Business Transformation with Data Intelligence.

Transcription

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2 Business Transformation with Data Intelligence

3 Why invest in Data Quality? By 2017, 33% of Fortune 100 organizations will experience an information crisis, due to their inability to effectively value, govern and trust their enterprise information. Source: Gartner Data IQ reports that 92% of companies suspect that their customer data is inaccurate Data is the lifeblood of an organisation, and quality data is crucial to the business

4 DATA QUALITY is not a project, it s a Lifestyle. The rule is a concept used to quantify the costs of quality issues.

5 Costs of Poor Address Data Quality Visible Costs Transportation & Warehousing Costs Inventory Carrying Costs Labor Cost Data Quality Inspection Costs Administrative Costs Deliveries Delayed Operations Longer Orders Processing Cycle Lower Competitive Advantages Data Analytics Inaccurate Analysis Hidden Costs Sales Communication Costs Inaccurate Sales Pipeline Loss of Potential Revenue Finance Overdue Receivables Pricing or Billing Error Customer Services Customer Dissatisfaction Complaint Handling Costs Customer Retention Costs Management Poor Informed Decision Making Company Reputational Damage

6 Data Quality Management Cycle 1. Assess data quality 4. Monitor data quality 2. Clean data 3. Improve business process

7 Data Quality & Preparation

8 Common Data Challenges 1. Duplicated data 2. Incomplete or missing data 3. Outdated contact information 4. Dirty data 5. Inconsistent format from multiple data sources File 1 Customer Name Address Postal Code Fullmark Pte Ltd 10 Soon Lee Rd, Fullmark Industrial Bldg, # United Overseas Bank Limited 80 Raffles Place UOB Plaza AFPD Pte Ltd 10 Tampines Industrial Ave 3 Singapore File 2 Company Name Addr1 Addr2 Addr3 Zip Code UOB Attn: Mr. Lee Kok Keong 451 Clementi Avenue 3 Tel: / S'pore Keppel Land Limited Bugis Junction Towers # Victoria St. Singapore FMC Technologies P/L 40 Scotts Rd, S'pore Attn: Karen Chia (IT Dept) Tel:

9 Data Standardisation Company Level Company Name Format* Industry and Sub-Industry Employee Size Annual Revenue Phone Numbers Mainline Fax Number Address Individual Segment Full Address Contact Level Job Title Job Level Department Domain Valid Corporate Domain Phone Numbers Direct Line Mobile *standardised during de-duplication

10 10 Company Name Standardisation Formatting Company Names (English and Non-English Company Type) E.g. Indonesian companies have a PT appended at the back of the name Country Company Name Corrected Company Name Indonesia PT Excelcom (Axiata Group Berhad) TBK Excelcom (Axiata Group Berhad) TBK, PT Indonesia PT TBK Excelcom (Axiata Group Berhad) Excelcom (Axiata Group Berhad) TBK, PT Indonesia PT Nusa Network Prakarsa Nusa Network Prakarsa, PT Indonesia Nusa Network Prakarsa PT. Nusa Network Prakarsa, PT

11 11 Standardisation Address Fields Standardise and parse unstructured address into individual address segments. Address 1 Address 2 Address 3 The Plaza Office Tower 42nd Floor Jl. MH. Thamrin Kav Jakarta Jl. P. Bunaken A3, Lantai 2, Kawasan Industri Medan 3 Kota Medan, 20242, Indonesia House No. Street Floor Building City Ward Postal Code Kav Jl. MH. Thamrin 42nd Floor The Plaza Office Tower Jakarta Jl. P. Bunaken A3 Lantai 2 Medan Kawasan Industri Medan 3 Country Indonesia Combine the segmented addresses House No. + Street + City/Country to get precise GPS coordinates from geocoding API. Jl. MH. Thamrin Kav Jakarta Latitude: Longitude: Jl. P. Bunaken A3 Medan Indonesia Latitude: Longitude:

12 Data Cleansing Steps Company Deduplication Merge company information Map company records Standardize data fields Company Matching Company Details Mainline and Fax Address Industry Employee Size Annual Revenue Master Database Standardized fields Matching priority fields Matching Confidence Fields Merging Based on Data Priorities Map parent /child relationships Map Contact IDs

13 Data Matching Methodology Natural Language Processing & Rule Based Algorithm 100% probability Exact Match Remove Extra Space & Insert Missing Space 2 Levels Remove Special Characters (., ) 3 Levels Standardize Keywords (Pte. Co.,) 7 Levels 50% probability Single & Double Letters Typo Errors

14 What s in an Account Name? Input by Sales Rep Entered Value Gillette Singapore - HQ Gillettes Singapore - Operations Office Unilever Singapore Pte Ltd Unilever (SG) Pte Ltd Unilever P/L UOB (Ang Mo Kio Branch) UOB - Serangoon Outlet Common Trend: Append extra account site information in the Account Name

15 Account Name Deduplication System Assigns Matched Probability 15 levels of name matching rules with varying matching confidence Entered Value Normalised Name Proposed Match Value Match Prob (%) Match Remark Gillette Singapore - HQ Gillette Singapore Gillette Singapore Proposed master name Gillettes Singapore - Operations Office Gillettes Singapore Gillette Singapore 50% Single letter typo Unilever Singapore Pte Ltd Unilever Singapore Pte Ltd Unilever Singapore Pte Ltd Proposed master name Unilever (SG) Pte Ltd Unilever (Singapore) Pte Ltd Unilever Singapore Pte Ltd 100% Exact Match Unilever P/L Unilever Pte Ltd Unilever Singapore Pte Ltd 90% Remove country UOB (Ang Mo Kio Branch) UOB UOB Proposed master name UOB - Serangoon Outlet UOB UOB 100% Exact Match

16 Data Merging Different merging treatment across all data fields Merge specific data fields in accordance with business methodology and predefined merging rules Account Name Address Phone Number Last Modified Date Unilever Singapore Pte Ltd 20 Pasir Panjang Road Dec-2015 Unilever (SG) Pte Ltd Final Cleansed Record 20 Pasir Panjang Rd, #06-22 Mapletree Business City Account Name Address Phone Number Mar-2016 Sample Merging Rule: Address: Level of completeness* Phone Number: Merge all unique values *Address Standardisation is a pre-requisite Unilever Singapore Pte Ltd 20 Pasir Panjang Rd, #06-22 Mapletree Business City ,

17 PROCESS IMPROVEMENTS

18 Master Data

19 Process Improvement Scenario 1 Master Data Linking for Single View Customer Relationship Management (CRM) Cleansed data channeled back to respective applications Enterprise Resource Planning (ERP) Supply Chain Management (SCM) Ent-Vision Data Standardisation Data Deduplication Data Merging Single View Accurate Data Analysis & Reporting Marketing Automation (MA)

20 Process Improvement Scenario 2 Dynamics Route Planning Standard Route Planning (By Zones) Handled by Team 1 & Team 2 Central Team 2 North East Team 1 Dynamics Route Planning (By Proximity) Handled by Team 1 based on nearest delivery locations which is more cost-effective

21 Process Improvement Scenario 3 Optimise Sales Route Planning With GeoAnalytics Monday Tuesday Wednesday Thursday Friday

22 Process Improvement Scenario 4 Sharpen Strategic Location Selection for New Service Centers Service Center 5 km km 4,800 Covered Customer Area Uncovered Customer Area km 5 km 5 km 1,125 5 km

23 For Enquiry or Live Demo Visit Us at BOOTH NO. T 7