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# DATA SCIENTIST 1.pdf

Introduction of Data Scientist

Introduction of Data Scientist

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### DATA SCIENTIST 1.pdf

1. 1. HOW TO BECOME A “REAL” DATA SCIENTIST (TH, 2017)
2. 2. 1+1=?
3. 3. 1+1=2
4. 4. 1+1=2 ?
5. 5. 25÷5=?
6. 6. 25÷5=14
7. 7. __ 5/25
8. 8. 1_ 5/25
9. 9. 1_ 5/25 5
10. 10. 1_ 5/25 5-
11. 11. 1_ 5/25 5- 20
12. 12. 14 5/25 5- 20
13. 13. 14 5/25 5- 20 20
14. 14. 14 5/25 5- 20 20- 0
15. 15. 14 5×
16. 16. 14 5× 20
17. 17. 14 5× 20 5
18. 18. 14 5× 20 5+ 25
19. 19. BUMI ITU…
20. 20. BUMI ITU… BULAT VS DATAR ???
21. 21. ?
22. 22. ?
23. 23. Why? Why? Why? Why? Why? Why?
24. 24. SCIENTIST IS KAYPOH !! Why? Why? Why? Why? Why? Why?
25. 25. WIKIPEDIA: A scientist is a person engaging in a systematic activity to acquire knowledge that describes and predicts the natural world.
26. 26. WIKIPEDIA: Data  A scientist is a person engaging in a systematic activity to acquire knowledge that describes and predicts the natural world.
27. 27. WIKIPEDIA: Data  A scientist is a person engaging in a systematic activity to acquire knowledge that describes and predicts the natural world.→ Data
28. 28. WIKIPEDIA: Data  A scientist is a person engaging in a systematic activity to acquire knowledge that describes and predicts the natural world.→ Data Data: a set of values of qualitative or quantitative variables.
29. 29. WIKIPEDIA: Data  A scientist is a person engaging in a systematic activity to acquire knowledge that describes and predicts the natural world.→ Data Data: a set of values of qualitative or quantitative variables.
30. 30. WIKIPEDIA: Data  A scientist is a person engaging in a systematic activity to acquire knowledge that describes and predicts the natural world.→ Data Data: a set of values of qualitative or quantitative variables.
31. 31. Formulation MATHEMATICAL MODEL (MODELER) MATHEMATICAL RESULTS (PROGRAMMER) REAL WORLD (DATA ANALYST) Interpretation Mathematical Analysis DATA SCIENTIST
32. 32. DATA SCIENTIST Formulation MATHEMATICAL MODEL (MODELER) MATHEMATICAL RESULTS (PROGRAMMER) REAL WORLD (DATA ANALYST) Ex: Text Mining Interpretation Mathematical Analysis
33. 33. DATA SCIENTIST Formulation MATHEMATICAL MODEL (MODELER) Latent Dirichlet Allocation MATHEMATICAL RESULTS (PROGRAMMER) REAL WORLD (DATA ANALYST) Ex: Text Mining Interpretation Mathematical Analysis
34. 34. DATA SCIENTIST Formulation MATHEMATICAL MODEL (MODELER) Latent Dirichlet Allocation MATHEMATICAL RESULTS (PROGRAMMER) Topic Model REAL WORLD (DATA ANALYST) Ex: Text Mining Interpretation Mathematical Analysis
35. 35. RESEARCH METHODOLOGY PROBLEMS
36. 36. RESEARCH METHODOLOGY PROBLEMS
37. 37. RESEARCH METHODOLOGY PROBLEMS TRIGGERS
38. 38. RESEARCH METHODOLOGY PROBLEMS TRIGGERS DATA DATA SCIENTIST
39. 39. RESEARCH METHODOLOGY PROBLEMS TRIGGERS DATA DATA SCIENTIST
40. 40. RESEARCH METHODOLOGY PROBLEMS SOLVE TRIGGERS DATA DATA SCIENTIST
41. 41. RESEARCH METHODOLOGY PROBLEMS SOLVE QUALITATIVE METHOD QUANTITATIVE METHOD TRIGGERS DATA DATA SCIENTIST
42. 42. RESEARCH METHODOLOGY PROBLEMS SOLVE QUALITATIVE METHOD QUANTITATIVE METHOD TRIGGERS DATA DATA SCIENTIST √ ×
43. 43. APPROACHES STATISTICS MACHINE LEARNING
44. 44. APPROACHES STATISTICS -Population VS Sample MACHINE LEARNING -Training VS Testing
45. 45. APPROACHES STATISTICS -Population VS Sample -Confidence MACHINE LEARNING -Training VS Testing -Accuracy
46. 46. APPROACHES STATISTICS -Population VS Sample -Confidence MACHINE LEARNING -Training VS Testing -Accuracy (SAMPLE)DATA≠(BIG)DATA
47. 47. Variables
48. 48. Variables Measurable Latent
49. 49. Variables Measurable Latent Categorical Numerical
50. 50. Variables Measurable Latent Categorical Numerical Likert Thurstone Semantic Differential
51. 51. Variables Measurable Latent Categorical Numerical Nominal Ordinal Interval Ratio Likert Thurstone Semantic Differential
52. 52. NOTES: -Big data analytic needs CLEAR definition of variables.
