Business Analytics I

Alexander Prosser

First Generation BI

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ERP/other operational systems Data warehouse

Aggrega-tion

hierarchy

Graphical representation

First Generation BI

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ERP/other operational systems Data warehouse

Aggrega-tion

hierarchy

Graphical representation

Why aggregation?

In-memory databases:

Disk access – msMain memory access – nsDifference: 106

Second Generation BI

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ERP/other operational systems Data warehouse

Aggrega-tion

hierarchy

Graphical representation

In-Memory

ERP BI

Second Generation BI

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ERP/other operational systems

In-Memory

ERP BI

Graphical representation

Machine learningArtificial intelligence

Run through individual records

BI & AI

Second Generation BI

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“Large main memory”. How large is “large”?

https://www.ibm.com/downloads/cas/VX0AM0EP

Mere example, performance industry standard.

How much is 64 TB?

Average Netflix HD Movie 2 GB => 32k+ moviesNetflix currently offers less than 10k movies or TV shows*

High-quality portrait 1MB => 67m+ photos

… and you can search that content in main memory in a matter of a few seconds

* https://www.businessinsider.com/netflix-movie-catalog-size-has-gone-down-since-2010-2018-2?r=DE&IR=T?r=US&IR=T

Modelling

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The technology may have changed, fundamental case modelling has not.

=> Dimensions and facts => Dimensional Fact Modelling

Let us design a BI system

Modeling

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STEP 1:

What is the fact I want to analyze ?

What are the key figures representing the fact ?

What do the key figures look like ?

Modeling

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()

Operator Nominal Ordinal Interval Rational

Sum No No No

Average No

Minimum No

Maximum No

Modeling

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STEP 2:

What are the axes in my analyses ?

What are their aggregation levels (if any) ?

Modeling

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STEP 3:

Are there any restrictions in aggregation ?

Modeling

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Additivity

Stock_level

Y M W

Storage_location

Plant

*

Material

Material_group

*

=> AVG Σ

Σ

Σ

Modeling

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x

Σ

AVG

min

max

Some dimensions All dimensionsSome aggregation operator Semi-additive Semi-additiveAll aggregation operators Semi -additive Additive

These are logical restrictions.

No technology in the world changes that.

Modeling

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STEP 4:

Do I have parallel hierarchies ?

Modeling

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STEP 5:

Where does the data come from ?

Do I need to reconcile data from different sources ?

Modeling

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Operational IS

DW

Key Integration

Key_1

Key_2

Example:• Accounts receivable• Customer• Transport destination

one object inDW

Modeling

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Operational IS

Field Integration

• Currencies• Measurements• Scope of figures (eg, gross/net)• … ?

• All fields available ?

DW

Filter:

Modeling

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Operational IS

Content Integration

• Material classes the same ?• Account assignment the same ?• Data maintenance discipline/rules the same ?• … ?

DW

Example MM/Procurement:

Modeling

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Dimensional Fact Modelling

Case Study: Mobile phoneprovider helpdesk

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The company looses a large number of customers and wonders why …

Helpdesk calls are to be analysed for customer sentiments towards three topics:- Price- Technical quality - Service quality

This is a combined task for second-generation BI and AI.

Case Study: Data model

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Interaction DateCustomerCustomer group

Interaction type

Download the data …

Case Study: Data flow

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Data sourcefile

Data sourceHana

Data source3rd party

aDSO Corporate memory

Character-isticsKey figures

“Cube”aDSO

Data warehouse core layer

Data source

Case Study: Reporting

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“Cube”aDSO

Reporting.xls

Virtual data mart

Case Study: Voice data analysis

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Voice clips Text filesSpeech to text

Case Study: Voice data analysis

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Text files Clean text filesExtract

& split

R

“Hello, my display is super and your service leaves a lot to be desired”

“1. my display is super” “2. your service leaves a lot to be desired”

Case Study: Voice data analysis

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Annotated text elements

Annotat-ionClean text files

Categories.csv

“1. my display is super” “2. your service leaves a lot to be desired”

Case Study: Voice data analysis

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Annotated text elements Input HANA

Senti-ment

analysis

Sentiment library

“1. my display is super” “2. your service leaves a lot to be desired”