ANALYTICS WITHOUT LOSS OF GENERALITY

ANALYTICS WITHOUT LOSS OF GENERALITY

http://wlogsolutions.com/en/

http://wlogsolutions.com/en/

AbstractWe are now in a "competing on analytics" (aka data-science) era. Unfortunately mostly it is understood as predictive modelling. We would like to show that data science is much more than this. We will present general architecture of any data science solution using selected case studies from our projects.

Copyright (c) WLOG Solutions 2

Why is it important?


Motivating example

Customer

• I want to improve demandforecasting model(s).

Me

• Why do you need forecastsfor?

Customer

• To make optimal resourceallocation.

…• …


Data ForecastsResource

allocation

Stages of the whole decision process

Why is it important?

• Companies want to compete on analytics at any business

level which means

• predictive analytics is not enough,

• optimization is a must to generate optimal recommendations at

low business level,

• simulation is a must to understand influence of unpredictable

factors,

• analytical toolbox must be flexible to minimize time-to-market.


What one can gain?

• More precise recommendations leads to better decisions.

• Automated decision making process leads to controllable

costs and safer business.

• Less expert guessing leads to healthier business.

• General problem tackling leads to minimized time-to-

market.


Analytics Without Loss Of Generality


Analytics without loss of generality

Data analysis

What are the facts?

How we can use them for our Organisation?

How the facts support/deny our expert knowledge?

Optimization

What are optimal course of actions?

How far are my decisions from being optimal?

Simulation

What are possible future scenarios?

How can I measure risks that our Organization is exposed to?


What are the main requirements for an analytical framework?

Flexible

• Can tackle anybusiness problem

Accessible

• Can do a prototype fast and cheap

Scalable

• Can scaleusing moremachines

Efficient

• Can get goodquality modelsfast


WLOG* Analytics architecture™ (1)


*WLOG = Without Loss of Generality

WLOG* Analytics architecture™ (2)

Flexible

•R

•Python

Accessible

•Open-sourcewhere possible

Scalable

•Spark

•Cloud

Efficient

•Selected and tested libraries

•Java if needed


Our toolbox (1)

Distributed computing

Flowmanagement

Visualization


Our toolbox (2)

ETL

•Spark

•R

•Python

Predictivemodelling

•R

•H2O

•MXNET

•XGBOOST

Optimization

•COIN-OR

•ECLiPSe

•Choco

•Gecode

•Java (!)

Simulation

•R

•MASON

•Spark

•Julia (testing)

Visualization

•Python

•Javascript


What did we get as a result?

• Flexible,

• productive,

• scalable,

• with great price to quality ratio

platform to tackle almost any business problem.


Case studies


Case studies

• Cash optimization in Deutsche Bank

• Midterm Energy price simulation


Cash optimization in Deutsche Bank (1)


CENTRAL

BANK

GROUP OF COOPERATING BANKS

VAULT

VAULTBRANCH

ATM

CORPORATE

CUSTOMER

RETAIL

CUSTOMER

deposit or

withdraw

deposit or

withdraw

buy or sell

buy or sell

closed

payment

closed

payment

closed

payment

cash

transfer

cash

transfer

Cash optimization in Deutsche Bank (2)

ETL

•End-of-day process

•Current balances

Demandforecasting

•Around 800+ forecasts

•Done in R

Cash movementrecommendations

•Done in CBC

•Workflow in R


Example


Before optimization:

Time: 3 months

Deposit transports: 20

Withdrawal transports: 0

After optimization:

Time: 3 months

Deposit transports: 3

Withdrawal transports: 0

Midterm Energy price simulation (1)


Statistical model for SPOT prices

Marginal costs

fundamental

model

Balancing market

fundamental

model

Demand model Supply model

• Complex model consisting of 5

submodels.

• Simulation is done on model

parameters:

• Temperature

• Wind

• Regulation

• Fuel prices (e.g. coal)

Example


9 scenarios:

1. Temperature: low, medium, high

2. Wind: weak, medium, strong

Montecarlo simulations

1. Temperature & wind

2. Peak probabilities

Summary


Key points

• Prediction is just a step to make a decision (optimal).

• Data science is expected to support any business decision.

• One should have tools covering three aspects: predictive,

optimization and simulation models.

• Open-source gives us flexibility and a great price/quality ratio.



Q&A

Wit Jakuczun, PhD

CEO

Email: [email protected]

Mobile: +48 601 820 620

Skype: jakuczun

WWW: http://www.wlogsolutions.com/enCopyright (c) WLOG Solutions 25

Thank you!

mailto:[email protected]?subject=Advanced Analytics Services Inquiry

http://www.wlogsolutions.com/en

ANALYTICS WITHOUT LOSS OF GENERALITY

Data & Analytics