53. 53. NOTES: -Big data analytic needs CLEAR definition of variables. -Data cleansing is a MUST!!
54. 54. NOTES: -Big data analytic needs CLEAR definition of variables. -Data cleansing is a MUST!! -Garbage in, Garbage out.
55. 55. Now assume that you have a cleansed big data set...
56. 56. Now assume that you have a cleansed big data set... - Describe the data using visualization or other appropriate measurements.
57. 57. Now assume that you have a cleansed big data set... - Describe the data using visualization or other appropriate measurements. - Define the problem.
58. 58. Now assume that you have a cleansed big data set... - Describe the data using visualization or other appropriate measurements. - Define the problem. - Supervised VS Unsupervised
59. 59. Now assume that you have a cleansed big data set... - Describe the data using visualization or other appropriate measurements. - Define the problem. - Supervised VS Unsupervised - Balanced VS Unbalanced
60. 60. Now assume that you have a cleansed big data set... - Describe the data using visualization or other appropriate measurements. - Define the problem. - Supervised VS Unsupervised - Balanced VS Unbalanced - Cross-section VS Time-Series VS Panel
61. 61. Now assume that you have a cleansed big data set... - Describe the data using visualization or other appropriate measurements. - Define the problem. - Supervised VS Unsupervised - Balanced VS Unbalanced - Cross-section VS Time-Series VS Panel - Prediction: Estimation VS Forecasting
62. 62. Now assume that you have a cleansed big data set... - Describe the data using visualization or other appropriate measurements. - Define the problem. - Supervised VS Unsupervised - Balanced VS Unbalanced - Cross-section VS Time-Series VS Panel - Prediction: Estimation VS Forecasting - Improvement: Accuracy VS Insight
63. 63. Now assume that you have a cleansed big data set... - Describe the data using visualization or other appropriate measurements. - Define the problem. - Supervised VS Unsupervised - Balanced VS Unbalanced - Cross-section VS Time-Series VS Panel - Prediction: Estimation VS Forecasting - Improvement: Accuracy VS Insight - Modeling.
