3. FUNCTIONING
A top down
birds eye view
of an area
identified by a
client –
visualized using
Smart Steps
Telefonica Smart Steps: Using Customer Insight to Drive Footfall
4. IBM and Orange Mobile Data: Urban Transportation
Mapping Orange mobile subscriber movements
against established public transport routes
USER CONGESTION / MOVEMENT BASED
ON CELL TOWER THIS IS MAPPED AGAINST BUS ROUTES –
TRAFFIC TO CREATE A MORE EFFICIENT
SYSTEM OF DELIVERY
5. FU NCTIONING:
• SingTel uses Amobee’s technology
combined with its own internal data
to create targeted ad campaigns to
for its advertisers.
• Location is the single largest data
point used to create these targeted
offers – for its external clients.
q Internally however the
organization combines Amobee
with its customer information
to create a 360 degree view of
its user in order to create even
greater personalization
SingTel Using Amobee To Create Location Based Advertising
6. Big Data Telkom Group Implementation
For Industry 4.0 For BUMN For Government For Internal
Sierad Produce: Smart Poultry Environment, Fan
Speed Automation, Weight Scale, Monitoring
Kemendagri:
IoT for Power Monitoring System (Disdukcapil)
IndiHome - Churn
Prevention
Paragon:
IoT for Overall Equipment Effectiveness System Angkasa Pura II:
IoT for Aviobridge Usage Counting System
IoT for Aircraft Block On/Off System
IoT for Passengers Arrival Counting System
IoT for Taxi Queue Management
BPBD Provinsi Bali:
IoT for Disaster Early Warning System
IoT for Volcano Eruption Monitoring System
IoT for Ambulance Tracking System
IndiHome - RPA for 147
Mr. Montir:
IoT for Monitoring Behavior of Motorcycles
IndiHome - Smart
Profiling
Lippo Karawaci: Monitoring Water Level, PJU,
Metering Water Residential & Distribution, FMS
Pegadaian:
Big Data
BPJS Kesehatan:
Big Data Full Stack
IndiHome - Smart
Collection
KAI:
Big Data
Kemen PANRB:
Big Data Solution for Aparatur Sipil Negara
IndiHome - SIIS
Pemerintah Kota Tangerang Selatan:
Digitalization
IndiHome - Smart CAPEX
Pemerintah Provinsi DKI Jakarta:
Water Ground Monitoring System
IndiHome - Growth
Hacking
Big data is a pretty popular term. And even though its definition is simple enough, it hides numerous potential
advantages for our company.
Enterprise - BIMA
Enterprise Digitization
PINS - Plate Number
Recognition
TLT - Visitor Face
Recognition System
MelOn - Growth Hacking
MelOn - Social Media
Analytics
LinkAja Digitization
HCM Digitization
TIOC DSO Assurance
Analytics
Enterprise A2P Analytics
Blanja Engine
Recommendation
Agro Digitization
Smart City DIgitization
Tourism Digitization
Pertamina:
SPBU Digitalization
Subscriber Profiling via API:
Kimia Farma:
Big Data Kimia Farma
IoT for Power Consumption Monitoring System
IoT for Purified Water & Total Organic Monitoring
IoT for Gas Detection Monitoring System
IoT for Environmental Monitoring System
Jasa Marga:
Video Analytics Rest Area
UGM:
Big Data Full Stack, Social Media Analytics
Bank Panin:
Data Audit
Mitsubishi Group:
Big Data Full Stack
Credit Scoring for FI’s Customer Acquisition:
Audience Profiling for Marketing Activation:
Event Analysis
Footfall & Movement Analysis
Digital Marketing Intelligence:
11. Analisa Indikator Kemiskinan
Data Ketahanan Pangan Nasional
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 *2019
14.15 13.33 12.49 11.66 11.47 10.96 11.13 10.70 10.12 9.66 *9.22
Year
Indonesia
Tren Persentase Penduduk Miskin – Indonesia VS Jawa Barat
Persentase Penduduk Miskin Menurut Provinsi (Persen)
26,6
21,5
20,6
17,7
15,3 15,0 14,9
13,9
13,2 12,6 12,3
11,4 11,0 11,0 10,6 10,2
9,2 8,6 8,6
7,5 7,5 7,3 6,9 6,9 6,8 6,5 6,3 5,9 5,8
4,9 4,8 4,5 4,5
3,6 3,4
P
A
P
U
A
P
A
P
U
A
B
A
R
A
T
N
U
S
A
T
E
N
G
G
A
R
A
T
I
M
U
R
M
A
L
U
K
U
G
O
R
O
N
T
A
L
O
A
C
E
H
B
E
N
G
K
U
L
U
N
U
S
A
T
E
N
G
G
A
R
A
B
A
R
A
T
S
U
L
A
W
E
S
I
T
E
N
G
A
H
S
U
M
A
T
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R
A
S
E
L
A
T
A
N
L
A
M
P
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N
G
D
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G
Y
A
K
A
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T
A
S
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L
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W
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S
I
T
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G
G
A
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A
S
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L
A
W
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S
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B
A
R
A
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J
A
W
A
T
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N
G
A
H
J
A
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M
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D
O
N
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S
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A
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T
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L
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A
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J
A
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B
I
S
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L
A
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S
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R
A
K
A
L
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A
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T
A
N
B
A
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A
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A
L
U
K
U
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T
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R
A
R
I
A
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J
A
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A
B
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A
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K
A
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A
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T
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A
R
A
S
U
M
A
T
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B
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K
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M
A
N
T
A
N
T
I
M
U
R
K
E
P
.
