4. By. Geoffrey Moore
Author and consultant
“ Without BIG DATA, You are
Blind and Deaf and in The
Middle of a Freeway ”
5. Big Data Success Story
Netflix started to implement Big Data in 2009.
Offer movie recommendation using personalization algorithm
soon after credits start.
Invest 100 Mio to buy TV Series and gain 86% new user subs
Amazon had built a recommender system from 2003.
The recommendation system has generated 29% of sales growth.
Now, they filed a new patent called ‘anticipatory shipping’
Source : KISS metric, Datafloq
6. Source : sprint US
1. Develop capability 2. Urgency 3. Internally proven
Data for
Improving
Business & Image
Improve services &
Corporate Image
Data for a better
Advertising
Targeted customer &
higher CTR (Click
Through Rate)
Data for
Business
Data Insight to
create new
business
Data for Risk
Business
Gatekeeper of
Personal data
Big Data Business Model that Telco’s can adopt:
7. Do I have enough information to darft
an analytical plan, business
requirement
DISCOVERY
Do I have enough good quality data to
start building models
DATA PREP
Do I have a good idea about the type of
model to try ? Can i refine the analytic
plan
Is the model robust enough ?
Heve we failed for sure
MODEL BUILDING
Is it the solve customer objective ?
COMMUNICATE
RESULTS
Implement
OPERATIONALIZE
Big Data
Analytics
Lifecycle
1
2
34
5
6
MODEL
PLANNING
7
HOW BIG DATA OPERATES
Source : EMC2
9. Unsructured & Semi Structured DataStructured Data
Text Analytics
Predictive
Analytics
Data Quality
Data Integration, Storage & Computing
RTD
CEP/PME
Descriptive Analytics
Behavior
Analytics
Location
Analytics
Real Time
Operations
Data
Governance
TextNetwork Data CRMBilling WebVideo
Data Security
User Mgmt
Deploy
Operations
Management
Configure
Maintenance
Fault
Performance
A&A
EDW
AcquiredAccessedAnalyticApplication
Audio
4A
Analytics
Data Sources
To handle VOLUME of Data To handle VARIETY of Data To handle VELOCITY of
Data
Product Analysis
Paralelize Data
Mart Query
Big Data Storage Streams
Sqoop Flume Hive
Original Data Transformed Data
TECHNOLOGY
Telkom Group Capabilities In Big Data
11. Internal
PO : Product Owner BPO : Business Process Owner
AO: Application Owner UPTI : Unit Pengelola Teknologi Informasi
Business Process
Use
Case
Development
MCM TBD DSC BDB
External
Commercial
PO BPO AO UPTI
UAT, D2P, BAST/BASTO8IC/QC
12. Workflow development-phase in Big Data
1
Big Data Business Process (1/2)
DISCOVERY DATA PREP MODEL PLANNING
MODEL BUILDINGCOMMUNICATE
RESULTS
OPERATIONALIZE
Internal MCM TBD DSC
DSC
MCM, DSC
MCM, DSC, TBD
Eksernal BDB,MCM TDB / Ext Partner DSC / Ext Partner
BDB
BDB, MCM, DSC,
TBD, Ext Partner
DSC / Ext Partner
13. PO
PO : Product Owner BPO : Business Process Owner
AO: Application Owner UPTI : Unit Pengelola Teknologi Informasi Anpers : Anak Perusahaan
Big Data Business Process (2/2)
