Apache-PwC Controlling Your Master Data Through Data Governance Global Material Master Data Management Project

Apache-PwC

Controlling Your Master Data Through Data Governance

Global Material Master Data Management Project

Andre Siaw, PwC

Andrew Mullinax, Apache Corporation

Master

Data

•

Introduction

•

Material Master Overview

•

Governance

•

Process

•

Technology

•

Implementation

•

People & Change Management

•

Key Learnings

•

Leading Practices

•

Questions/Contact

Agenda

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PwC refers to the United States member firm, & may sometimes refer to the PwC network. Each member firm is a separate legal entity. Please see www.pwc.com/structure for further details.

• Founded in 1954, Apache is one of the world’s top independent oil & gas exploration & production companies with operations in the United States, Canada, the North Sea, Egypt, Australia & Argentina. • 2012 Total Assets: $61 billion

• 2012 Proven Reserves: 3 billion barrels of oil equivalent (boe) • 2012 Production: 779,000 boe per day

• 2012 Revenue: $17 billion • Total Acreage: 41 million • Employees: 6,000

• Apache’s portfolio strategy has enabled the company to grow throughout commodity cycles, delivering strong financial results consistently through an unrelenting focus on rates of return & benefiting from a high-margin product mix.

• Strong culture reinforced by centralized management & incentive systems, decentralized decision-making, cost-control focus & creative application of technology

Introduction to Apache

Introduction

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Apache SAP Background

• SAP MM went live in 2008

• Limited & late focus on cleansing data

• Ineffective data cleansing due to lack of data

standards

Implementation Outcomes

• Poor data quality generated issues around inventory & reporting

• Procurement users dissatisfied with results of data quality & processes

• Assessment conducted in 2010 to determine the extent of the issue

• Assessment recommendations:

• Establish data governance set-up

• Re-cleanse material master data to common, global standards

• Implement an MDM tool to help sustain ongoing data quality & standards

Material master records were found to contain significant quality issues which impaired the ability to locate

inventory items, limited spend visibility, increased inventory costs & reduced process efficiencies

• Significant use of free form text results in: • Higher costs (off-contract buying) • Higher recycle (at buyer & AP levels)

• Limited ability to analyze & aggregate spend • Duplication of data & effort across groups

• Lack of visibility impacts ability to optimize inventory • Inefficient tools lead to backlog in MM maintenance • Impacts reliability, supply chain optimization &

process automation

Materials Data Management Prior State Business Impacts

• Poor data quality, both incomplete & obsolete data • Ineffective search impairs ability to find items • No global catalog or standard taxonomy

• No version of truth

• No single owner or data governance organization

(multiple regional groups) with common processes or standards

• Multiple siloed technologies, with manual controls & processes, limiting ability to enforce standards • Limited capability to monitor & sustain data quality

Project Objective

• Consolidate & standardize materials repository to represent a 'single source of truth' across enterprise

• Standardize & improve data management processes through automation & integration of common workflows • Cleanse data to Apache standards & enhance taxonomy

• Implement Technology & integrate with SAP to manage & sustain data quality

• Deploy the MDM tool to Business Units enabling them to create & change standard material masters

Project Value Drivers

Strategy

Strategy is two-fold:

• Legacy Data Cleansing: Cleanse to common standards through application of governance processes, policies & tools

• Sustaining Solution: Implement MDM tool for creating new materials

Project Scope

• Data Governance supported by MDM Tool (sparesFinder) • Data Cleansing & Data Quality Management

• Materials: OCTG, Controllable equipment & MRO/Consumables/Other materials

• Geographies: Central, Permian, Gulf of Mexico, Canada, North Sea, Argentina, Australia, Apache Egypt, Qarun, Khalda, Beryl

Material Master Overview

“Material Master” is a repository of item records with specific attributes that provide critical data on materials &

equipment that are purchased & inventoried

Material

Search Master Data

Request Master Data Request Material Master Records Bill of Materials Purchase Requisition Purchase Order (PO) Goods

Receipt Invoice Receipt Payables (A/P) Plant

Maintenance Work Order

• General material data • Material description • Material purchasing data • Accounting data

• Plant specific data

Spend Analysis Business Warehouse

ERP

Outline Agreement Manual requisition Inventory Management Pr o cesses/ tr an sac tion s w h er e M M ’s ar e u sed

Cleansing without Governance leads to data quality decay over time

Why We Need Data Governance

• Data Governance is critical for maintaining data quality

• Data cleansing alone will improve data quality, but over time data quality deteriorates & additional resources & effort are required to restore data quality to acceptable standards