R
I
A
U
B
A
N
T
E
N
K
A
L
I
M
A
N
T
A
N
T
E
N
G
A
H
K
E
P
.
B
A
N
G
K
A
B
E
L
I
T
U
N
G
K
A
L
I
M
A
N
T
A
N
S
E
L
A
T
A
N
B
A
L
I
D
K
I
J
A
K
A
R
T
A
Sejak tahun 2015, presentase
penduduk miskin di Indonesia dan
Jawa Barat selalu menurun, hingga
di tahun 2019 ditutup dengan angka
persentase 9,22% dan 6,82%
penduduk miskin
Lima provinsi dengan jumlah penduduk miskin
paling banyak didominasi oleh provinsi di wilayah
Indonesia Timur. Sedangkan jumlah persentase
penduduk miskin paling sedikit adalah provinsi
Kalimantan Selatan, Bali dan DKI Jakarta
11.96 11.27 10.65 9.89 9.61 9.18 9.57 8.77 7.83 7.25 *6.82
Jawa Barat
Source Data : BPS, Dukcapil, Telkom Analysis. 2018.
12. Analisa Indikator Proporsi Pengeluaran untuk Pangan
Data Ketahanan Pangan Nasional
473.382
512.796
421.216
420.732
330.890
326.512
329.208
359.187
383.546
426.278
511.272
549.351
470.450
494.858
426.381
298.180
355.034
421.577
483.956
380.993
365.012
330.646
425.883
615.486
602.071
578.812
382.368
413.263
379.945
428.457
495.322
472.428
414.566
415.354
PAPUA
PAPUA BARAT
MALUKU UTARA
MALUKU
SULAWESI BARAT
GORONTALO
SULAWESI TENGGARA
SULAWESI SELATAN
SULAWESI TENGAH
SULAWESI UTARA
KALIMANTAN UTARA
KALIMANTAN TIMUR
KALIMANTAN SELATAN
KALIMANTAN TENGAH
KALIMANTAN BARAT
NUSA TENGGARA TIMUR
NUSA TENGGARA BARAT
BALI
BANTEN
JAWA TIMUR
DI YOGYAKARTA
JAWA TENGAH
JAWA BARAT
DKI JAKARTA
KEP. RIAU
KEP. BANGKA BELITUNG
LAMPUNG
BENGKULU
SUMATERA SELATAN
JAMBI
RIAU
SUMATERA BARAT
SUMATERA UTARA
ACEH
473.382
512.796
421.216
420.732
330.890
326.512
329.208
359.187
383.546
426.278
511.272
549.351
470.450
494.858
426.381
298.180
355.034
421.577
483.956
380.993
365.012
330.646
425.883
615.486
602.071
578.812
382.368
413.263
379.945
428.457
495.322
472.428
414.566
415.354
473.382
512.796
421.216
420.732
330.890
326.512
329.208
359.187
383.546
426.278
511.272
549.351
470.450
494.858
426.381
298.180
355.034
421.577
483.956
380.993
365.012
330.646
425.883
615.486
602.071
578.812
382.368
413.263
379.945
428.457
495.322
472.428
414.566
415.354
473.382
512.796
421.216
420.732
330.890
326.512
329.208
359.187
383.546
426.278
511.272
549.351
470.450
494.858
426.381
298.180
355.034
421.577
483.956
380.993
365.012
330.646
425.883
615.486
602.071
578.812
382.368
413.263
379.945
428.457
495.322
472.428
414.566
415.354
2015 2016 2017 2018
*Rata-Rata Pengeluaran per
Kapita untuk Makanan
Source Data : BPS, Dukcapil, Telkom Analysis. 2018.
