demystifying data modeling -...

DEMYSTIFYING DATA MODELING

© Sisense Inc, 2015

→Goals of Data Modelling

AGENDA

→The Challenge of Data Modelling

→Process of Data Modelling

→Three Challenges

→What Data Do I Need? - Map Data

→How Do I Connect Different Sources? – Join Data

→How Do I Want To Analyze Data? – Clean Data

→Demo: Sisense Data Modelling


GOALS OF DATA MODELLING


The ability to create a new report, dashboard or just get a new analytic question answered in real-time, or at least in-time.

…What's the Goal


What Business Needs

ACCURATE UP-TO-DATE READY FOR ANALYSIS


CHALLENGE OF DATA MODELLING


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?

Imagine This Scenario

DISPERSED

WHAT MAKES DATA COMPLEX

SIZE

QUERY LANGUAGE

TYPE

STRUCTURE

GROWTH RATE

DETAIL


DS DS DS

ETL (EXTRACT, TRANSFORM, LOAD)

CENTRALIZE

ANALYZE

DATA SOURCES

QUERY/IMPORT

Modelling Steps

DISPERSED

QUERY LANGUAGESTRUCTURE

SIZE GROWTH RATE

DETAILQUERY LANGUAGE


MAP DATAWHAT DATA DO I NEED?


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

12

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

13

Example Business Inquiry Structure

Show me an aggregation of certain

along certain

under certain

in a certain

Do certain stores sell “Bikes” significantly better than others?Do certain stores sell “Bikes” significantly better than others?

FACTS

DIMENSIONS

FILTER CONDITIONS

ORDER

14

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?


HOW DO I CONNECT DIFFERENT SOURCES? - JOIN 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


Data Relationships

What portion of the connected data is required for analysis

Inner Join Left Join Right Join Full Join

Other Join Options© Sisense Inc, 2015

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

Data Keys


CLEAN DATAHOW DO I WANT TO ANALYZE DATA?


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


Accurate

Data Capture

Example: Correct at source of entry

Data Decay + Movement

Example: Constant updates


Complete and Consistent

Data correction

Example: Transform data

Data consistency

Example: Standardization

Data completeness

Example: Merge Data


DATA MODELING IN SISENSE


PREPARE FOR ANALYSSACCESS

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

DEMO:

Sisense Data

Modelling Environment

Thank you

Image Credits

pakorn

Stuart Miles

winnond

adamr

sattva

markuso

Mister GC

John Kasawa

Images courtesy of

tungphoto

at FreeDigitalPhotos.net


demystifying data modeling -...

Documents