BIG DATA GREAT VALUE.

(1)

BIG DATA – GREAT VALUE.

(2)

ON THE LOOKOUT FOR NEW SOURCES OF VALUE

CREATION WHAT WILL DRIVE BUSINESSES IN FUTURE?

CREATION. WHAT WILL DRIVE BUSINESSES IN FUTURE?

From the black gold of the

i d t i l

t th



Old sources dry up, while

industrial era to the new

riches of the information

age.

new ones emerge.



The future belongs to the

(3)

BIG DATA: FAST-GROWING RAW MATERIAL DEPOSITS.

RESOURCES ASKING TO BE DEVELOPED

RESOURCES ASKING TO BE DEVELOPED.

Data volumes

double every 18

months

5 million

transactions

t a sact o s

per second

24 Exabytes

of data growth

_85%

250 million

emails a day

of data growth

per day

85%

_unstructured

(4)

SOME KEY FIGURES.

IN A 60 SECOND FLASH

IN A 60-SECOND FLASH.

695,000

STATUS UPDATES

168 M

EMAILS

SENT

600+

NEW

VIDEOS

11 MILLION

INSTANT

MESSENGERN

SENT

6 600+

MESSENGERN

CONNECTIONS

6,600+

NEW PHOTOS

694,445

QUERIES

2,100

CHECK-INS

90,000+

TWEETS

$ 219,000.–

_REVENUE

(5)

BIG DATA.

FEATURES AND ADDED VALUE

FEATURES AND ADDED VALUE.

ANALYTICS

t

VALUE

value comes from knowing more than the rest

creates

(6)

SEEING THE OPPORTUNITIES.

RECOGNIZING NEW POTENTIALS

RECOGNIZING NEW POTENTIALS.

Complex simulations and trend analyses

improve and accelerate product

p

development and market maturity.

_{Make decisions}

faster and more

intelligently.

Better insights into markets and

customer needs enable tailormade

products and services

products and services.

Machines and sensor data

optimize production processes.

Traffic data and route planning

saves costs and CO

_2,

and improves

logistics and distribution.

Projecting financial data

results in better forecasts,

indentifies fraud and risk

mitigation.

Opportunities for

new business models.

(7)

BIG DATA MINING:

THE VALUE CREATION PIPELINE

THE VALUE CREATION PIPELINE.

Mining for data

processing

transporting

storing

refining

implementing

(8)

MINING FOR DATA.

IN HOUSE AND EXTERNALLY!

IN-HOUSE AND EXTERNALLY!

Mining for data

processing

transporting

storing

refining

implementing

Until now: updated answers to structured

databases and repetitive, with mostly

standardized questions.

Until now: updated answers to structured

databases and repetitive, with mostly

standardized questions.

Today: New answers on the basis of

unstructured data and creative,

variable questions.

Today: New answers on the basis of

unstructured data and creative,

variable questions.

!

Perfect:

?

link up the two

Perfect:

link up the two

Weather

Weather data

data

_Laws,

_{Laws, guidelines}

_guidelines

Reports

Health

Health data

data

Tweets

Traffic data

Measurement

Measurement data

data

pp

Machine

Machine and

and

sensor

sensor data

data

Tweets

Tweets, ,

Likes

Likes & Co.

& Co.

JPEG, PDF,

Financial

Financial data

data

And

And a

a lot

lot more

more …

…

JPEG, PDF,

etc.

(9)

PROCESSING DATA IN REAL TIME. WITH THE RIGHT

TECHNOLOGIES AND PROCESS KNOW HOW

Mining for data

processing

transporting

storing

refining

implementing

TECHNOLOGIES AND PROCESS KNOW-HOW.

Business

Problem



Backward-looking analysis



Using data out of business

li i



Quasi-real-time analysis

(In-Memory)

U i d

f b i



Forward-looking predictive

analysis

Q

i

d fi d i h

Legacy BI

High performance BI

„Hadoop“ Ecosystem

applications



Using data out of business

applications



Questions defined in the

moment, using data from

many sources

Technology

Solution



SAP Business Objects



IBM Cognos



MicroStrategy



Oracle Exadata



SAP HANA



Cloudera Hadoop distribution



Splunk (visualization)

Selected Vendors

c oSt ategy



Structured



Limited (2 – 3 TB in RAM)



Structured



Limited (1 PB in RAM)



Structured or unstructured



Quasi unlimited (20 – 30 PB)

Data Type/Scalability

(10)

TAKING THE EXISTING WITH YOU.

GENERATING MORE EFFICIENCY

GENERATING MORE EFFICIENCY.