13. Analisa Indikator Akses Listrik
Data Ketahanan Energi Nasional
Persentase Rumah Tangga Yang Tidak Menggunakan Penerangan
Dengan Sumber Listrik (40% Ke Bawah),
Menurut Provinsi (Persen)
0
1
2
3
4
5
6
7
8
9
10
2015 2016 2017 2018
ACEH
SUMATERA UTARA
SUMATERA BARAT
RIAU
JAMBI
SUMATERA SELATAN
BENGKULU
LAMPUNG
KEP. BANGKA BELITUNG
KEP. RIAU
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
2015 2016 2017 2018
DKI JAKARTA
JAWA BARAT
JAWA TENGAH
DI YOGYAKARTA
JAWA TIMUR
BANTEN
0
5
10
15
20
25
30
35
40
45
50
2015 2016 2017 2018
BALI
NUSA TENGGARA BARAT
NUSA TENGGARA TIMUR
KALIMANTAN BARAT
KALIMANTAN TENGAH
KALIMANTAN SELATAN
KALIMANTAN TIMUR
KALIMANTAN UTARA
0
10
20
30
40
50
60
70
80
2015 2016 2017 2018
SULAWESI UTARA
SULAWESI TENGAH
SULAWESI SELATAN
SULAWESI TENGGARA
GORONTALO
SULAWESI BARAT
MALUKU
MALUKU UTARA
PAPUA BARAT
PAPUA
Sedangkan jika menurut daerah tempat
tinggal, berikut persentasenya:
2016 2017 2018
0,31 3,44 0,24
7,09 2,08 4,62
Year
Perkotaan
Pedesaan
2016 2017 2018
4,03 3,12 2,55
Year
Indonesia
Source Data : BPS, Dukcapil, Telkom Analysis. 2018.
14. Analisa Indikator Lama Sekolah Perempuan
Data Ketahanan Pendidikan Nasional
8,09
8,1
8,3
8,41
8,76
8,97
9,14
9,22
9,45
9,88
LAMPUNG
KEP. BANGKA BELITUNG
SUMATERA SELATAN
JAMBI
BENGKULU
RIAU
SUMATERA BARAT
ACEH
SUMATERA UTARA
KEP. RIAU
7,45
7,49
8,29
8,53
9,36
10,75
JAWA TENGAH
JAWA TIMUR
JAWA BARAT
BANTEN
DI YOGYAKARTA
DKI JAKARTA
7,17
7,31
7,52
8,11
8,33
8,37
8,9
9,32
NUSA TENGGARA BARAT
KALIMANTAN BARAT
NUSA TENGGARA TIMUR
KALIMANTAN SELATAN
BALI
KALIMANTAN TENGAH
KALIMANTAN UTARA
KALIMANTAN TIMUR
5,97
7,83
8,17
8,26
8,27
8,6
8,74
8,82
9,37
9,58
9,71
PAPUA
SULAWESI BARAT
GORONTALO
INDONESIA
SULAWESI SELATAN
SULAWESI TENGAH
SULAWESI TENGGARA
MALUKU UTARA
PAPUA BARAT
SULAWESI UTARA
MALUKU
2018
8,26
Year
Indonesia
Source Data : BPS, Dukcapil, Telkom Analysis. 2018.
Rata-rata Lama Sekolah Penduduk Perempuan
Berumur 15 Tahun ke Atas
Pulau Sumatera dan
Pulau Jawa
Kalimantan, Bali, Nusra,
Sulawesi, Maluku
dan Papua
Top 3 Province
Sumatera : Kep.Riau, Sumut, Aceh
Jawa : DKI, DIY, Banten
Kalbanusra : Kaltim, Kaltara, Kalteng
Sulmapua : Maluku, Sulut, Papua Barat
15. Analisa Indikator Akses Air Bersih
Data Ketahanan Kesehatan Nasional
Proporsi Populasi Penduduk Yang Memiliki Akses Terhadap
Layanan Sanitasi Layak Dan Berkelanjutan (Persen)
Proporsi Populasi Yang Memiliki Akses Terhadap Layanan Sumber
Air Minum Layak Dan Berkelanjutan Menurut Provinsi (Persen)
Bali, DKI Jakarta dan DIY
adalah provinsi dengan
jumlah penduduk yang
paling banyak dalam
memiliki layanan sanitasi
layak
49
57
58
63
63
65
65
66
67
67
69
70
71
71
72
72
73
73
74
74
75
76
76
77
78
78
79
80
81
81
81
84
88
90
91
BENGKULU
LAMPUNG
PAPUA
KALIMANTAN SELATAN
SULAWESI BARAT
SUMATERA SELATAN
KALIMANTAN TENGAH
ACEH
JAMBI
KEP. BANGKA BELITUNG
MALUKU UTARA
SUMATERA BARAT
JAWABARAT
SULAWESI TENGAH
SUMATERA UTARA
NUSA TENGGARA TIMUR
BANTEN
KALIMANTAN BARAT
NUSA TENGGARA BARAT
INDONESIA
JAWATIMUR
SULAWESI UTARA
MALUKU
PAPUABARAT
SULAWESI SELATAN
JAWATENGAH
GORONTALO
RIAU
DI YOGYAKARTA
SULAWESI TENGGARA
KALIMANTAN TIMUR
KEP. RIAU
KALIMANTAN UTARA
DKI JAKARTA
BALI
34
44
51
52
53
54
57
63
63
64
64
64
65
67
67
69
69
69
69
70
71
71
72
74
74
74
75
75
79
80
85
86
89