BPO
AO
UPTI
Fault Handling
Infrastructure
Fault Handling
Application
Cust
Care/Helpdesk
Sales/
Marketing
Product
Portfolio
Role
Maintenance
Application
Maintenance
Infrastructure
Product
Enhancement
Big Data
CFU
Big Data
ISC
Big Data
Anpers
Big Data
ISC
Workflow commercial-phase in Big Data
Internal Eksternal
17. Why We Need Social Media Analytics ?
Customer voice Listening
Structuring
Data
Analysis
Insight for
strategy
F r o m s o c i a l n o i s e t o s t r a t e g y
18. Udata Channel
Local Twitter
Timeline
Global Twitter
Timeline
Specific Account
Keyword
Twitter
Public Page
Public
Statuses
Facebook
Blogspot
Tumblr
Wordpress
Blog
More 1000 online
media
Kompas.com
Detik.com
Tempo.co
Tribunnews.com
Theurbanmama.com
Viva.co.id
Antaranews.com
Fimela.com
Gatra.com
Femina.com
dll
Media DiGitaLForum
Kaskus
Indowebster
Kompasiana
Viva news forum
Chip forum
dll
Coming soon Coming soon
19. Key Features
Sentiment
analysis with
local language
NLP
Word Cloud
Analysis
Engagement &
Interaction
rate
Tracking Keyword
comparation
Geo Distribution
Influence Analysis
Measure
conversation
Gender
20. Always on, All the time
track and analyze post about your keyword in real-time
21. All You Need, In Your Inbox
Get up date on special issue “spike”
23. Data Driven Advertising, How it works ?
CONSUMER PERSPECTIVE
Why am i seeing this ads? How do
advertisers know what i like? Whats
really going on behind the reason?
AGENCY / ADVERTISER
Right time, Right place, right
consumer. How does an online ad hit
all those marks ?
ADCHOICES
We all wonder, what else do
advertisers know about me and how
can stop them from storing and using
that data
24. CMC - Campaign Management Centre
“Integrated Targeted Campaign Management with multi channel offering SMS
(PCM), web ads (Push Ads), Social Media dan Email”
Targeted Customer,
dibangun dari hasil analisis terhadap profil dan
behavior pelanggan
Real Time Report Dashboard
Integrated Multi Channel Campaign
memiliki banyak pilihan campaign channel yang
dapat dimanfaatkan untuk meningkatkan revenue
CMC for Better Advertising
26. Profiling Targeted Campaign
Geo Targeting
Behavioral Targeting
Demographic Targeting
Mobile Device Targeting
Keyword Targeting
(Contextual Ads)• Area
• City
• Apartment
• Perumahan SES
A,B, C
• Hobby: Business, Traveling,
Sport, Lifestyle, etc
• Payment Model: CC, Auto
Debit, Cash, etc
• Gadget yang sering digunakan
: HP, Laptop
• Data ARPU Indihome
• SES ABC
• Occupation Selected
Owner Company
(Limited Data)
• Orang yang sedang
browsing dengan
keyword tertentu
misal HP
• Orang yang sedang
akses menu tertentu
suatu website. Misal
orang yang sedang
mengakses menu
elektronik di web
ecommerce
• OS Laptop (Windows,
Apple),
• Mobile (Android &
IOS)
29. PCM – Definition & Type
“Layanan berbasis event (event based service) dalam penyediaan campaign platform yang
memungkinkan enterprise client untuk melakukan marketing campaign dan survey kepada
pelanggannya, yang telah melakukan panggilan ke call center yang dimilikinya..”
30. PCM – Post Call Segmented Adv.
Layanan semi realtime SMS yang lebih presisi dengan parameter kota, hari, dan jam tertentu
untuk melakukan SMS Campaign
Berfungsi untuk program akuisisi pelanggan, mencakup: awareness produk, penawaran
promosi, info discount, dll.
32. Push Ads – Definition & Flow
“Advertising yang menggunakan media channel IndiHome yang TARGETED
berdasarkan hasil analisis profil dan behavior pelanggan..”
