ECMS801021 STRATEGIC MANAGEMENT BIG DATA AND …

Faculty of Economic and BusinessDepartement of ManagementGraduate School of Management

ECMS801021 STRATEGIC MANAGEMENT

* Des’ 2018

Dr. Ir. Manerep Pasaribu

Disampaikan pada Kuliah Sesi ke-14: SHARING ECONOMY

• The Rise of Sharing Economy• Spontaneous Deregulation

BIG DATA AND STRATEGIC ENTREPRENEURSHIP

2

ECMS801021 STRATEGIC MANAGEMENT Kuliah Sesi ke-14: Sharing Economy


Faculty of Economic and Business Departement of Management Graduate School of Management

* Des’ 2018

• Introduction to Big Data

• Data as the new oil.

• Data:

• Small Data

• Big Data

• Data Strategy

• Big Data in Practice

• Entreprenurship and Business Model

• Millennial Generation

• Q & A-Conclution

AGENDA :

1.

2.

3.

4.

5.

6.

7.

8.

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Introduction

to Big Data.1

4




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BIG

DATA

Internet

Algorithm

Digital Economy

People AnalyticData Science

Cloud Computing

Smartphone

Online Interaction

Facebook

Google

Twetter

Digital Technology

Platform

Hadoop

Microsof

tInstagram

WhatsApp

Artificial Intelligence

RoboticsIoT

Etc.

We now live

in the digital

and Big Data era

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Industrial Evolution

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The Three Imperialist Big Waves(“Scandal of Facebook and Imperialism 3.0”, Yuswohady, Koran Sindo, Minggu, 8 April 2018)

IMPERIALISM 1.0 IMPERIALISM 2.0 IMPERIALISM 3.0

What to Conquered… Land Internet Big Data

ConquerorNation, global

corporationDigital Corporation

Corporaticracy, data

analytic company

Enabler Weapons/battleshipsDigital biz models &

capital

Algorithms & data

analytics

Monetization Industrial commerce Digital commerce Data commerce

Economies Global economy Digital economy Surveillance economy

Influential Players EIC, VOC, etc. The Big FourFacebook, Cambridge

Analytics, etc.

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* Des’ 2018

Five IT Megatrends(Information Systems Today, Managing in the Digital World, 8TH, Global Edition, Pearson, 2018)

Changes in

Organizations

and Society

BIG DATA

Social Media Cloud Computing

Mobile Internet of Things

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TRANSLATING DATA INTO DECISIONS

Sumber: David A.J. Axson (2003)

• Algorithms

• Machine learning

• Statistics

Analytics

Experts

Statisticiens

The basic of decision

making

DATA INSIGHTS

ACTIONS DECISIONS

KNOWLEDGEINFORMATION

Small Data

BigData

• Implementation of SOP

• Evaluation

• Result

• Strategy

• Policy

• SOP

• Trends

• Ideas

• Solutions

.

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“Physical World” “Digital World”

BIG

DATA

“BUSINESSES”

“COMPANIES”

(Pelayanan,

Kinerja, dan

Daya Saing)

• Social Media

• Mobile devices

• Web/Online interaction

• Activities or Behavior

→ Big Data

• Sensors

• Machines Learning

(Collect, Store, processing

Information)

• Algorithms

“Address the most pressing

questions to our business, our

lives, our world”:

• More informed decisions

• Adapt to change more quickly

• Better understand the world

around us

“Tanpa disadari,

setiap hari kita telah

membagi aneka informasi

pribadi, mulai dari perbincangan

sosial, kebiasaan belanja, pola

bepergian, hingga transaksi

keuangan, melalui berbagai

aplikasi berbasis internet yang

kita miliki.”

(Junanto Herdiawan,

Kepala Bank Indonesia

Fintech Office, 2017)

(Industrial Revolutions 1-3) (Industrial Revolutions 4)

Teknological Digital & Internet

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«The real challenge in

using Machine Learning

(ML) is finding algorithms

whose learning bias is the

most suitable for a

particular data set»

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«Data types of an attribute

(numeric, ordinal, nominal)

affect the methods we can

use to analyze and

understand data»

12




* Des’ 2018

«The key to success is

getting the right data

and finding the best

attributes»

13




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«The algorithm will control

every aspect of our lives»Henrik von Scheel,

Advisory Board Member, Google

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* Des’ 2018

Data, Information, and Knowledge(Information Systems Today, Managing in the Digital World, 8TH, Global Edition, Pearson, 2018)

DATA

465889727

Raw

Symbols

Meaning:

---------------

???