91
91
PAPUA
BENGKULU
NUSA TENGGARA TIMUR
LAMPUNG
KALIMANTAN TENGAH
KALIMANTAN BARAT
SUMATERA BARAT
KALIMANTAN SELATAN
SULAWESI BARAT
JAMBI
SULAWESI TENGAH
GORONTALO
JAWABARAT
MALUKU UTARA
ACEH
SUMATERA SELATAN
JAWATIMUR
MALUKU
INDONESIA
SULAWESI TENGGARA
BANTEN
RIAU
KALIMANTAN UTARA
NUSA TENGGARA BARAT
PAPUABARAT
JAWATENGAH
SUMATERA UTARA
SULAWESI UTARA
KALIMANTAN TIMUR
SULAWESI SELATAN
KEP. RIAU
KEP. BANGKA BELITUNG
DI YOGYAKARTA
DKI JAKARTA
BALI
Bali, DKI Jakarta dan
Kaltara adalah provinsi
dengan jumlah penduduk
yang paling banyak dalam
memiliki akses layanan
sumber air minum layak
Akses sanitasi layak masih
dirasa susah oleh pendu-
duk di Papua, Lampung
dan Bengkulu. Persepsi
masyarakat untuk menja-
ga kesehatan lingkungan
masih belum menjadi ke-
butuhan.
Source Data : BPS, Dukcapil, Telkom Analysis. 2018.
16. Analisa Indikator Tenaga Kesehatan
Data Ketahanan Kesehatan Nasional
Rasio Dokter terhadap 100.000 Penduduk
di Indonesia 2016
Rasio Perawat terhadap 100.000 Penduduk
di Indonesia 2016
Rasio Bidan terhadap 100.000 Penduduk
di Indonesia 2016
Rasio dokter terhadap 100.000 penduduk baik secara nasional
maupun provinsi masih jauh dari target rasio dokter pada tahun
2019 yaitu 45 per 100.000 penduduk. Secara nasional, rasio
dokter di Indonesia sebesar 16,02 per 100.000 penduduk.
Rasio bidan di Indonesia adalah sebesar 63,22 per 100.000
penduduk. Angka ini masih jauh dari target 2019 sebesar 120
per 100.000 penduduk. Ada empat provinsi yang telah
memenuhi target tahun 2019 yaitu Aceh, Bengkulu, Maluku
Utara, dan Jambi.
Secara nasional, rasio perawat adalah 114,75 per 100.000
penduduk. Hal ini masih jauh dari target tahun 2019 sebesar
180 per 100.000 penduduk. Namun ada delapan provinsi
dengan rasio perawat yang sudah memenuhi target tahun
2019.
10,4
10,9
11,6
12,0
12,2
12,4
12,6
13,1
13,7
13,9
14,8
15,5
16,2
16,3
17,6
17,7
19,1
19,7
19,7
20,1
20,4
20,6
20,8
21,8
22,2
22,8
23,0
23,1
24,8
27,2
28,1
28,8
31,4
37,6
38,3
LAMPUNG
JAWA BARAT
JAWA TIMUR
MALUKU
BANTEN
NUSA TENGGARA TIMUR
SULAWESI BARAT
JAWA TENGAH
KALIMANTAN BARAT
NUSA TENGGARA BARAT
SUMATERA SELATAN
SULAWESI TENGGARA
INDONESIA
SULAWESI TENGAH
SULAWESI SELATAN
KALIMANTAN SELATAN
JAMBI
SUMATERA BARAT
KALIMANTAN TENGAH
KEP. RIAU
SUMATERA UTARA
RIAU
PAPUA BARAT
MALUKU UTARA
BENGKULU
DI YOGYAKARTA
PAPUA
GORONTALO
KALIMANTAN UTARA
BALI
KALIMANTAN TIMUR
KEP. BANGKA BELITUNG
ACEH
SULAWESI UTARA
DKI JAKARTA
49,4
70,8
73,5
85,4
105,1
105,2
110,1
111,8
112,3
114,7
114,8
126,5
137,4
140,1
144,6
144,8
144,8
147,1
155,1
155,1
160,0
165,4
167,8
170,7
172,8
177,1
178,7
180,7
189,0
197,1
200,6
202,6
205,4
207,2
223,6
LAMPUNG
JAWA BARAT
BANTEN
JAWA TIMUR
SUMATERA UTARA
JAWA TENGAH
NUSA TENGGARA BARAT
KALIMANTAN BARAT
KALIMANTAN SELATAN
RIAU
INDONESIA
NUSA TENGGARA TIMUR
KEP. RIAU
SUMATERA SELATAN
SULAWESI SELATAN
SUMATERA BARAT
SULAWESI BARAT
GORONTALO
SULAWESI TENGAH
SULAWESI TENGGARA
BALI
PAPUA BARAT
KALIMANTAN TENGAH
DI YOGYAKARTA
MALUKU UTARA
KALIMANTAN UTARA
PAPUA
JAMBI
BENGKULU
SULAWESI UTARA
KEP. BANGKA BELITUNG
MALUKU
ACEH
KALIMANTAN TIMUR
DKI JAKARTA
37,2
40,5
42,0
43,4
44,3
46,4
48,6
51,4
51,9
54,0
55,6
55,9
57,6
60,6
61,8
63,2
67,1
68,0
73,1
76,7
81,1
82,2
85,5
90,0
90,2
96,1
102,2
103,8
107,0
107,3
108,8
117,1