Time

Data Quality

Data Cleansing alone

Data Cleansing with Governance Data Cleansing alone

Data Cleansing with Governance

Governance

• Data Governance is a framework of capabilities which when executed together, help maintain data that is accurate & consistent to meet Apache’s business requirements

• Governance is fundamental to MDM – cleansing data & establishing sustaining solution to keep data clean

Policy: guidelines & principles to enforce data governance

Processes: Guidelines on how data policies are created & implemented

Governance Metrics:

measures to monitor performance of data

Key Enablers to Enforce Standards & Sustain Quality

Technology: Scalable tools to enable

governance capabilities

Taxonomy & Dictionary:

standardization & enhancement of material description

Data

Governance

Processes Technology Governance Metrics Policies, Principles & Standards Taxonomy & Dictionary

Taxonomy is a hierarchical structure to organize materials. Taxonomy facilitates search by users, sourcing spend analysis, & data exchange with suppliers. Data Dictionaries like PIDX, SMD, etc. can be leveraged to realize a taxonomy by providing standard material description elements like noun, modifier & attributes.

BEARING, BALL BEARING Attributes TYPE ROW INSIDE DIAMETER OUTSIDE DIAMETER WIDTH SIZE SERIES STYLE RADIAL CLEARANCE CAGE MATERIAL ADDITIONAL DETAIL … Attributes Level 1 Level 2 / Class

SERIES VALUES EXTRA LIGHT HEAVY LIGHT MEDIUM … Values Noun Noun, Modifier

Upper Level Taxonomy Lower Level Taxonomy

What is Taxonomy?

Value Add

Uncleansed Description

Cleansed Description

Example 1 • Structured data created from unstructured data

• Complete & enriched data created from incomplete data

Short Description:

VALVE,GATE:WDG,6IN,CL 600,RF,22IN FACE-~ Long Description:

VALVE,GATE:WEDGE,6IN,CLASS 600,RF,22IN FACE TO FACE, FULL

BORE,BEVELLED GEAR OPERATED,CARBON STEEL BODY,ASTM A105 OR ASTM

A216 GR WCB,13CR/STELLITE

TRIM,MANUFACTURED TO ANSI/ASME B16.5,,STELITE FACED SEAT,WEDGE,BACKSEAT & BACKSEAT BUSH

Short Description: VLV,GATE:WEDGE,6in,600lb,RF FLANGED,CS,> Long Description: VALVE, GATE TYPE: WEDGE VALVE SIZE: 6in

PRESSURE RATING: 600lb CONNECTION TYPE: RF FLANGED BODY MATERIAL: CARBON STEEL

MATERIAL SPECIFICATION: ASTM A105/ASTM

A216

GRADE: WCB

SEAT MATERIAL: STELLITE OPERATOR: BEVEL GEAR STANDARD: ASME B16.5

CONSTRUCTION: 22IN FACE – FACE CONSTRUCTION: 13CR STELLITE TRIM

Outcome of Structured Data

NMA (Noun Modifier Attribute) Structure – ‘fundamental concept’ for building Taxonomy

While data can be effectively cleaned, ongoing sustainment requires tools to enforce standards & monitor quality

Continuously monitor data quality standards & provide metrics for ensuring sustainability

Maintain business & technical processes to support data quality Detect, correct, enrich & validate incorrect data to improve data quality Establish & control data

quality across enterprise through governance & taxonomy Monitor Quality Process Enhance-ment Cleanse & Validate Define Standards

Data Quality Initialization

Data Quality Sustainment

1 2

3 4

Sustainment Model

Governance Council

• Collaboration of subject matter experts to manage & enhance dictionary • All taxonomy/dictionary issues will be reviewed before changes are

finalized in the system

• Council will be coordinated & facilitated by Corporate MMDM team

• Strategic management of material master data

• BU engagement on important taxonomy/dictionary issues

• BU consensus on standardization of materials described differently across various BU’s

• Business Unit Representatives

• Houston Material Master Data Governance Team • Requester other than the BU Representative

What is Governance

Council?

Benefits

Who are the members?