INFORMATION

465-88-9727

Formatted

Data

Meaning:

---------------

SSN

(Social Security Number)

KNOWLEDGE

465-88-9727 → John Doe

Data

Relationships

Meaning:

---------------

???

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15

WISDOM

KNOWLEDGE

INFORMATION

DATA (SMALL DATA & BIG DATA)

Dari Data Menjadi Informasi dan Knowledge(DIKW Framework, Ackaff, 1989; Sveiby.com, 2013)

PEOPLE FOCUS

• Analitics

• Experts

• Para Ahli Statistik

(Scientist)

• Knowledge worker

• Dapat mengambil keputusan hanya

dengan intuisi.

• Rich knowledge (experience, tanpa

mengambil data).

• Allows us to make decision when we

don’t know what to do (i.e. intuition in

uncertain situation).

• Hasil analisis dan diagnosa manusia

atau kelompok manusia dalam rangka

kepastian.

• Proses sosial (sosialisasi).

• Give us certainty on what to do.

• Koran, buku, TV, Social

Media, Jurnal, Tabel-tabel

yang menghubungkan

kejadian.

• Will help us know what to do.

• Fakta (tanpa

dihubungkan

dengan kejadian

lain).

• As it stands lone will

not help us.

SYSTEM FOCUS

• Algorithm

• Machine

learning

• Contoh:

Facebook,

Google, etc.

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* Des’ 2018

Proses dari Data Menjadi Informasi dan Knowledge

WISDOM

KNOWLEDGE

INFORMATION

DATA (SMALL DATA & BIG DATA)

• Dipahami• Diterapkan

• Dibandingkan• Dihubung-hubungkan• Dikomunikasikan

• Divalidasi• Dikelompokkan• Diolah

Inovasi, kebijakan/prinsip-prinsip.

Informasi yang siapdiaplikasikan dalamsuatu konteks.

Data yang mengandung pesan.

Fakta mengenaikejadian.

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* Des’ 2018

Framework Integrating Big Data and Knowledge Management(Sumbal and See-to, 2017)

KNOWLEDGE

FOCUS

• KM System

Improved Decision Making and Business Performance

Processing Data (Analytics)

Big Data (Structured, Semistrutured,

Unstructured)

Knowledge Creation and Discovery through smart

algorithms

Knowledge Creation and LeraningTransformation SECI Model

1. Expert’s insights (tacit

knowledge)

2. Codifying new knowledge

3. Updating the existing

knowledge bases or

algorithms for future use

SYSTEM FOCUS

• Algorithm

• Machine learning

• Contoh: Facebook,

Google, etc. PEOPLE FOCUS

• Analitics

• Experts

• Para Ahli Statistik

(Scientist)

• Knowledge worker

18




* Des’ 2018

• The quality of the algorithm used to process, and solve data (for example: Algorithmic Trading on Financial Trade).

• The level of talents that has been hired for data processing.

• Google's success is about "recipes", not "ingredients" (Google's success is about recipes, not ingredients).

• Halvarian, Google: "I continue to state that sexy (interesting) work in the next 10 years will be a field of statistics, and I'm not kidding"

THE MAIN FACTORS AFFECTING BIG DATA QUALITY

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Data as the new oil2

20




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20

21




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21

22




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22

• ‘Big Data’ is similar to ‘small data’, but bigger in size.

• But having data bigger it requires different approaches: Techniques, tools and architecture.

• Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them.

What is

Big data?

23




* Des’ 2018

Sources of Big Data

• Social Media Data

• Black Box Data• Stock

Exchange Data • Transport Data • Power Grid

Data • Search Engine

Data

24




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24

• Social media such as Facebook and Twitter hold information and views posted by millions of people across the globe.

Social Media Data:

• It is a component of helicopter, airplanes, and jets, etc. It captures voices of the flight crew, recordings of microphones and earphones, and the performance information of the aircraft.

Black Box Data:

• The stock exchange data holds information about the ‘buy’ and ‘sell’ decisions made on a share of different companies made by the customers.

Stock Exchange Data:

Contoh:

25




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25

• Transport data includes model, capacity, distance and availability of a vehicle.