144,0
162,3
172,4
JAWA BARAT
DI YOGYAKARTA
LAMPUNG
DKI JAKARTA
BANTEN
JAWA TIMUR
SULAWESI UTARA
KALIMANTAN SELATAN
JAWA TENGAH
KALIMANTAN UTARA
KALIMANTAN BARAT
PAPUA
KEP. RIAU
NUSA TENGGARA BARAT
PAPUA BARAT
INDONESIA
SULAWESI SELATAN
MALUKU
KALIMANTAN TENGAH
NUSA TENGGARA TIMUR
KEP. BANGKA BELITUNG
KALIMANTAN TIMUR
BALI
SULAWESI TENGAH
GORONTALO
RIAU
SULAWESI BARAT
JAMBI
SUMATERA SELATAN
SUMATERA BARAT
SUMATERA UTARA
SULAWESI TENGGARA
MALUKU UTARA
BENGKULU
ACEH
Source : BPS, 2016 Source : BPS, 2016 Source : BPS, 2016
Source Data : BPS, Dukcapil, Telkom Analysis. 2018.
17. Analisa Indikator Tinggi Badan Balita
Data Ketahanan Kesehatan Nasional
Stunting merupakan masalah kurang gizi kronis akibat asupan gizi yang kurang sehingga tinggi badan bayi di bawah standar menurut
usianya/pendek. Menurut World Health Organization/WHO batas maksimal stunting bayi adalah 20%. Artinya stunting Balita di Indonesia saat
ini masih di atas batas toleransi yang ditetapkan oleh Badan Kesehatan Dunia.
Berdasarkan hasil Pantauan Status Gizi (PSG) 2017 prevalensi stunting bayi berusia di bawah lima tahun (Balita) Nusa Tenggara Timur (NTT)
mencapai 40,3%. Angka tersebut merupakan yang tertinggi dibanding provinsi lainnya dan juga di atas prevalensi stunting nasional sebesar
29,6%. Prevalensi stunting di NTT tersebut terdiri dari bayi dengan kategori sangat pendek 18% dan pendek 22,3%. Sementara provinsi
dengan prevalensi Balita stunting terendah adalah Bali, yakni hanya mencapai 19,1%. Angka tersebut terdiri dari Balita dengan kategori sangat
pendek 4,9% dan pendek 14,2%.
36
29
31 30
25
23
29
32
23
29 29
20
27
30
19
37
40
36
39
34
31
33
36
31
35 36
32
40
25 25
33 33
31
A
C
E
H
S
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A
B
A
R
A
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R
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U
J
A
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B
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S
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M
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R
A
S
E
L
A
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A
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B
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N
G
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P
U
N
G
D
K
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A
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G
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A
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O
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Source Data : BPS, 2017
18. Analisa Indikator Angka Harapan Hidup
Data Ketahanan Kesehatan Nasional
Angka Harapan Hidup (AHH)
adalah perkiraan rata-rata
tambahan umur seseorang
yang diharapkan dapat terus
hidup
Angka Harapan Hidup (AHH) merupakan alat untuk mengevaluasi kinerja pemerintah dalam meningkatkan kesejahteraan penduduk pada
umumnya, dan meningkatkan derajat kesehatan pada khususnya.
Angka Harapan Hidup (AHH) yang rendah di suatu daerah harus diikuti dengan program pembangunan kesehatan, dan program sosial
lainnya termasuk kesehatan lingkungan, kecukupan gizi dan kalori termasuk program pemberantasan kemiskinan.