Mining for data

processing

transporting

storing

refining

implementing

BUSINESS INTELLIGENCE TOOLS AND ANALYTICAL APPLICATIONS

Reporting

Dashboard

Analyse OLAP

Data & Text Mining

Predictive

_Analytics

Operational

_Intelligence

Complex event

processing

Stuctured and

unstructured data

Data

Warehouse

Appliance

Data Mart

Cube

Real-time data

_{processing and}

analysis

Business

_Hadoop

_Cloud

Static data

Flowing data

Data integration ETL

EXISTING DATA SOURCES

Transactional

OLTP DBMS

Business

Applications

ERP, CRM, etc.

Hadoop,

NoSQL,

Log-Daten

Cloud

SaaS

(11)

SAVE DATA SECURELY.

IN THE BIG DATA CLOUD FROM T SYSTEMS

IN THE BIG DATA CLOUD FROM T-SYSTEMS.

Mining for data

processing

transporting

storing

refining

implementing

90 Twin Core Data

Centers worldwide

with 120 000m²

Legacy BI, In-memory

technology and

with 120,000m

total surface area.

Hadoop Ecosystem

from one source

99.98%

availability

guaranteed

guaranteed.

Strictest security standards

and German data

protection guidelines.

(12)

REFINING DATA. ANSWERS TO QUESTIONS YOU DIDN’T

EVEN THINK TO ASK

EVEN THINK TO ASK.

Mining for data

processing

transporting

storing

refining

implementing

Automate semantic

Recognize patterns,

meanings, correlations

analyses

Preparing analyses and

making them of universal use

Data Scientists

ANALYTICS

t

meanings, correlations

a a Sc e s s

g

wanted

creates

VALUE

(13)

IMPLEMENTING BIG DATA TO GENERATE PROFIT.

SELECTED USE CASES

SELECTED USE CASES.

Mining for data

processing

transporting

storing

refining

implementing

 Automatic research of video, audio

and online print files

 Semantic analyses and results

visualization practically in real time

Intelligent News Discovery

Threats identified securely and

blocked immediately

Comprehensive monitoring of

unlimited data volumes and types

Realtime Security Analytics

Real-time reaction to vehicle conditions

and traffic situations

Connected Car: Traffic and Diagnostics

Driving tips in real time

Competitive advantage thanks to cost

Efficient Fleet Management

Smarter Procurement

Increased customer loyalty due to individual

service provision

Secure product development

Campaign Analytics

p g reductions

Lower fuel consumption and CO₂emissions Better planning of routes and cargo loads

Smarter Energy Management

 Transparency across all

suppliers and prices

 Stronger negotiating position

in purchasing

 Efficient cashflowmanagement

Smarter Procurement

Real-time monitoring of

marketing campaigns

Consideration of all sources

and formats

Efficient campaign management

Campaign Analytics

Optimized use of resources

for all energy sources due to real-time forecasts

Forecasts in real time Customer-specific prices

(14)

T-SYSTEMS BIG DATA.

THE ADVANTAGES AT A GLANCE

THE ADVANTAGES AT A GLANCE.

Mining for data

processing

transporting

storing

refining

implementing

Provision of an end-to-end value

creation pipeline for business

i t lli

& Bi D t

l ti



Mining for data, processing, transporting,

saving, enhancing and implementing it

profitably

intelligence & Big Data solutions:

profitably



Also as an Analytics-as-a-Service/

On-demand model



Best price, best function technologies

p

,

g



Available immediately, simple and fast

scalability



High-performing VPN/MPLS network

i f t t

infrastructures



Transition concepts for entry into the

(15)

YOUR BIG DATA MINING PROGRAM.

BIG DATA READINESS ASSESSMENT

BIG DATA READINESS ASSESSMENT.

ASSESSMENT IN 3 PHASES



Evaluation

Phase 2



Development of your

Bi D

Phase 3



Analysis of challenges

Phase 1



Prioritizing the

Big Data potentials



Solution design



Operation and

Big Data strategy



Defining your Big Data

roadmap

facing you



Identification of relevant

systems and processes



Operation and

maintenance concept

Simulation of selected

scenarios with initial

cost-benefit analysis

Strategy with

compre-hensive analysis of

costs savings

Specifying the potentials

and requirements

cost-benefit analysis

costs, savings

potentials, ROI and

business case

(16)

OUR OFFER FOR A TRIAL RUN.

WHERE DO YOU STAND?