Transport Data:

• Search engines retrieve lots of data from different databases.

Search Engine Data:

• The power grid data holds information consumed by a particular node with respect to a base station.

Power Grid Data:

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Data:• Small Data

• Big Data

3

27




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27

Type of

Data

Structured (traditional relational

databases)

Unstructured(documents,

customer service records, pictures, videos

and photos)

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28Slide 4 www.edureka.co/big-data-and-hadoop

Un-structured Data is Exploding

Source: Twitter

Un-structured Data is Exploding

Source: Twitter/www.edureka.co/big-data-and-hadoop

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29

"Big Data is the foundation of all

megatrends that will occur today, tomorrow, the day after tomorrow,

and the future"

30




* Des’ 2018

MODEL BISNIS7• Three Driving Forces

• Sharing Economy

• Sharing Economy Business Model

• Business Model & Strategy

• Traditional Business Model and

Social Business Model

31




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31

BUSINESS MODEL CANVAS

32




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THREE DRIVING FORCES

Sumber: Based on Jeffry Timmons’ Framework

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33

SHARING ECONOMY

Sumber: Kredit: http://bmtoolbox.net/wp-content/uploads/2016/06/SharingEconomy.jpg

34




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SHARING ECONOMY BUSINESS MODEL

Sumber: Kredit: Air Cargo News, http://www.dpdhl.com/content/dam/dpdhl/presse/img/2017/sharing-economy-

infographic-01-900.jpg

35




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BUSINESS MODEL AND STRATEGY

Sumber: Kredit: http-

::s21.q4cdn.com:589145389:file

s:images:BMS:BM:DPM_Pyrami

d-Business-Model.jpg

36




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BUSINESS MODEL

Sumber: Kredit: https-

::www.safaribooksonline.

com:library:view:busines

s-models-

for:9781118612750:image

s:9781118612750-

pp0201_fmt.png

37




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OUR BUSINESS MODEL AND STRATEGY

Sumber: Kredit: http-

::www.barrattdevelopments.co.

uk:~:media:Images:B:Barratt-

Developments:content-

images:business-model-

graph.jpg?la=en.jpg

38




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TRADITIONAL BUSINESS MODEL

AND SOCIAL BUSINESS MODEL

Sumber: www.ericsson.com:assets

39




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Entrepreneurship

6

40




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40

Megatrends and

Trends are

Opportunities for

an Entrepreneur.

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• Entrepreneurship is the dynamic process of creating wealth.

• Entrepreneurship is not only being entrepreneur. Its also builds a mentality of entrepreneur (Entrepreneurial Mindset).

Entrepreneur/Entrepreneurship

41

Peter F. Drucker, 1985

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* Des’ 2018

BIG DATA & ENTREPRENEURSHIP

Knowledge

Innovation

Competitive Advantage, Wealth Creation, Change

Entrepreneurship

Entrepreneuria

l Mindset

Entrepreneuria

l Culture

Entrepreneuria

l Leadership

Resource

s

Sumber: The Knowledge-Creating Company (Nonaka & Takeuchi, 1995), dimodifikasi Manerep Pasaribu

Big DataInformation

Knowledge

Wisdom

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43

TEORI PERTUMBUHAN

(Growth Theory)Entrepreneurship Economic Growth Development

(Schumpeter, 1951; Ronstadt, 1984)

Sumber: Tempo, 3-9 Oktober 2016 (diolah)

0 2 4 6 8 10 12 14

Indonesia

Malaysia

Singapura

Jepang

USA 11,5%

11%

7,2%

3%

1,65%

▪ % dari populasi

Idealnya sebuah

negara maju mempunyai

Entrepreneur > 2%

44




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44

TRENDS OF THE NEXT DECADE

GreenClean

EnergyOrganic

OrientationEconomic Social Health Web

Trends of The Next Decade

45




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45

THE FUTURE:

• Robot and Automation

• Voice/Face Recognition System

• Environmental Production

• Healthcare

• Hydrology

• Nanotechnology

The Perfect for Innovative Product

• Water

• Wind

• Sun-shine

The Noble Quest: “Energy for All”

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46

Volume

• Data quantity

Velocity

• Data speed

Variety

• Data types

Veracity

• Data uncertainty

Four Vs of Big Data :

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Volume

A typical PC might have

had 10 gigabytes of

storage in 2000.

