BIG DATA KICK START

Troy Christensen

December 2013

• Define the Target Operating Model 1

• Develop Implementation Scope and Approach 2

• Progress Key Data Management Capabilities 3

• Transition to Operate 4

Big Data Roadmap

Target Data Operating Model

the Data Solution

Target Operating Model

Begin with the End in Mind

Data Solution

Define a content based scope

then standardize to common process requirements

Data Processes

1. Manage Data Strategy, Performance & Capability: Define and execute data management strategy, including governance, performance management and organizational discipline.

2. Manage Data Issues & Risks: Identify, mitigate, and resolve issues and Risks within the Data Solution. This process refers only to the data related issues and risks which have an impact on the Data Solution (other issues are managed through normal issue resolution process.

3. Manage Data Objects: Defines Data Objects, Manages Metrics, Manages Policies, Manages Metadata, Manages Data Attributes and Manages Data Standards.

4. Manage Data Template: Enables data to be bulk loaded into the Data Solution in compliance with defined standards.

5. Manage Data Content: This process manages data content via agreed on hierarchy (e.g. global vs. regional)

6. Manage Data Audit: Enables the data audit requirements of the business processes to be traced and analyzed

7. Monitor Data Quality: Enables the data quality of each data object to be managed to requirements of the collective business processes

Data Roles

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Today's Date: 18-Sep-13

REGIONAL ROLES

REGION P2P REGION O2C REGION R2R REGION PROCESS

DRAFT DATA MANAGEMENT ORGANIZATION

Role Assignments

USERS

SUPER USERS

PROCESS & DATA

OWNERDATA STEWARD

P2P PROCESS & DATA

OWNERP2P DATA STEWARD

O2C PROCESS & DATA

OWNERO2C DATA STEWARD

R2R PROCESS & DATA

OWNERR2R DATA STEWARD

IT&S ROLES

GLOBAL SERVICE MANAGER SERVICE DELIVERY MANAGER USER MANAGEMENT SUPPORT LEVEL, 1-3 APPLICATION SMES FUNCTIONAL SMES

SEGMENT ROLES

SEGMENT P2P SEGMENT O2C SEGMENT R2R SEGMENT PROCESS

P2P PROCESS & DATA

OWNERP2P PROCESS LEAD

O2C PROCESS & DATA

OWNERO2C PROCESS LEAD

R2R PROCESS & DATA

OWNERR2R PROCESS LEAD

PROCESS & DATA

OWNERPROCESS LEAD

DATA STEWARDP2P DATA STEWARDP2P VENDOR

RELATIONSHIP MGMTO2C DATA STEWARD R2R DATA STEWARD

GLOBAL DATA

SPECIALIST

GLOBAL P2P

PROCESS OWNER

GLOBAL P2P

SPECIALIST

GLOBAL O2C

PROCESS OWNER

GBS ROLES

GLOBAL O2C

SPECIALIST

GLOBAL R2R

PROCESS OWNER

GLOBAL R2R

SPECIALIST

GLOBAL PROCESS

OWNER

GLOBAL

SPECIALIST

GLOBAL DATA

DIRECTOR

Without organizational roles, data process won’t execute

Data Tools

Big Data tools will likely enable the target operating model, but we simply can’t define any tool

requirements until the operating model requirements are landed

Big Data Implementation

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Implementation involves four objectives: • Standardization – The standards, policies or rules that define the

target state (what good data looks like) are gathered and documented. Standards are then mapped to the business processes that use them.

• Harmonization – Data sources are documented

and each data source is mapped to the data standard. Scorecards are developed to compare data sets from disparate sources against common standards.

• Alignment – Scorecards are analyzed to identify gaps in current data source against common data standards. Gaps are prioritized into remediation projects and funding secured.

• Operate – Harmonization and alignment activities are periodically repeated as data standards continue to mature. Tools are implemented to improve SLA’s and reduce costs.

Harmonize

Align

Operate

Standardize

Key - Defining the Start Point

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Pilot an Achievable Objective • Data is a critical input to every business

process we execute daily.

• We can ensure it does not cause our business processes to fail by managing our core data to specific standards.

• We can demonstrate its value by deploying a

proven model to a limited set of data subject areas.

