EnterpriseInformation Management

CHALLENGES

CONS

OLID

ATIO

N Lack of clear standards can lead to the creation of duplicate records

CustID Name Street66899 W. Smith 100 Park Av.

CustID Name StreetWO605 Bill Smith 100 Park St.

Inability to determine the uniqueness of data is a problem that plaguesthe downstream business processes

CONS

OLID

ATIO

NInaccurate Reporting

Shipping Errors

Unnecessary Inventory

Redundant Vendor Validation

QUAL

ITY All enterprises

face common data quality challengesthat effect their ability to execute business process Consistency

Completeness

Timeliness

Accuracy

Lack of enforced data integrity will create poor quality in master records, resulting in multiplicity of negative impacts

Material Status

PurchaseFunctions

ResourceUtilization

DuplicateRecords

Poor Nomenclature

Lack Of Standards

Attribute Alignment

Supply ChainDisruptions

QUAL

ITY

GOVE

RNAN

CE Top level governance and oversight is the foundation for long term viability of systems design and integration into business strategies

Potential for security breaches Lengthy process life-cycles

Lack of ownership Inappropriate resource allocation

Without strong leadershipand corporate vision, projects will face common implementation barriers

BARR

IERS Lack of transparency

Slow accounting closures

Risk of non-compliance

FinanceMultiple systems to manage data

Impact to other IT Initiatives

High cost managing master data

IT

No consolidated view of customers

Inaccurate data causestransactions errors

SalesInefficient supplier selection

Lost opportunity for cost reduction

Lack of transparency tosupplier product information

Supply Chain

METHODOLOGY

Projects must be championed by top executives and overseen by an external group that can focus on business goals

Define Organization

Data Modeling

Roles & Ownership

Process Design

Data Standards

Data Profiling

EXECUTIVE SPONSOR

EDM LEADERSHIP TEAM

OVERSIGHT COMMITTEEDATA STEWARD

DATA STEWARD

DATA SPECIALIST

DATA SPECIALIST

DOMAIN LEAD

DOMAIN LEAD

Data modelling is frequently an imperfect mix of systems and sources. Focus must be maintained on key enterprise level attributes.

Define Organization

Data Modeling

Roles & Ownership

Process Design

Data Standards

Data Profiling

SAP ERP

ENTERPRISE LEVEL

ATTRIBUTES

LEGACYNON-SAPERP

Shared across key processes High risk data quality

Enterprise level attributes with the most impact share common characteristics

Source of competitive advantageUsed to calculate KPI

Define Organization

Data Modeling

Roles & Ownership

Process Design

Data Standards

Data Profiling

Define Organization

Data Modeling

Roles & Ownership

Process Design

Data Standards

Data Profiling

BasicData

BankInfo

SalesArea

CompanyCode

ShippingBilling Partner Functions

Role design should support efficient business operations

GLOBAL DATA STEWARD

GLOBALDATA

STEWARD

REGIONAL DATA STEWARD

FINANCEDATA STEWARD

BUSINESSDATA SPECIALIST

BUSINESSDATA SPECIALIST

BUSINESSDATA SPECIALIST

SALES GLOBAL DATA STEWARD

REGIONALDATA STEWARD

Define Organization

Data Modeling

Roles & Ownership

Process Design

Data Standards

Data Profiling

Request

LocateDuplicates

Enter Basic Data

Extendto Company

Code

PartnerFunctions

Shipping

Enter BankInfo

Extend to Sales

BUSINESSDATA SPECIALIST

FINANCE

Defining roles & ownership ensures a simplified process design

Clearly defining all processes in the scope will simplify the more complex workflows

Define Organization

Data Modeling

Roles & Ownership

Process Design

Data Standards

Data Profiling

CustomerProspect

CustomerCreate

ExtendCustomer

GLOBALDATA STEWARD

BUSINESSDATA SPECIALIST

FINANCE REGIONALDATA STEWARD

Search for DuplicateEnter Basic DataAssign Codes

Assign Sales Org, Distribution Channel, Division

Banking InfoAssign Partner FunctionsAssign Shipping InfoAssign Billing Info

BASIC DATA

By defining the organization, roles, and ownership prior to defining data standards, you can avoid disputes

Define Organization

Data Modeling

Roles & Ownership

Process Design

Data Standards

Data Profiling

Customer < Name >

BASIC DATA

Customer < City >

SALES DATA

Customer < Incoterms >

S1

S2

S3

Standards + OwnershipFunctional Area

DOMAIN LEAD

DOMAIN LEAD

REGIONAL DATA STEWARD

Data profilingis a quantitative method of measuring the results of implementing data governance

Define Organization

Data Modeling

Roles & Ownership

Process Design

Data Standards

Data Profiling

BEFORE PROJECT

AFTER PROJECT

Q1 Q2

25%

DATA

ACC

URAC

Y

50%

75%

100%

PROCESS CONSOLIDATION QUALITY

EDM Leadership Team develops data standards

Oversight Committeedevelops KPI’s

Sample data and measure the KPI’s against standards

Use metric to measure success

Ready. Set. Go Live.