Today, Facebook

ingests 600 terabytes of

new data every day.

The smart phones, the data they create

and consume; sensors embedded into

everyday objects will soon result in billions

of new, constantly-updated data feeds

containing environmental,

location, and other information, including

video.

(Jumlah yang

besar)

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48

Velocity

high-frequency stock trading

algorithms reflect market

changes within microseconds.

machine to machine

processes exchange

data between billions of devices.

on-line gaming systems

support millions of concurrent users, each producing

multiple inputs per second.

(Kecepatan

yang tinggi)

49




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49

Variety

Big Data isn't just numbers, dates, and strings. Big Data is

also geospatial data, 3D data, audio

and video, and unstructured text, including log files and social media.

Traditional database systems were designed to address smaller

volumes of structured data, fewer updates or

a predictable, consistent data

structure.

Big Data analysis includes different types of

data.

(Bentuk yang

bermacam)

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Veracity

Managing the reliability and predictability of inherently

imprecise data types

(Kebenaran

yang tidak

terjamin)

51




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51

Storage

Searching

Sharing

Transfer

Analysis

Challenges

of Big Data

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52

GRAFIK SMALL DATA DAN BIG DATA TENTANG COST EFFECTIVENESS DAN VOLUME OF DATA TO ANALYZE

(dataflog.com, Martin Lindstrom dalam Small Data, 2017)

Volume of Data to Analyze

Co

st

Eff

ecti

ven

ess

“SMALL DATA” “BIG DATA”

Too close to call

53




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PERBEDAAN SMALL DATA DAN BIG DATA(dataflog.com, Martin Lindstrom dalam Small Data, 2017)

54




* Des’ 2018

Data Strategy

4

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Information System Are More Than Computers(Information Systems Today, Managing in the Digital World, 8TH, Global Edition, Pearson, 2018)

56




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56

Management

Technology

OrganizationInformation

System

Business Challenges

Business Solutions

• Develop big data strategy

• Develop privacy policies

• Develop big data predictive models

• Big data mining technology

• Big data analytics tools

• Opportunities from new technology

• Undeveloped legal environment

Predictive Modeling Systems

• Predictive policing• Predict insurance risk• Identify employment

risks• Target financially

vulnerable individuals

• Invade privacy?• Increase efficiency• Lower costs

57




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57

STRATEGIC BIG DATA(Sumber: Big Data: Using SMART Big Data, Analytics and Metrics To Make

Better Decisions and Improve Performance, Bernard Marr (2015))

58




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The ‘Datafication’ of our world:

• Activities

• Conservation

• Words

• Voice

• Social Media

• Browser Logs

• Photos

• Videos

• Sensors

• Etc.

Volume

Velocity

Variety

Veracity

AnalysingBig Data:

• Text Analytics

• Sentiment Analysis

• Face Recognition

• Voice Analytics

• Movement Analytics

• Etc.

VALUE

Strategic Big Data Menjadi Nilai (Value)

Sumber: Big Data: Using SMART Big Data, Analytics and

Metrics To Make Better Decisions and Improve

Performance, Bernard Marr (2015)

59




* Des’ 2018

Big Data dan Praktik-Praktikdi Perusahaan5

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60

STRATEGY BIG DATA

Data Strategy:

How Big Data Is At The

Heart Of Google’s

Business Model?• Membantu memecahkan masalah business.

• Big Data digunakan dalam Praktek Business (dari household name) di setiap penjuru dunia sampai dengan knowledge graph.

• 89% menyumbang pengguna pencarian internet (11%: yahoo, Bing, Baidu)

1.

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61

STRATEGY BIG DATA

Data Strategy:

How Twitter and IBM

Deliver Customer Insight

from Big Data?

• Sumber wawasan yang kaya akansentimen dan perilaku konsumen.

• Hampir 310 juta pengguna aktif bulanan, mereka mem-posting 500 juta pesantiap hari.

2.

62




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STRATEGY BIG DATA

➢ Tantangan pada era Big Data ini meliputi pemerolehan, kurasi,

penyimpanan, penelusuran (search), pembagian, pemindahan, analisis,

dan visualisasi data.