Land Initial Scope Vendor data is a good data subject area candidate to pilot a data management operating model because: • Relevancy – Vendor data impacts many

business processes across many different segments and functions. There is value to ensure it does not impede someone’s ability to quickly procure the right materials or services from the most cost effective source.

• Reusability – Vendor data standards and data quality scorecards already exist, so we can quantify and address the vendor data quality gap with fewer resources.

Data silos weren’t built overnight and they won’t be removed overnight, but common

processes won’t function properly until data silos are removed.

Big Data Dependency Matrix

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Maturity

Level

Common

Process

Data

Requirements

Organization/

Governance

Data Tools Architecture

Level 1 Common

Business

Processes

Documented

Data Process

Requirements

Defined

Common Data

Maturity Model

Landed

Data Tool

Requirements

Documented

Data Flows

Diagrammed

Level 2 Data Scope

Defined

Data

Standards

Common Data

Organization

Landed

Tool Strategy

Defined

Data

Architecture

Strategy

Defined

Level 3

Data Sustain

and Support

Processes

Defined

Data Metrics

and

Scorecards

Common Data

Governance

Landed

Common Data

Tool Gaps

Identified

Data

Architecture

Gaps

Identified

Level 4 Data Sustain

and Support

Processes

Integrated

Data Quality

Performance

Functional Data

Management

Capabilities

Developed

Common Data

Tool

Roadmap

Developed

Data

Architecture

Roadmap

Developed

Level 5

Common

Business

Processes

Optimized

Data Quality

Enforcement

Functional Data

Management

Capabilities

Optimized

Common Data

Tool Set

Implemented

Common Data

Architecture

Implemented

Dependencies generally run left to right, top to bottom. To understand the requirements for a common data architecture and tool set, we first need to develop common data processes,

data standards and governance capabilities.

Big Data Activity Sets

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Standardization • Gather data standards across domain

• Determine common format for capturing global data standards

• Document data standards using common formatting

• Share with segment data stewards and gain consensus on group standards

• Map standards to global process

Harmonization • Identify all data sources

• Map data sources to group data standards

• Develop scorecard to measure data quality gaps

• Run scorecards against each data source

Alignment • Identify data quality gaps from each scorecard

• Use global process mapping to assess impact and risks of each gap

• Prioritize remediation activities

• Build scorecard to describe impact of improved vendor data quality

• Secure funding, execute remediation and monitor progress

Operate • Monitor benefits and costs

• Develop data management strategy

• Identify cost effective data management tools

• Prioritize implementation across remaining data objects

Transition to Operate

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Measure impact of the pilot program in terms of the business case. For example, for vendors, metrics could be: Safety and Cost Drivers

• Invalid D&B Numbers result in orders from vendors who are not authorized to do business with company

• Incorrect shipping addresses cause delays in receipt of supplies that results in increased down or idle time

Safety Drivers

• Incorrect vendor data result in non-OEM part being shipped or services in lieu of OEM parts or services

Cost Drivers

• Missing vendor data result in increased invoice cycle time resulting in additional processing costs and inability to realize payment discounts.

• Maintenance of large number of inactive vendors results in erroneous analytic reports and higher maintenance costs.

• Improper vendor addresses lead to incorrect tax calculations and collections

• Incomplete vendor data requires additional resources to maintain and process orders and invoices.

• Poor data quality in vendor names results inability to search for and find appropriate vendor in a pick list.

• Invalid contact information results in inability to contact vendor to correct errors which results in processing delays

Summary - Big Data Kick Off Milestones

• Define the Target Operating Model – Solution – Process – Roles – Tools

• Develop Implementation Scope and Approach – Standardize – Harmonize – Align – Operate

• Progress Key Data Management Capability – Navigate the Dependency Matrix – Execute the Activity Set

• Transition to Operate – Ensure Big Data benefits are measurable and traceable

Final Take Away

For most companies, kicking off a Big Data initiative is really about developing foundational data management capabilities

(standardization, harmonization, alignment and operations) across respective business lines.

Unless these core foundational capabilities exist or are developed as

part of the program, Big Data can’t deliver its full potential.

QUESTIONS?