64. 64. Now assume that you have a cleansed big data set... - Describe the data using visualization or other appropriate measurements. - Define the problem. - Supervised VS Unsupervised - Balanced VS Unbalanced - Cross-section VS Time-Series VS Panel - Prediction: Estimation VS Forecasting - Improvement: Accuracy VS Insight - Modeling. - Expertise
65. 65. Now assume that you have a cleansed big data set... - Describe the data using visualization or other appropriate measurements. - Define the problem. - Supervised VS Unsupervised - Balanced VS Unbalanced - Cross-section VS Time-Series VS Panel - Prediction: Estimation VS Forecasting - Improvement: Accuracy VS Insight - Modeling. - Expertise - Econometric
66. 66. Now assume that you have a cleansed big data set... - Describe the data using visualization or other appropriate measurements. - Define the problem. - Supervised VS Unsupervised - Balanced VS Unbalanced - Cross-section VS Time-Series VS Panel - Prediction: Estimation VS Forecasting - Improvement: Accuracy VS Insight - Modeling. - Expertise - Econometric - AI
67. 67. Now assume that you have a cleansed big data set... - Describe the data using visualization or other appropriate measurements. - Define the problem. - Supervised VS Unsupervised - Balanced VS Unbalanced - Cross-section VS Time-Series VS Panel - Prediction: Estimation VS Forecasting - Improvement: Accuracy VS Insight - Modeling. - Expertise - Econometric - AI - Hybrid
68. 68. Data Validation set Training set Test set Train classifier Homogeneous ensemble algorithm Individual classification algorithm Apply model Classification models Apply model Test set prediction Train classifier Ensemble model Validation set predictions Apply model Heterogeneous ensemble algorithm Features Selection Clustering Estimated Value STATISTICAL LEARNING FLOWCHART PLIZ, OJO NGE-LIB!!
69. 69. OPTIMAL INDIVIDUAL SALES ALLOCATION & FORECASTING ASTRA HONDA MOTOR - METRA DIGITAL MEDIA -
70. 70. Description Value ROW_ID Row ID NUMERIC MAIN_PARTNER Nomor referral ID dari Astra World (AWO) NUMERIC FRAME_NO Nomor rangka motor yang dipunyai customer TEXT CUST_ID Nomor ID customer yang didapat dari KTP/SIM TEXT SALES_DATE Tangga sepeda motor honda dibeli DATE (YYYY-MM-DD HH:MM:SS) KODE_MESIN Tiap tipe motor mempunyai kode mesin yang berbeda dengan tipe motor yang lain 75 NOMINAL {JF81E, ...