➢ Tren kian membesarnya himpunan data terjadi akibat bertambahnya

informasi dari himpunan-himpunan besar yang saling terkait,

dibandingkan dengan himpunan-himpunan kecil lain dengan jumlah total

data yang sama.

Data Strategy:

Memanfaatkan Big Data &

Transformasi Kebijakan Moneter BI.

3.

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(Bernard Marr, 2016)

NO. COMPANY NAME

BIG DATA IN PRACTICE

(How Companies Used Big Data Analytics to Deliver

Extraordinary Results)

1. Facebook How Facebook Use Big Data To Understand Customers

2.Royal Bank of Scotland

Using Big Data To Make Customer Service More Personal

3. GE (General Electric) How Big Data Is Fuelling The Industrial Internet

4. Shell How Big Oil Uses Big Data

5. Rolls-Royce How Big Data Is Used To Drive Success In Manufacturing

6. UberHow Big Data Is At The Centre Of Uber’s Transportation Business

7. AmazonHow Predictive Analytics Are Used To Get A 360-Degree View Of Consumers

8. Apple How Big Data Is At The Centre Of Their Business

Contoh Lainnya:5.

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BIG DATA IN PRACTICE#1


NO. COMPANY NAMEBIG DATA IN PRACTICE

(How 45 Companies Used Big Data Analytics to Deliver Extraordinary Results)

→ Data Strategy

1. Google How Big Data Is At The Heart Of Google’s Business Model

2. Electronic Arts Big Data In Video Gaming

3. Milton Keynes How Big Data Is Used To Create Smarter Cities

4. Palantir How Big Data Is Used To Help The CIA And To Detect Bombs In Afghanistan

5. Airbnb How Big Data Is Used To Disrupt The Hospitality Industry

6. Sprint Profiling Audiences Using Mobile Network Data

7. Dickey’s Barbecue PitHow Big Data Is Used To Gain Performance Insights Into One Of America’s Most Successful Restaurant Chains

8. Caesars Big Data At The Casino

9. Fitbit Big Data In The Personal Fitness Arena

10. Facebook How Facebook Use Big Data To Understand Customers

11. John Deere How Big Data Can Be Applied On Farms

12. Royal Bank of Scotland Using Big Data To Make Customer Service More Personal

13. LinkedIn How Big Data Is Used To Fuel Social Media Success

14. Microsoft Bringing Big Data To The Masses

15. Acxiom Fuelling Marketing With Big Data

16. US Immigration And Customs How Big Data Is Used To Keep Passengers Safe And Prevent Terrorism

17. Nest Bringing The Internet of Things Into The Home

18. Autodesk How Big Data Is Transforming The Software Industry

19. GE How Big Data Is Fuelling The Industrial Internet

20. Etsy How Big Data Is Used In A Crafty Way

21. Narrative Science How Big Data Is Used To Tell Stories

22. BBC How Big Data Is Used In The Media

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BIG DATA IN PRACTICE#2


NO. COMPANY NAME

BIG DATA IN PRACTICE

(How 45 Companies Used Big Data Analytics to Deliver Extraordinary Results)