Legacy Operating Model

SAP Houston Master Data Team Materials Control Staff Engineers Business Unit Purchasing Staff

Creators of requests had least knowledge of materials

No 24/7 availability

Delays due to time zones between BU’s Slower processing times due to back & forth between Houston & the BU

Domain expertise is decentralized Creators are field users with the most knowledge of materials

More users improve material description with continued deployment

Tool is used to enable ongoing Governance

New Operating Model

Houston Master Data Team BU 6 Approver BU 5 Approver BU 1 Approver BU 6 Catalogers BU 1 BU 6 BU 2 Approver BU 3 Approver BU 4 Approver BU 5 Catalogers BU 4 Catalogers BU 1 Catalogers BU 2 Catalogers BU 3 Catalogers Field users Field users Field users Field users Field users Field users Master Data Management Tool

Houston Master Data Team

(Cataloger)

BU Approver

BU Cataloger

Role

Responsibilities

• Domain expertise for ongoing Data Governance, Taxonomy & Dictionary • Data Quality & QC Processes

• Manage Governance tool • Technical errors & Helpdesk

• Approve standard material master creation requests submitted by BU requesters • Approve non-standard material master & taxonomy requests to be sent to Houston

Master Data team for processing

• Participate in Governance council discussion • Participate in QC processes & rectifications

• Create standard material master creation requests submitted by fields users • Update local plant & warehouse level changes in the tool

In order to sustain the material master data management initiative, the following organization structure has been established:

Technology Defined

Effective master data management requires both MDM tool & other supporting components working together as

part of a comprehensive solution

Component Description

MDM Tool

• Manages master data through its lifecycle

• Provides a “single version of truth” for accurate & efficient decision making • Facilitates maintenance & syndication of an enterprise taxonomy

• Provides enhanced front-end data validation & search capability

ERP • Contains full material master data to enable transactions

• Serves as source of truth for local data attributes Data Quality

Management Tool

• Provides capability to identify & correct errors & inconsistencies in data by applying pre-defined business rules & data standards

• Enables processes such as monitoring, profiling, cleansing, mapping & enrichment Workflow

Management Tool

• Enables the passage of information, documents (e.g. requests), & tasks (e.g. approvals) between users & systems to facilitate accuracy, accountability & process automation

Integration • Facilitates automation & enhances user experience Monitoring &

Tool – Enables Process & Controls

• MDM tool supported by Governance & Processes is the sustaining solution • Entry Point to create, maintain & search all material masters

• Enterprise-wide solution to sustain data quality • Maintains global dictionary – ‘single source of truth’ • Single common approach – standard rules apply to all • 24/7 availability

• Advanced search capability • Prevents creation of duplicates • Helps leverage existing inventory

• Phased deployment to field users with multiple levels of approval • All users have role based access in the tool

• Users focused among Supply Chain, Plant Maintenance & IT functions • Automated workflow can be configured to Business Unit requirements

MDM tool

• Two fundamentally different records can be created in the tool • Attributed description – can be created by business unit users • Catalog (OEM) description – can only be created by Houston • Menu driven; pre-populated list of NMA

• Manages taxonomy – NMA

• Manages Global & Local level ERP data • Controls Units of Measure

• Controls Attributes values

Benefits User Access Features What is created? SparesFinder MDM Tool

Project Streams

• Processes that are executed concurrently as part of project implementation

• Robust, repeatable & scalable processes were built & tested in Pilot Phase with a small number of critical materials • Repeatable in cases of new acquisitions

Cleansing & Cutover

• Cleansing of materials by MDM vendor • Apache review & acceptance

• Cleansed data upload from MDM tool to SAP

Integration & Tool Deployment

• Seamless availability in SAP of data created in MDM tool

• Houston Deployment

• Pilot rollout to first Business Unit • Deployment to other BU’s

Cleansing & Cutover Process

Cleanse legacy data to global standards through application of governance processes & tools

• Iterative process

• Regional review & Apache acceptance of data cleansed by MDM vendor

Cleansing by MDM Vendor Review by Apache Regions Houston Acceptance SAP Raw Legacy Data

Time

Design, Build & Test Pilot Deployment (Houston) Pilot Deployment to Business Unit 1 Deployment to BU 2 Deployment to BU 3 Deployment to BU 4 Deployment to BU 5 Deployment to BU 6

Deployment Plan

Concurrent streams

Legend: Design, Build & Test Pilot Deployment to Houston Pilot BU Deployment Tool Deployment to Global BU’s

• Phased Deployment • Train the trainer strategy

• Pilot implementation of MDM tool in Houston before rollout to BU's • Pilot rollout to first BU – implement lessons learned