42. USECASE & INKUBASIOTHERS UNIT
BIG DATA TECH
DATA SCIENTIST
ISP (IS, CSP, Sinergi)
EXTERNAL / CFU
Mobile Digital Services Consumer Enterprise Wholesale/
International
HCMNetwork/ITStrategy /
Corporate M&A FinanceDBS, DESTR, DITKONS
OTHERS UNIT
BUSINESS RESEARCH
CEA
GENERAL AFFAIRS
Dok Usecase/Product SLA Business Owner & Platform Owner Package & Tarif
SERVICE
EXCELLENCE
COREACTIVITYSUPPORTING
CDC
FRAMEWORKBUSDEVBIGDATA GTM & ECO EX (1-5)
INT SERVICE STRAT & DESIGN EXT SERVICE STRAT & DESIGN
DESIGN STRAT & BUSINESSDESIGN STRAT & BUSINESS
PERFORMANCE MGMT
PROJECT MANAGEMENTPROJECT MANAGEMENT
MARKET PRACTICERS
PERFORMANCE MONITORING
ISC
MA
DSS
(MULTI CHANNEL MARKETING BIG DATA)
PRODUCT
Engagement Brand SentimentTrack Issue Data Monetizing Post Call Marketing AAAS. Solution IAAS PAAS
PROGRAM
CUSTOMER
Internal
Usecase
Finance &
Banking
Education Hospitality
Logistic Media
Gov & Military
Police
Property Agriculture Consumer …etc
INTERNAL FU
Hinweis der Redaktion
Big Data membutuhkan dukungan teknologi yang handal, terutama dalam penanganan volume data yang besar, variety yang kompleks dan velocity yang cepat.
Acquired berhubungan dengan mendapatkan data, baik data terstruktur maupun tidak terstruktur.
Accessed berhubungan dengan daya akses data, data yang sudah dikumpulkan diperlukan tata kelola, integrasi, storage dan computing agar dapat dikelola untuk tahap berikutnya.
Analytic behubungan dengan insight yang akan didapatkan, hasil pengelolaan data yang telah diproses.
Application merupakan tahapan akhir, dimana hasil dari analytic dilakukan visualisasi dan reporting guna mendukung pengambilan keputusan..
Push ads for
Objective
Menurunkan churn dari rata-rata 5% menjadi 2%
Membangun model prediksi untuk mengantisipasi customer yang akan churn
Melakukan pro-active retention dengan melakukan caring sebelum pelanggan menjadi churn.
Menjaga pertumbuhan revenue eksisting, memanage program retensi & loyalty
Sumbe data
Demografi : Nama, Alamat, Kota, Witel, dan Regional
Portfolio Produk : paket, pricing, promo, bundling, add-on, dll
Pricing : over pricing / under value
Data Network : Data Akses (Copper, FTTH, MSAN), Kualitas network SNR, R2BB Capability, R2BB measurement result, etc
Billing & Payment : Billing, Payment, Outstanding
Transaksi POTS & Internet : Usage POTS and Internet, Durasi, Number of called, frequency called, etc
Komplain Customer : Jumlah komplain, produk komplain, tipe komplain, MTTR, dll
Customer flags : product ownership (2P, 3P), jumlah penurunan usage, jumlah kenaikan komplain, dll
Browsing pattern penggunaan Internet untuk mengetahui concern pelanggan
Tracker: topik yang terkait kota/tokoh tertentu.
Keywords: seleksi data yang terkait kata-kata tertentu saja.
Gender: komposisi jumlah responden laki-laki dan perempuan.
Sentimen: positif, netral atau negatif per tweet/posting/artikel.
Category: klasifikasi tweet/posting/artikel per kategori (didefinisikan sebelumnya).
Popular topic: tren topik saat ini (realtime).
Hourly & daily buzz: statistik tweet/posting per jam dan hari.
Audiences: responden yang sering aktif dan dibicarakan (mentioned).
Popular urls: obyek yang terbanyak di-share.
Social graphs: menampilkan kata-kata yang menonjol dan relasinya.
Alert: mengirimkan peringatan terkait dengan kata dan jumlah tweet/posting tertentu (via sms dan email).
Compare: menyajikan perbandingan dengan tracker lain (versi multi tracker).