→ Data Strategy

23. Walmart How Big Data Is Used To Drive Supermarket Performance

24. CERN Unravelling The Secrets Of The Universe With Big Data

25. Netflix How Netflix Used Big Data To Give Us The Programmes We Want

26. Ralph Lauren Big Data In The Fashion Industry

27. Zynga Big Data In The Gaming Industry

28. Shell How Big Oil Uses Big Data

29. Apixio How Big Data Is Transforming Healthcare

30. Lotus F1 Team How Big Data Is Essential To The Success Of Motorsport Teams

31. Pendleton & Son Butchers Big Data For Small Business

32.US Olympic Women’s CyclingTeam

How Big Data Analytics Is Used To Optimize Athletes’ Performance

33. ZSL Big Data In The Zoo And To Protect Animals

34. Rolls-Royce How Big Data Is Used To Drive Success In Manufacturing

35. Kaggle Crowdsourcing Your Data Scientist

36. Amazon How Predictive Analytics Are Used To Get A 360-Degree View Of Consumers

37. Uber How Big Data Is At The Centre Of Uber’s Transportation Business

38. Walt Disney Parks and Resorts How Big Data Is Transforming Our Family Holidays

39. Experian Using Big Data To Make Lending Decisions And To Crack Down On Identity Fraud

40. Transport for London How Big Data Is Used To Improve And Manage Public Transport In London

41. The US Government Using Big Data To Run A Country

42. IBM Watson Teaching Computers To Understand And Learn

43. Terra Seismic Using Big Data To Predict Earthquakes

44. Apple How Big Data Is At The Centre Of Their Business

45. Twitter How Twitter And IBM Deliver Customer Insights From Big Data

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Generasi Milenial

7

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• 1925-1946

• Usia 70-91

GENERASI TRADISIONALIS

T/SILENT

• 1947-1964

• Usia 52-69

GENERASI BABY

BOOMERS • 1965-1979

• Usia 37-51

GENERASI X

• 1980-1999

• Usia 17-36

GENERASI MILENIAL/Y

• 2000-....

• Usia < 16 Thn

GENERASI Z

Pembagian Generasi

68




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Mode Speed/mph Downsides When Generasi

Stagecoach 5 Dangerous and unreliable 1766 –

Pony Express 10 Expensive and dangerous 1860-1861 –

Telegraph 4.900.000 Unreliable, few locations First in 1861, last in 2006 –

Railroad 10-May Expensive, few locations 1869 first

transcontinental

–

Airplane 80-600 Expensive to operate First 1848, common by

1940s

Generasi

Tradisionalist/Silent

Jet 768 Expensive First in 1930s, common

by 1960

Generasi Baby

Boomers

Fax 4.900.000 Specialized equipment First 1924, popular

1970s

Generasi X

E-mail 669.000.000 Requires computer First 1965, popular by

1990

Generasi

Millenial/Generasi Y

Texting* 669.000.000 Limited information First in 1992, popular by

2000

Generasi Z

Tabel History of Speed vs Generation

Mph = miles per hour. Sumber: Bernard (2012), modified by Pasaribu (2017)

* Texting or text messaging, is the act of composing and sending electronic messages, typically consisting of alphabetic and

numeric characters, between two or more users of mobile phones, tablets, desktops/laptops, or other devices. Text

messages may be sent over a cellular network, or may also be sent via an Internet connection.

69




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Pedesaan

37,7Perkotaan

43,2

Gen Milenial/Y

Tradisionalis/Silent

Baby Boomers

Generasi

X

Generasi

Z

Keterangan data merupakan hasil sensus penduduk 2010.

Sumber: Kompas, diolah.

Generasi Milenial/Gen Y Indonesia Banyak Berada di Kota

(juta jiwa)

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Aspek Keterangan

Tujuan bekerja • Terkait dengan kinerja bisnis dan kepuasan karyawan

Kepemimpinan • Fokus kepada orang.

• Persepsi kaum milenial tentang kepemimpinan mencakup individu

dan organisasi. Menurut milenial pemimpin memiliki ciri-ciri:

- berpikir strategis

- bisa menjadi inspirasi

- memiliki ketrampilan interpersonal yang kuat

- memiliki visi, semangat, anstusiasme, dan ketegasan.

• Pemimpin bergaya arsitokrat cenderung tidak disukai.

Perekrutan karyawan • Milenial merekrut lebih banyak pekerja berdasarkan atribut

kecakapan pribadi mereka.

Kewirausahaan • Kaum milenial sangat menaruh orientasi pada bisnis mandiri.

Kalaupun belum memiliki bisnis sendiri, mereka memiliki niat besar

untuk menjalankan bisnis mereka sendiri di masa depan.

Sejumlah Perilaku Generasi Milenial dalam Menjalankan Bisnis

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Karakteristik Gen Milenial/Y

(Acar, 2014)

Fokus kepada karir pribadi

Optimistik

Berpihak pada keragaman

Team Player

Cerdas teknologi

Menyukai busana casual (non formal)

Menyenangkan

Work life balance

Pengakuanterhadap nilaipekerjaan

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CONCLUTION - QA

8

• Big Data is the foundation of megatrend and knowledge, "Megatrends that will occur today, tomorrow, the day after, and the future"

• 4 V data (variation, volume, speed, truth) play a very important role in the capital market specifically for decision making.

• Machinery industry companies such as jet engines (airplanes), power plants that have been transformed based on Big Data in operational maintenance and others.

• Companies that are still thinking about investing in Big Data technology must get ready before it's too late to stay competitive.

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* Des’ 2018

TERIMA KASIH

73

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