} SEQUENCE_MESIN Sequence dari kode mesin NUMERIC VARIAN_MOTOR Varian motor yang dipunyai customer 76 NOMINAL {ALL NEW VARIO, …} COLOR Warna motor yang dipunyai customer 73 NOMINAL {HITAM, …} KODE_CUSTOMER Tipe customer {INDIVIDUAL, COLLECTIVE, GROUP, JOINT PROMO} JENIS_KELAMIN Jenis kelamin customer {LAKI-LAKI, PEREMPUAN} TANGGAL_LAHIR Bulan dan tahun lahir customer DATE (MM/YYYY) KELURAHAN_SURAT Kelurahan surat menyurat customer 1251 NOMINAL {KETEWEL, …} KECAMATAN_SURAT Kecamatan surat menyurat customer 120 NOMINAL {SUKAWATI, …} KOTA_SURAT Kota surat menyurat customer 30 NOMINAL {KAB. GIANYAR, …} KODE_POS Kode pos surat menyurat customer NUMERIC PROPINSI Propinsi surat menyurat customer 8 NOMINAL {BALI, …} STATUS_RUMAH Status rumah customer {RUMAH SENDIRI, RUMAH SEWA, RUMAH ORANG TUA/KELUARGA} JENIS_PENJUALAN_STNK Jenis penjualan saat keluar faktur (bener-benar terjual) {CASH, CREDIT} JENIS_PENJUALAN_SSU Jenis penjualan ini saat deal, bisa berubah saat transaksi {CASH, CREDIT} NAMA_LEASING_COMPANY Nama leasing company yang menangani cicilan customer TEXT BESAR_DP Besar DP yang diberikan customer TEXT BESAR_CICILAN Besar cicilan per bulan NUMERIC LAMA_CICILAN Lama cicilan sampai lunas (bulan) NUMERIC AGAMA Agama customer {HINDU, KRISTEN, ISLAM, KATOLIK, LAIN-LAIN, BUDHA} PEKERJAAN Pekerjaan customer 16 NOMINAL {PEGAWAI SWASTA, …} PENGELUARAN Pengeluaran customer per bulan {1,2,3,4,5,6,7} PENDIDIKAN Pendidikan terakhir customer {SLTA/SMU, AKADEMI/DIPLOMA, TIDAK TAMAT SD, SD, SLTP/SMP, SARJANA, PASCA SARJANA} NO_HP Nomor handphone customer TEXT STATUS_NOMOR_HP Tipe kartu handphone customer {PRABAYAR, PASCABAYAR} NO_TLP Nomor telepon customer TEXT KEBERSEDIAAN DIHUBUNGI Kebersediaan customer untuk dihubungi lagi di masa depan {YES, NO} MERK_MOTOR_SBLMNYA Merk motor yang dipunyai customer sebelumnya {HONDA, YAMAHA, SUZUKI, BELUM PERNAH MEMILIKI, KAWASAKI, MOTOR LAIN} TYPE_MOTOR_SBLMNYA Tipe motor yang dipunyai customer sebelumnya {AT AUTOMATIC, CUB BEBEK, SPORT, BELUM PERNAH MEMILIKI} SMH_DIGUNAKAN_UNTUK Tujuan dibelinya sepeda motor {LAIN-LAIN, KEBUTUHAN KELUARGA, KE SEKOLAH/ KE KAMPUS, BERDAGANG, PEMAKAIAN JARAK DEKAT, REKREASI / OLAH RAGA, BEKERJA} YG_MENGGUNAKAN_SMH Orang yang akan menggunakan sepeda motor yang dibeli {ANAK, LAIN-LAIN, PASANGAN SUAMI ATAU ISTRI, SAYA SENDIRI} MD Kode Main Dealer yang membawahi dealer tempat customer membeli sepeda motor Honda {N01} DEALER_CODE Kode dealer tempat customer membeli sepeda motor Honda 77 NOMINAL {06877, …} KODE_SALES_PERSON Kode sales person yang menjual sepeda motor Honda ke customer 1718 NOMINAL {218595, …} TGL_MASUK_DATA Tanggal masuk ke AHM dari MD DATE (YYYY-MM-DD HH:MM:SS) STATUS_VALIDASI Validasi dari MD untuk menandakan apakah baris data CDB terkait sudah divalidasi kebenarannya atau belum {1,2} UPLOADED_ON Tanggal masuk ke AWO dari AHM DATE (YYYY-MM-DD HH:MM:SS)
71. 71. METHODOLOGY AHM
72. 72. METHODOLOGY AHM DEALER DEALER DEALER
73. 73. METHODOLOGY AHM DEALER DEALER DEALER ❶
74. 74. METHODOLOGY AHM DEALER DEALER DEALER ❶ FORECASTING