AHH Perempuan 73,19
tahun
Setiap Penduduk
perempuan yang lahir
tahun 2018 diharapkan
dapat hidup selama 73
hingga 74 tahun
AHH Laki-laki
69,3 tahun
Setiap Penduduk
perempuan yang lahir
tahun 2018 diharapkan
dapat hidup selama 69
hingga 70 tahun
2010 2011 2012 2013 2014 2015 2016 2017 2018
69,8 70,0 70,2 70,4 70,6 70,8 70,9 71,1 71,2
Year
Indonesia
Tren Angka Harapan Hidup Indonesia VS Jawa Barat
71,3 71,6 71,8 72,1 72,2 72,4 72,4 72,5 72,7
Jawa Barat
Source Data : BPS, Dukcapil, Telkom Analysis. 2018.
19. POTENTIAL USERS
Kemenpar, Kemenhan.
LIST OF CLIENS
Kementrian Pariwisata ( 2017, 2018,
2019 , 2020 )
POTENTIAL REVENUE
Rp. 7 Milyar / Year and Yearly
recurring.
SUPPORTING PARTIES
ü Wisatawan
mancanegara
ü Menggunakan No
ponsel asal
negaranya
ü Memasuki area PLB
yang dilakukan
observasi dengan
Jaringan Telkomsel
TRAVELLERS
INBOUND ROAMERS Network Coverage
§ Data LBA Pre-
processing
§ Spatial
mapping
§ Cross border
algorithm
implementati
on
Cross border dashboard monitor
Distribution of visitor’s origin
Tourism Insight Use Case
20. 20
Case of Social Media Analytics
BACKGROUND AND OPPORTUNITIES
SOURCE DATA
Background. Gather information from media online and social media to
analysis hot issues or new request from Business User. And we provide analytics /
platform of Digital Media Analytics that we named it as Sonar Platfor.
Opportunities. These platform will make easy to use by Business user to
find out the Hot issues or posting from outside of company on media online or social
media. We provide analytic for adhoc cases as a request more easyest for Data
Scientist of Requester.
• Social Media
• Media Online
• Others
IMPACT
• Easy to use if we are looking to hot issues & Impact
• Already use by Melon, Blanja, KF
ALGORITHM AND PROCESS
Media
Online
Social
Media
Sonar Platform
Crawling
Crawling
Business user
Dashboard
Analytics
Data Scientist
Adhoc
Dashboard for Social Media Analytics
Kemen BUMN memanfaatkan untuk monitoring semua BUMN
21.
22.
23.
24. Custom Mobility Insight – Segment Heat Map
Derived from Clients’s internal
definition of NGID*, Telkomsel
helps sizing up and visualized
the area populated of
Telkomsel’s subscribers whom
categorized as NGID segment
Greater Jakarta &
Palembang as cities filter
Populations’ Home, Office and Hangout place
category count
*NGID Segment Definition:
Gender: Male
Age: 18-30
Application Accessed:
• Social Media
• Video Streaming
• Ecommerce
25. McKinsey & Company 5
Data availability and cost Computing power Connectivity
Cost of IoT nodes have come
down and are expected to fall
by another
Why now? Computational Power
SOURCE: Wikipedia; V&C; Digital Agenda EU; Internet live stats, McKinsey
Data storage costs have been
reduced by ...
95%
… of the world's data today
has been created
in the last 3 years!
53x 50%
Increase from 1999 to 2016,
to 318,000 million instructions
per second
26. McKinsey & Company 6
SOURCE: Dave Evans (April 2011) "The Internet of Things: How the Next Evolution of the Internet Is ChangingEverything”
Why now? Advanced Analytics
1950’s 1980’s 2010’s
Deep
Learning
A branch
of ML
Machine Learning
A major approachto
realizeAI
Artificial Intelligence
The science of making
intelligent machines
Maths
Data
availability
Costs of data storage
and processing
2020
1980
Transac
tions
Demo- data
graphic
data
Gov.