Big Data optimization

Sustainable optimization of your business

5 Assessment Phase 1



Analyse Ihrer

Phase 1

_{Big Data execution}

Big Data optimization

Sustainable optimization of your business

First processes optimized

4

5 

Analyse Ihrer

Herausforderungen



Identifikation der

relevanten Systeme

und Prozesse

Big Data strategy

First projects before finalization

3 und Prozesse

Konkretisierung

der Potenziale und

A f d

Big Data initiatives

Big Data CoE

First projects launched

Fi t t

&

P C

1

2 Anforderungen

Legacy applications

Big Data initiatives

No Big Data

First concepts & PoC

0

1

(17)

(18)

DATA PROCESSING AND ANALYSIS IS NOT NEW.

THE QUALITY AND QUANTITY ARE

THE QUALITY AND QUANTITY ARE.

Over the past 50 years, operative data have been summarized, evaluated and presented to

management to support decision-making processes.

DWH

OLAP

Data

Mi i

CPM

BPM

MIS

DSS

EUS

EIS

FIS

Analytical

Mining

Operational

BI

1960

_FIS

2013

EIS

FIS

Analytical

Applications

Business

Analytics

Based on: Humm B /Wietek F (2005 S 4) Based on: Humm, B./Wietek, F. (2005, S. 4)

(19)

BIG DATA USE CASES BY BUSINESS FUNCTION.

Supply Chain Optimization controlling own and OEM

d ti it Production Optimization using

Sensor Data and M hi 2 M hi Using Online Forums for

Product Development & S ti t A l i

Customer Individual Discounts for products on websites and call

t ( lti f t l ti ) Online Marketing

Campaign Optimization

Marketing & Sales

Product Development &

Research

Product Service &

Support

Distribution & Logistics

Finance & Controlling

production capacity Machine 2 Machine

Communication Sentiment Analysis

Social Media Usage

for Macro/Micro Trend analysis Massive Parallel Processing for Drug Testing in Pharma

Predictive Maintenance & Prediction (Combat unwanted production stops)

Truck transportation optimization (transport order navigational data, combined with traffic data)

centers (multi factor, real time) Financial Simulation and Scenario Calculations Financial Simulation and Scenario Calculations Big Data for Point of Sales

Optimization/Cross Selling Big Data for Point of Sales Optimization/Cross Selling

g g

CERN number crunching for test data (40GB/sec)

Production Planning for Seasonal Goods (multi factor )

Road Charge Optimization (real time adaptation of fees

according to current traffic)

Online Fraud Detection (Credit Card transactions, etc.) Risk Controlling

(Market Risk/Value at Risk) Competitive Analysis

using Online Press,

Social Media with Scraping and Text Analysis

Customer Churn Analysis

for Prepaid Telco business _{Detection of unknown financial} risk (e.g. for real estate loans) Optimize Target Group

Marketing for online banking based on trading/depot transactions

for Prepaid Telco business (behavior based)

(20)

BIG DATA MARKET POTENTIAL.

Global Big Data Market

25.000 30.000

CAGR 2012/2016

Services:

+ 41 %

5 000 10.000 15.000 20.000 25.000

Services:

41 %

Software:

+ 45 %

Hardware: +

32 %

Breakdown per Region in 2016

Breakdown by Vertical in 2016

0 5.000

2011 2012 2013 2014 2015 2016

Source: PAC

Breakdown per Region in 2016

y

6% Germany

25% Western

E

25% Rest of

the World

30% Banking

19% Others

8% Insurance

_Europe

Source: PAC

16% Manufacturing

11% Public

8% Insurance

8% Telecommunication

8% Retail

44% USA

(21)

POTENTIAL OFFERED BY BIG DATA TECHNOLOGY

IN TERMS OF BUSINESS

IN TERMS OF BUSINESS.

I

t f

li

t

Precise financial reporting

Data governance

27

24

22 N = 254

Optimizing existing business cases

Recognizing new potential for business

Improvement of compliance aspects

30

28

27 Detailed information

Better information basis for corporate decisions

Optimizing existing business cases

33

31

30 Better information management

Better corporate control

36

33 Cost optimization

Information obtained faster

45

42

Source: IDC-Survey “Big Data in Germany” 2012 Source: IDC-Survey Big Data in Germany , 2012

(22)

COMMON APPLICATION MISTAKES

IN BIG DATA PROJECTS

IN BIG DATA PROJECTS.

HR

Other

Brand Management

8

12

12 Companies in many fields of

business gain vital knowledge

by evaluating Big Data.

The most important application

areas are marketing sales and

Customer Service

Logistics

12

22

30 areas are marketing, sales and

operational management.

IT Analytics

Finance

Product Development

32

33 Sales

Risk Management

IT Analytics

33

35

38 Marketing

Operations

43

45

Source: Forrester Research Inc : How Forrester Clients are using Big Data September 2011 Source: Forrester Research, Inc.: How Forrester Clients are using Big Data, September 2011