75. 75. METHODOLOGY AHM DEALER DEALER DEALER ❶ ALLOCATION FORECASTING
76. 76. METHODOLOGY AHM DEALER DEALER DEALER ❶ ALLOCATION FORECASTING ❷
77. 77. METHODOLOGY AHM DEALER DEALER DEALER ❶ ALLOCATION FORECASTING ❷ FORECASTING
78. 78. METHODOLOGY AHM DEALER DEALER DEALER ❶ ALLOCATION FORECASTING ❷ FORECASTING TOTAL
79. 79. METHODOLOGY AHM DEALER DEALER DEALER ALLOCATION FORECASTING ❷ FORECASTING TOTAL
80. 80. METHODOLOGY AHM DEALER DEALER DEALER ALLOCATION FORECASTING ❷ FORECASTING TOTAL
81. 81. METHODOLOGY AHM DEALER DEALER DEALER ALLOCATION FORECASTING ❷ FORECASTING TOTAL ❶
82. 82. METHODOLOGY AHM DEALER DEALER DEALER ALLOCATION FORECASTING ❷ FORECASTING TOTAL ❶
83. 83. METHODOLOGY AHM DEALER DEALER DEALER ❶ ALLOCATION FORECASTING ❷ FORECASTING TOTAL
84. 84. METHODOLOGY AHM DEALER DEALER DEALER ❶ ALLOCATION FORECASTING ❷ FORECASTING TOTAL ❷
85. 85. METHODOLOGY AHM DEALER DEALER DEALER ❶ ALLOCATION FORECASTING ❷ FORECASTING TOTAL ❷
86. 86. DATA PREPARATION NO VARIABEL NO VARIABEL 1ROW_ID 22BESAR_DP 2MAIN_PARTNER 23BESAR_CICILAN 3FRAME_NO 24LAMA_CICILAN 4CUST_ID 25AGAMA 5SALES_DATE 26PEKERJAAN 6KODE_MESIN 27PENGELUARAN 7SEQUENCE_MESIN 28PENDIDIKAN 8VARIAN_MOTOR 29NO_HP 9COLOR 30STATUS_NOMOR_HP 10KODE_CUSTOMER 31NO_TLP 11JENIS_KELAMIN 32KEBERSEDIAAN DIHUBUNGI 12TANGGAL_LAHIR 33MERK_MOTOR_SBLMNYA 13KELURAHAN_SURAT 34TYPE_MOTOR_SBLMNYA 14KECAMATAN_SURAT 35SMH_DIGUNAKAN_UNTUK 15KOTA_SURAT 36YG_MENGGUNAKAN_SMH 16KODE_POS 37MD 17PROPINSI 38DEALER_CODE 18STATUS_RUMAH 39KODE_SALES_PERSON 19JENIS_PENJUALAN_STNK 40TGL_MASUK_DATA 20JENIS_PENJUALAN_SSU 41STATUS_VALIDASI 21NAMA_LEASING_COMPANY 42UPLOADED_ON
87. 87. DATA PREPARATION NO VARIABEL NO VARIABEL 1ROW_ID 22BESAR_DP 2MAIN_PARTNER 23BESAR_CICILAN 3FRAME_NO 24LAMA_CICILAN 4CUST_ID 25AGAMA 5SALES_DATE 26PEKERJAAN 6KODE_MESIN 27PENGELUARAN 7SEQUENCE_MESIN 28PENDIDIKAN 8VARIAN_MOTOR 29NO_HP 9COLOR 30STATUS_NOMOR_HP 10KODE_CUSTOMER 31NO_TLP 11JENIS_KELAMIN 32KEBERSEDIAAN DIHUBUNGI 12TANGGAL_LAHIR 33MERK_MOTOR_SBLMNYA 13KELURAHAN_SURAT 34TYPE_MOTOR_SBLMNYA 14KECAMATAN_SURAT 35SMH_DIGUNAKAN_UNTUK 15KOTA_SURAT 36YG_MENGGUNAKAN_SMH 16KODE_POS 37MD 17PROPINSI 38DEALER_CODE 18STATUS_RUMAH 39KODE_SALES_PERSON 19JENIS_PENJUALAN_STNK 40TGL_MASUK_DATA 20JENIS_PENJUALAN_SSU 41STATUS_VALIDASI 21NAMA_LEASING_COMPANY 42UPLOADED_ON NO VARIABEL 1SALES_DATE 2JENIS_PENJUALAN_STNK 3KODE_CUSTOMER 4BESAR_DP 5BESAR_CICILAN 6LAMA_CICILAN 7DEALER_CODE
