Modeling Data Transformations in SAP NetWeaver Business Intelligence
The Definitive Guide to Data Modeling for Business Intelligence
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Transcript of The Definitive Guide to Data Modeling for Business Intelligence
© Sisense Inc, 2015
MODELING DATA FOR BUSINESS INTELLIGENCE: KEY CONCEPTS, TIPS & TRICKS
Presented by Sisense: Business Intelligence Software for Complex Data
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
To Get There, Data Needs to Be:
ACCURATERecords should be reliable
and reflect the reality of the business
UP-TO-DATEData has to be complete
and pertain to the relevant period
READY FOR ANALYSISStructured in a way that lets you get answers to
new questions
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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
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 LANGUAGESTRUCTURE
SIZE GROWTH RATE
DETAILQUERY LANGUAGE
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…and Challenges
MAP DATAWHAT DATA DO I NEED?
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WHAT DATA DO I NEED? - MAP THE DATA
Facts Filter & OrderDimensions
Key business entities (subjects) that we want to analyze
Performance measurementsA set of conditions and order that specify the data subset
that we want to look at
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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
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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
JOIN DATA HOW DO I CONNECT DIFFERENT SOURCES?
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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
Data Relationships
Many-to-ManySubjectStudent
How an instance of data from one source is related to data in another source
One-to-ManySongArtist
One-to-OneWifeHusband
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Data Relationships
What portion of the connected data is required for analysis
Inner Join Left Join Right Join Full Join
Other Join Options
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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CLEAN DATAHOW DO I WANT TO ANALYZE DATA?
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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 values Data is correct and reasonable
Valid
Stable response
Example: Compare samplesHave 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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Accurate
Data Capture
Example: Correct at source of entry
Data Decay + Movement
Example: Constant updates
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Complete and Consistent
Data correction
Example: Transform data
Data consistency
Example: Standardization
Data completeness
Example: Merge Data
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DATA MODELING IN SISENSE
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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
WANT TO LEARN MORE?
Visit sisense.com
To see real end-to-end business analytics software in action
Image Credits
pakorn
Stuart Miles
winnond
adamr
sattva
markuso
Mister GC
John Kasawa
Images courtesy of
tungphoto
at FreeDigitalPhotos.net
© Sisense Inc, 2015