Integrated Change Readiness Approach

• Change impact analysis • End user analysis • Leadership alignment • Super user & change agent

networks

• Continuous stakeholder engagement to focus change support

Successful MDM Deployment

• Communications strategy & plan

• Targeted messaging

• Mix of mediums including in person, phone, web, email, & video

• Regular meetings with BU’s & stakeholders

• Regular project update communications • Publication of Project

Advisories to communicate important issues

• Training strategy & plan • Training materials that are

clear & simple • Reusable content for

retraining & for new users • Timely training delivery • Step by step training manual,

training exercises & presentations

• Role-based curriculums

Four Elements of Change Readiness

Change

Management Communications Training Deployment

• Timely training delivery by mix of MDM experts & super users • Tracking & reporting

• On-site support by project team, as needed

• Flexible delivery that can be modified to local & region needs

• Mix of mediums including in person, phone, web, email, & video training to suit regional requirements

• Capture lessons learned for future initiatives

Raise user awareness & engagement while

reducing need for alternate or workaround systems

Effective Change Management involves an integrated approach across Change, Communications, Training, &

Key Lessons Learned

 Governance

• Dictionary needs to be tailored to what is important to your specific industry – ‘Off the Shelf’ dictionaries require substantial customization

• Focus on governance & dictionary & ensure that rules are in place before cleansing

• Allocate substantial time & resources to dictionary, as this is the foundation for data cleansing  Cleansing

• Only cleanse records that are really critical

• Cleansing is not around industry specific materials - domain experts are needed to enhance cleansing

• Engage Business Unit resources early  Technology

Leading Practices

 Ensure critical data taxonomy & governance is finalized early

 Establish repeatable & scalable processes

 Establish a Governance Council

• Strategic management & enhancement of material master data by leveraging subject matter expertise • Achieves BU engagement on important taxonomy/dictionary issues

• Achieves BU consensus on standardization of materials described differently across various BU’s

 Deploy tool to Business Units

• Data creators in the field have the most knowledge of materials • Ensures 24/7 availability

• Facilitates faster & more accurate creation of standard materials

 Central MDM team to control non-standard material creations & global changes

 Establish Quality Control & Governance Processes to ensure data integrity & sustain data quality

standards

Questions

Andre H Siaw

Advisory Director

PwC

[email protected]

Andrew Mullinax

Manager, Supply Chain Technologies

Corporate Supply Chain

Apache Corporation

sparesFinder Materials MDM suite

Masterpiece

Data Cleaning & Project Control

Gatekeeper

Governance & Workflow

Foundation module –User Control, Integration, System Admin

Common taxonomy – ISO 22745 compliant

Term

Description

Material Master A repository of item records with specific attributes that are fundamental to purchasing, inventory management, spend analysis & operations

Data Governance A framework of capabilities, which when executed together, ensure that data is accurate & consistent to meet business needs & objectives.

Data Quality (DQ) Management

DQ Management is the capability to provide reliable data that satisfies the business functions & technical requirements of the enterprise.

Data Cleansing The process of detecting & correcting erroneous data & data anomalies both within & across the system. Data cleansing can take place in both real-time as data is entered by automated tools or afterwards as part of a Data Cleansing initiative.

Dictionary Dictionary provides standard material description elements like Noun/Modifier, attributes, Synonyms,

Language & UoM.

Taxonomy A hierarchical structure to organize materials. It facilitates search by users, sourcing spend analysis, & data exchange with suppliers. Data Dictionaries can be leveraged to realize a taxonomy by providing standard material description elements like noun, modifier & attributes.

MMDM Implementation Benefits

Data Quality Benefits

• Structured data & enhanced taxonomy/dictionary by implementing Data Cleansing & Governance processes • Elimination of duplication reduces duplicated inventory,

assisting the corporate goal

• A ‘single source of truth’ for material master records

• Consolidated & standardized material master records across all regions

• Increased material visibility

Controls Benefits

• Inventory Control & Management • Improved reporting & monitoring

• Global reports • Global agreements • Spend Analytics

• Other Analytics - KPIs

Efficiency Benefits

• Increased regional involvement & self-service through deployment of MDM tool to BU’s

• Acceleration of processing times – BU’s take the same time as before but make updates directly in the system, hence eliminating time spent corresponding with Houston cataloger

• Corporate staff able to better utilize time taken for data entry tasks for data quality & content management

• 24/7 availability • Process automation

Cost/Commercial Benefits

• Optimize dedicated manpower at Corporate as a result of BU deployment, hence saving client resources

• Reduced spend by leveraging existing inventory • Reduced inventory holding costs

• SG&A cost reduction

• More readily integrate with new acquisitions/assets in a controlled environment i.e. cleansed, structured environment

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