88. 88. DATA PREPARATION NO VARIABEL NO VARIABEL 1ROW_ID 22BESAR_DP 2MAIN_PARTNER 23BESAR_CICILAN 3FRAME_NO 24LAMA_CICILAN 4CUST_ID 25AGAMA 5SALES_DATE 26PEKERJAAN 6KODE_MESIN 27PENGELUARAN 7SEQUENCE_MESIN 28PENDIDIKAN 8VARIAN_MOTOR 29NO_HP 9COLOR 30STATUS_NOMOR_HP 10KODE_CUSTOMER 31NO_TLP 11JENIS_KELAMIN 32KEBERSEDIAAN DIHUBUNGI 12TANGGAL_LAHIR 33MERK_MOTOR_SBLMNYA 13KELURAHAN_SURAT 34TYPE_MOTOR_SBLMNYA 14KECAMATAN_SURAT 35SMH_DIGUNAKAN_UNTUK 15KOTA_SURAT 36YG_MENGGUNAKAN_SMH 16KODE_POS 37MD 17PROPINSI 38DEALER_CODE 18STATUS_RUMAH 39KODE_SALES_PERSON 19JENIS_PENJUALAN_STNK 40TGL_MASUK_DATA 20JENIS_PENJUALAN_SSU 41STATUS_VALIDASI 21NAMA_LEASING_COMPANY 42UPLOADED_ON NO VARIABEL 1SALES_DATE 2JENIS_PENJUALAN_STNK 3KODE_CUSTOMER 4BESAR_DP 5BESAR_CICILAN 6LAMA_CICILAN 7DEALER_CODE
89. 89. DATA PREPARATION NO VARIABEL NO VARIABEL 1ROW_ID 22BESAR_DP 2MAIN_PARTNER 23BESAR_CICILAN 3FRAME_NO 24LAMA_CICILAN 4CUST_ID 25AGAMA 5SALES_DATE 26PEKERJAAN 6KODE_MESIN 27PENGELUARAN 7SEQUENCE_MESIN 28PENDIDIKAN 8VARIAN_MOTOR 29NO_HP 9COLOR 30STATUS_NOMOR_HP 10KODE_CUSTOMER 31NO_TLP 11JENIS_KELAMIN 32KEBERSEDIAAN DIHUBUNGI 12TANGGAL_LAHIR 33MERK_MOTOR_SBLMNYA 13KELURAHAN_SURAT 34TYPE_MOTOR_SBLMNYA 14KECAMATAN_SURAT 35SMH_DIGUNAKAN_UNTUK 15KOTA_SURAT 36YG_MENGGUNAKAN_SMH 16KODE_POS 37MD 17PROPINSI 38DEALER_CODE 18STATUS_RUMAH 39KODE_SALES_PERSON 19JENIS_PENJUALAN_STNK 40TGL_MASUK_DATA 20JENIS_PENJUALAN_SSU 41STATUS_VALIDASI 21NAMA_LEASING_COMPANY 42UPLOADED_ON NO VARIABEL 1SALES_DATE 2JENIS_PENJUALAN_STNK 3KODE_CUSTOMER 4HARGA_MOTOR 5DEALER_CODE NO VARIABEL 1SALES_DATE 2JENIS_PENJUALAN_STNK 3KODE_CUSTOMER 4BESAR_DP 5BESAR_CICILAN 6LAMA_CICILAN 7DEALER_CODE
90. 90. OPTIMAL INDIVIDUAL SALES FORECASTING
91. 91. OPTIMAL INDIVIDUAL SALES ALLOCATION Rp- Rp200,000,000 Rp400,000,000 Rp600,000,000 Rp800,000,000 Rp1,000,000,000 Rp1,200,000,000 Rp1,400,000,000 Rp1,600,000,000
92. 92. EXISTING VS PROPOSED ALLOCATION METHODOLOGY Rp- Rp500,000,000 Rp1,000,000,000 Rp1,500,000,000 Rp2,000,000,000 Rp2,500,000,000 Rp3,000,000,000 Rp3,500,000,000 3749 12642 9701 432 8692 12628 987 637 10244 11662 10090 9669 5563 7525 2564 8693 11840 793 4010 12993 9219 8122 5920 794 2521 9222 11646 5553 1847 5772 9221 11422 166 6855 7715 7803 6330 9220 10290 5780 2546 5267 11844 1646 13701 9223 13718 6877 2519 1905 10291 1904 12421 10098 12177 12646 986 3426 810 811 984 9930 15934 4 7440 812 Proposed Existing
93. 93. Description Value ROW_ID Row ID NUMERIC MAIN_PARTNER Nomor referral ID dari Astra World (AWO) NUMERIC FRAME_NO Nomor rangka motor yang dipunyai customer TEXT CUST_ID Nomor ID customer yang didapat dari KTP/SIM TEXT SALES_DATE Tangga sepeda motor honda dibeli DATE (YYYY-MM-DD HH:MM:SS) KODE_MESIN Tiap tipe motor mempunyai kode mesin yang berbeda dengan tipe motor yang lain 75 NOMINAL {JF81E, ...