregations
FILTER & ORDER
• Greater than
• Between
• When
• True/False
• Range
Facts are presented in aggregate format: Max, Sum,
Average, Variance, Median, Count, Year-to -Date
• Number of transactions
• Quantity
• Amount
• Cost
• Revenue
• Discount
• Profit

9. 9
Correspondence Between Business Question And SQL Queries
Select <Dimensions>,
<Facts>
From <Tables>
Where <Conditions>
Group by <Dimensions>
Having <Conditions>
Order by <Order Specifications>
“What were the best-selling
products this year, per
country?
(show only products that
sold more than 20,000
units)”
Select Country, Product,
Sum (quantity)
From OrdersSales
Where Getyear( SaleDate ) = 2015
Group by Country, Product
Having Sum (quantity) > 20,000
Order by State, sum (quantity)
1 2 3
Business Question SQL Structure SQL Query

10. JOIN DATA
HOW DO I CONNECT DIFFERENT SOURCES?
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11. HOW DO I CONNECT DIFFERENT SOURCES? - JOINING DATA
Relationship Join Types Key
The way separate data sources
can reference each other
The total portion of data included when
connecting separate data sources
Field(s) used to connect
data sources

12. Data Relationships
Many-to-Many
SubjectStudent
How an instance of data from one source is related to data in another source
One-to-Many
SongArtist
One-to-One
WifeHusband
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13. Data Relationships
What portion of the connected data is required for analysis
Inner Join Left Join Right Join Full Join
Other Join Options

14. TABLE A: SALES
PRODUCT ID
EMPLOYEE ID
ORDER DATE
DELIVERY DATE
PRODUCT ID
CLIENT ID
AMOUNT
TABLE B: STOCK
PRODUCT ID
STOCK DATE
UNITS
COST
EMPLOYEE ID
Examples of Data Keys
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15. CLEAN DATA
HOW DO I WANT TO ANALYZE DATA?
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16. HOW DO I WANT TO ANALYZE DATA? – CLEAN DATA
Valid Accurate Complete & Consistent
Corrections related to missing,
incomplete, incorrect or inconsistent data
Data is precise and shows the right valuesData is correct and reasonable

17. Valid
Stable response
Example: Compare samples
Have a sufficient portion of data.
Example: Access comprehensive
portion of data
Measures what it is supposed to.
Example: Compare multiple
measurements
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18. Accurate
Data Capture
Example: Correct at source of entry
Data Decay + Movement
Example: Constant updates
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19. Complete and Consistent
Data correction
Example: Transform data
Data consistency
Example: Standardization
Data completeness
Example: Merge Data
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20. DATA MODELING IN SISENSE
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21. PREPARE FOR ANALYSISACCESS
Visual with
No Coding
Connect Directly to
Raw Data
Single Model - Many
Sources, Rows & Columns
Drag & Drop to Join Varied
Data Sources
Automatically Model
Based on Query
Complete Solution
ETL & Analysis
Change Incrementally
as Needed
ACCURATE + ON TIME
Ease of Modelling in Sisense
Synchronization

22. WANT TO LEARN MORE?
Visit sisense.com
To see real end-to-end business
analytics software in action

23. Image Credits
pakorn
Stuart Miles
winnond
adamr
sattva
markuso
Mister GC
John Kasawa
Images courtesy of
tungphoto
at FreeDigitalPhotos.net
© Sisense Inc, 2015