agencies
Regular
survey/
satisfaction
data
Inputs
from CRM
systems
Telcos
Call
center
Wholesalers
Utilities
(e.g.,
payment
record)
Video analysis
of customer
footage
Comments on
webpages
Website navi-
gation data Social media
sentiment
Human activity
& health data
IoT data (e.g., homes,
cars, devices)
App user
data
~90% of all
data available
today are
estimated to
have been
generated in
the past 2
years
By 2020, 50
billion
devices will
be connected
online
28. Big Data Operating Model
IT & Data
Management
Manage,
gather,
integrate,
extract
data from internal &
external source
Sponsorship & Governance
Organization Structure & Talent Mgt
Capability Development
Data to Insights Insights to action
The process to obtain executive sponsorship and senior
leader commitment to the analytics vision
Organization structure, people, skill set to support
analytics transfromation
Big Data Academy for Data Driven Organization
Process to analyze
data to be insights
Deliver insights, analysis,
recommendation for consumption by
business units
Outcomes
Measurement
KPI
Process to measure
the value of analytic
insights and track the
benefits over time
Unit Bisnis
Business
Use Cases
Innovation
from Data
V
A
L
U
E
Semi-
structure
Unstructure
Data Source
Structure
Activity inside Big Data Unit Activity outside Big Data Unit
Big Data & AI Platform – Providing Big Data Platform, software & licensing
Cloud – Digital Infrastructure – Providing Hardware, Cloud, Data Center – Ensure availability & reliability infrastructure
29. Structure Data
• Any data or information that is located in a fixed field within a defined
record or file, usually in database, spreadsheets. Usually it is
organized in rows and column.
• The most common examples include customer data, sales data,
transactional records, financial data, number of website visit, etc.
• Structure data just represent 20% of all the data available. The
remaining 80% is unstructured data.
30. UnStructure & Semi Structure
• Any data or information that is the term for any data that doesn’t fit
neatly into traditional structure formats or database.
• The most common examples include email conversation, website text,
social media posts, video content, photos, and audio recordings, etc.
• Everything that didn’t fit into database or spreadsheets.
• Semi-structure data is a cross between unstructured and structured
data.
• For example: a tweet can be categorized by author, date, time, length
event.
32. Defining Internal Data
• Refers to all the information your business currently has or has the
potential to collect (customer database, transactional record, etc).
• It can be structured in format or unstructured (customer call record,
employee interview).
• It is owned by your business and this mean only your company
controls access to the data.
• Usually cheap and free to access which often makes it good starting
point when you considering your data option.
33. Defining External Data
• Refers to all the data or information that exist outside of your
organization. This is owned by third party.
• It can be structured in format or unstructured. Social media data,
google trends, government census data, economic data, weather
data, etc.
• For small company, it can be very useful.
• It can be free to access, but sometimes we have to buy 3rd party data
to add from our internal data.
34.
35. Data Management
Data Sources Data Ingestion Data Integration & Transformation
Data Internal
Bappenas
Data External
(Kementrian,
Lembaga Negara)
Data External
(Pemerintah
Daerah)
Data External
(Open Data)
Staging Area
Penyimpanan
raw data, dimana
data dari
berbagai sumber
data disimpan
tanpa merubah
apapun (as is).
Holding Area
Holding Area sebagai dapur dari
Data Engineer and Data Scientist
Sebelum data dipindah ke Data
Mart atau data dikurasi.
Tempat untuk melakukan: Data
Quality, Data Validity, Konversi
Data (string, timestamp)
Join with other tables (e.g..
Lookup to Data Reference)
Append Data Set (Union)
Pembuatan temporary table
Curated Data
Data Bridge
1. Standarisasi data
2. Standarisasi
Metadata
3. Interoperabilitas
4. Referensi Data
Cleansed,
standardised,
organised data
for data delivery
Data Summary
Aggregation to
daily, weekly,
monthly
Quickwin Use
cases SDGs atau
Program Nasional
Holding Area
Digunakan
oleh Data
Engineer
dan Data
Scientists
Data
Acquisition
Data
Taxonomy
Tagging
and
Cataloging
Data
Data
Translation
Auto
Indexing,
Auto
Translation
Data Source
36. Organizations do not need a big data
strategy; they need a business
strategy that incorporates big
data.
Bill Schmarzo, CTO IoT and Analytics, Hitachi Vantara
University San Francisco, School Of Management Executive Fellow
Twitter: @Schmarzo
37. Analytics Value Chain
Learning to “Think Like a Data Scientist”
Prescriptive Actions
(What should we do?)
Plant X and Y crops across
N acres
Pre-order X amount of
fertilizer at 5% discount
Service your harvester
and tractor #2 in January
Hire X number of workers
for Y days
Descriptive Questions
(What happened?)
What were revenues and
profits last year?
How much fertilizer did I
use last planting season?
How much downtime did I
have last month due to
unplanned equipment
maintenance?
How many workers did I
use last month?
Predictive Analytics
(What is likely to happen?)
What will revenues and
profits be next year?
How much fertilizer will I
need next planting season?
When will my equipment
need maintenance next
month?
How many workers will I
need next month and when
will I need them?