} SEQUENCE_MESIN Sequence dari kode mesin NUMERIC VARIAN_MOTOR Varian motor yang dipunyai customer 76 NOMINAL {ALL NEW VARIO, …} COLOR Warna motor yang dipunyai customer 73 NOMINAL {HITAM, …} KODE_CUSTOMER Tipe customer {INDIVIDUAL, COLLECTIVE, GROUP, JOINT PROMO} JENIS_KELAMIN Jenis kelamin customer {LAKI-LAKI, PEREMPUAN} TANGGAL_LAHIR Bulan dan tahun lahir customer DATE (MM/YYYY) KELURAHAN_SURAT Kelurahan surat menyurat customer 1251 NOMINAL {KETEWEL, …} KECAMATAN_SURAT Kecamatan surat menyurat customer 120 NOMINAL {SUKAWATI, …} KOTA_SURAT Kota surat menyurat customer 30 NOMINAL {KAB. GIANYAR, …} KODE_POS Kode pos surat menyurat customer NUMERIC PROPINSI Propinsi surat menyurat customer 8 NOMINAL {BALI, …} STATUS_RUMAH Status rumah customer {RUMAH SENDIRI, RUMAH SEWA, RUMAH ORANG TUA/KELUARGA} JENIS_PENJUALAN_STNK Jenis penjualan saat keluar faktur (bener-benar terjual) {CASH, CREDIT} JENIS_PENJUALAN_SSU Jenis penjualan ini saat deal, bisa berubah saat transaksi {CASH, CREDIT} NAMA_LEASING_COMPANY Nama leasing company yang menangani cicilan customer TEXT BESAR_DP Besar DP yang diberikan customer TEXT BESAR_CICILAN Besar cicilan per bulan NUMERIC LAMA_CICILAN Lama cicilan sampai lunas (bulan) NUMERIC AGAMA Agama customer {HINDU, KRISTEN, ISLAM, KATOLIK, LAIN-LAIN, BUDHA} PEKERJAAN Pekerjaan customer 16 NOMINAL {PEGAWAI SWASTA, …} PENGELUARAN Pengeluaran customer per bulan {1,2,3,4,5,6,7} PENDIDIKAN Pendidikan terakhir customer {SLTA/SMU, AKADEMI/DIPLOMA, TIDAK TAMAT SD, SD, SLTP/SMP, SARJANA, PASCA SARJANA} NO_HP Nomor handphone customer TEXT STATUS_NOMOR_HP Tipe kartu handphone customer {PRABAYAR, PASCABAYAR} NO_TLP Nomor telepon customer TEXT KEBERSEDIAAN DIHUBUNGI Kebersediaan customer untuk dihubungi lagi di masa depan {YES, NO} MERK_MOTOR_SBLMNYA Merk motor yang dipunyai customer sebelumnya {HONDA, YAMAHA, SUZUKI, BELUM PERNAH MEMILIKI, KAWASAKI, MOTOR LAIN} TYPE_MOTOR_SBLMNYA Tipe motor yang dipunyai customer sebelumnya {AT AUTOMATIC, CUB BEBEK, SPORT, BELUM PERNAH MEMILIKI} SMH_DIGUNAKAN_UNTUK Tujuan dibelinya sepeda motor {LAIN-LAIN, KEBUTUHAN KELUARGA, KE SEKOLAH/ KE KAMPUS, BERDAGANG, PEMAKAIAN JARAK DEKAT, REKREASI / OLAH RAGA, BEKERJA} YG_MENGGUNAKAN_SMH Orang yang akan menggunakan sepeda motor yang dibeli {ANAK, LAIN-LAIN, PASANGAN SUAMI ATAU ISTRI, SAYA SENDIRI} MD Kode Main Dealer yang membawahi dealer tempat customer membeli sepeda motor Honda {N01} DEALER_CODE Kode dealer tempat customer membeli sepeda motor Honda 77 NOMINAL {06877, …} KODE_SALES_PERSON Kode sales person yang menjual sepeda motor Honda ke customer 1718 NOMINAL {218595, …} TGL_MASUK_DATA Tanggal masuk ke AHM dari MD DATE (YYYY-MM-DD HH:MM:SS) STATUS_VALIDASI Validasi dari MD untuk menandakan apakah baris data CDB terkait sudah divalidasi kebenarannya atau belum {1,2} UPLOADED_ON Tanggal masuk ke AWO dari AHM DATE (YYYY-MM-DD HH:MM:SS)