www.ent-vision.com

Business Transformation with Data Intelligence

• 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

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

The 1-10-100 rule is a concept used to quantify the costs of quality issues.

DATA QUALITY is not a project, it’s a Lifestyle.

Costs of Poor Address Data Quality

Administrative Costs

Labor Cost

Hidden Costs

Visible Costs

Deliveries Delayed

Data Quality Inspection Costs

Customer Services • Customer Dissatisfaction • Complaint Handling Costs • Customer Retention Costs

Management • Poor Informed Decision Making • Company Reputational Damage

Data Analytics • Inaccurate Analysis

Sales • Communication Costs • Inaccurate Sales Pipeline • Loss of Potential Revenue

Finance • Overdue Receivables • Pricing or Billing Error

Inventory Carrying Costs

Transportation & Warehousing Costs

Operations • Longer Orders Processing Cycle • Lower Competitive Advantages

Data Quality Management Cycle

Assess data

quality

Clean data

Improve business process

Monitor data

quality

1.

2.

3.

4.

Data Quality & Preparation

Customer Name AddressPostal

Code

Fullmark Pte Ltd 10 Soon Lee Rd, Fullmark Industrial Bldg, #03-02 628074

United Overseas Bank Limited 80 Raffles Place UOB Plaza 738205

AFPD Pte Ltd 10 Tampines Industrial Ave 3 Singapore 528758

Company Name Addr1 Addr2 Addr3 Zip Code

UOB Attn: Mr. Lee Kok Keong 451 Clementi Avenue 3 Tel: 91208372/63491077 S'pore 120451

Keppel Land Limited Bugis Junction Towers #15-05 230 Victoria St. Singapore 188024

FMC Technologies P/L 40 Scotts Rd, S'pore 228231 Attn: Karen Chia (IT Dept) Tel: +65 8739 1366

1. Duplicated data 2. Incomplete or missing data 3. Outdated contact information 4. Dirty data 5. Inconsistent format from multiple data sources

File 1

Common Data Challenges

File 2

Job Title • Job Level • Department

Email Domain • Valid Email • Corporate Domain

Phone Numbers • Direct Line • Mobile

Company Name Format*

Industry and Sub-Industry

Employee Size

Annual Revenue

Phone Numbers • Mainline • Fax Number

Address • Individual Segment • Full Address

Data Standardisation

Company Level Contact Level

*standardised during de-duplication

Company Name Standardisation

10

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

Formatting Company Names (English and Non-English Company Type) E.g. Indonesian companies have a ‘PT’ appended at the back of the name

Standardisation Address Fields

11

• Standardise and parse unstructured address into individual address segments.

• Combine the segmented addresses “House No. + Street + City/Country” to get precise GPS coordinates from geocoding API.

Address 1 Address 2 Address 3

The Plaza Office Tower 42nd Floor Jl. MH. Thamrin Kav. 28-30 Jakarta 10350

Jl. P. Bunaken A3, Lantai 2, Kawasan Industri Medan 3 Kota Medan, 20242, Indonesia

House No. Street Floor Building City Ward Postal

Code Country

Kav. 28-30 Jl. MH. Thamrin 42nd Floor The Plaza Office Tower Jakarta 10350

Jl. P. Bunaken A3 Lantai 2 Medan Kawasan Industri

Medan 3 20242 Indonesia

Jl. MH. Thamrin Kav. 28-30 Jakarta Latitude: -6.1969939 Longitude: 106.8204433

Jl. P. Bunaken A3 Medan Indonesia Latitude: 3.6760964 Longitude: 98.7025541

Data Cleansing Steps

Company Details

• Mainline and Fax • Address • Industry • Employee Size • Annual Revenue

Standardize data fields

Company Matching

Master Database

Standardized fields Matching priority fields Matching Confidence

Fields Merging Based on Data Priorities

Map parent /child relationships

Map Contact IDs

Company Deduplication

Merge company information

Map company records

Data Matching Methodology Natural Language Processing & Rule Based Algorithm

Exact Match

Remove Extra Space & Insert Missing Space – 2 Levels

Remove Special Characters (. , ’) – 3 Levels

Standardize Keywords (Pte. Co.,) – 7 Levels

Single & Double Letters Typo Errors

100% probability

50% probability

What’s in an Account Name?

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

Input by Sales Rep

Common Trend: Append extra account site information in the Account Name

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

Data Merging

Account Name Address Phone Number Last Modified Date

Unilever Singapore Pte Ltd 20 Pasir Panjang Road +65 6643 3000 12-Dec-2015

Unilever (SG) Pte Ltd 20 Pasir Panjang Rd, #06-22 Mapletree Business City

+65 6643 3001 25-Mar-2016

Account Name Address Phone Number

Unilever Singapore Pte Ltd 20 Pasir Panjang Rd, #06-22 Mapletree Business City

+65 6643 3000, +65 6643 3001

Sample Merging Rule: Address: Level of completeness* Phone Number: Merge all unique values

*Address Standardisation is a pre-requisite

Final Cleansed Record

Different merging treatment across all data fields Merge specific data fields in accordance with business methodology and

predefined merging rules

PROCESS IMPROVEMENTS

Master Data

Process Improvement – Scenario 1 Master Data Linking for Single View

Customer Relationship Management

(CRM)

Enterprise Resource Planning

(ERP)

Supply Chain

Management (SCM)

Marketing

Automation (MA)

Single View

Data Standardisation Data Deduplication Data Merging

Accurate Data Analysis & Reporting

Cleansed data

channeled back to

respective applications

Ent-Vision

North East Team 1

Central Team 2

• Standard Route Planning (By Zones) Handled by Team 1 & Team 2 • Dynamics Route Planning (By Proximity) Handled by Team 1 based on nearest delivery locations which is more cost-effective

Process Improvement – Scenario 2 Dynamics Route Planning

Process Improvement – Scenario 3 Optimise Sales Route Planning With GeoAnalytics

Monday Tuesday Wednesday Thursday Friday

Process Improvement – Scenario 4 Sharpen Strategic Location Selection for New Service Centers

Service Center

5 km

Covered Customer Area

Uncovered Customer Area 580

5 km

5 km

4,800 5 km

769

5 km

1,125

400

5 km

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