Source: Bill Schmarzo “Big Data MBA” Course Curriculum
38. Data Science Engagement Process
Supports rapid exploration, rapid testing, continuous learning “Scientific Method”
REPEAT
Step 1: Define Hypothesis (Decision)
to test or Prediction tomake
Step 2: Gather data…and more data
(Data Lake: SQL + Hadoop)
Historical
Google
Trends
Physician
Notes
Local
Events
Weather
Forecast CDC
Kronos
Epic
Lawson
Step 3: Prepare data; Build schema
(schema-on-query)
Step 4: Visualize the data
(Tableau, Spotfire, ggplot2,…)
Step 5: Build analytic models
(SAS, R, MADlib, Mahout,…)
Step 6: Evaluate model “goodness of fit”
(coefficients, confidence levels)
Source: “Scientific Method: Embrace the Art of Failure”, University of San Francisco School of Management Big Data MBA
40. What Makes a data analytics team?
Programming
Database
Statistical
Mathematical
Visualization
Business/Comm
Data Engineer Data Analyst
Data Scientist
Low High Low High Low High
• Computer science, Software engineer,
database administrator
• Building data infrastructure & pipeline
• Machine learning, predictive analytics,
prescriptive analytics
• Building modelling, recommendation engine
• Business, economy, excel, tableu
• Building business report, insight,for
business team.
42. Organizational Structure: Centralized Approach
Chief Data
Scientist
Business Unit Leaders
Data Scientists
Pros Cons
Flexible resources require less
initial investment
Prioritization of project requests
can be difficult
Simple for data scientists to share
ideas and best practices
Difficult for data scientists to
acquire specific domain
knowledge for each business unit
43. Organizational Structure: Decentralized
Approach Business Unit Leaders
Data Scientists
Pros Cons
Data scientists gain a better
understanding of their assigned
business unit and can proactively
bring new data-driven solutions to
the business
Difficult for data scientists to share
best practices, data sources,
software, etc.
Business units are more likely to
be involved
Data scientists optimize locally
rather than globally
44. Organizational Structure: Deployed Approach
Pros Cons
Ability to share ideas and best
practices
Data scientists report to two
bosses
Ability to acquire specific domain
knowledge and proactively bring
new ideas to management
Access to data scientists and
resources is competitive
Optimize globally rather than locally
Chief Data
Scientist
Data Scientists Business Unit Leaders
46. 1. Don’t Think Big Data Technology,
Think Business Transformation
Technology
INITIATIVES
SCIENCE
EXPERIMENT
Source: “Driving Business Strategies with Data Science Big Data MBA”, Schmarzo, 2016
47. Arif Rachman
2. Don’t Think Business Intelligence,
Think Data Science
Arif Rachman
Data Science
Reporting (Descriptive analytics)
Predicting (Predictive analytics)
Reccomending (Prescriptive analytics)
Most Internal Internal - External
OLAP, ETL, Data Warehousing
Cust. Service, Sales, Marketing, Operation,
Employee Performance
IT, Business Technology
Cloud Platforms, Python, R Machine Learning
Transactional, Social Machine, Audio, Video, Emails, PDFs
Math, Stats, Coding, Business
Outputs
Data Sources
Technologies
Types of Data
Expertise
Business Intelligence
Source: “Driving Business Strategies with Data Science Big Data MBA”, Schmarzo, 2016
48. 3. Don’t Think Data Warehouse,
Think Data Lake
Source: “Driving Business Strategies with Data Science Big Data MBA”, Schmarzo, 2016
“Hadoop and HDFS is a game changer”
§ Massively parallel processing
§ Cheap scale-out data architecture
Data Lake enables to gather, manage,
enrich, and analyze many new sources
od data, wether structured or
unstructured
49. Order [5.000] units of Component Z to
support widget sales for next month
Hire [Y] new sales reps by these zip codes to
handle projected Christmas sales
Set aside [$125k] in financial reserve to
cover Product X returns
Sell the following product mix to achieve
quarterly revenue and margin goals
Increase hiring pipeline by 35% to
achieve hiring goals
4. Don’t Think “What Happened”,
Think ”What Will Happen”
Source: “Driving Business Strategies with Data Science Big Data MBA”, Schmarzo, 2016
“What Happened”
How many widgets did I sell last
month?
What were sales by zip code for
Christmas last year?
How many of product X were
returned last month?
What were company revenues for
the past quarter?
How many employees did I hire
last year?
How many widgets will I sell next
month?
What will be sales by zip code
over this Chirstmas season?
How many of product X will be
returned next month?
What were projected company
revenues for next quarter?
How many employees will I need
to hire next year?
“What Will Happen”
“What Should I do”
50. 5. Don’t Think HIPPO,
Think Collaboration
Source: “Driving Business Strategies with Data Science Big Data MBA”, Schmarzo, 2016
Collaboration
The key to big data success
Empowering cross-functional
collaboration
Exploratory thinking to challenge
long-held organizational rules
Inclusive of all the key stakeholders
