Sustaining Data Quality May 21, 2003 Denise Sanders and James Ruan.

Sustaining Data Quality

May 21, 2003Denise Sanders and James Ruan

Impact of Data Quality

Source: PricewaterhouseCoopers Data Management Survey

¾ of companies surveyed have experienced problems as a result of data quality issues¾ of companies surveyed have experienced problems as a result of data quality issues

Data quality problems cost U.S. businesses more than $600 billion a year. - Data Warehouse Institution (TDWI)

“…biggest issue is

that data inaccuracy

causes hard dollar

loss, failed

deliveries, missed

corporate actions,

or erroneous

decisions.”

(US Top 500)

24%

31%

24%

34%

58%

25%

0%

25%

50%

75%

Extra costs toprepare

reconcilliations

A delay or scrappingof a new systemimplementation

A failure to bill orcollect receivables

Inability to deliverorders or lost sales

because of incorrectstock records

Failure to meet asignificant contractual

requirement orservice levelperformance

None of these

Key Learning Points

1. Key elements of a successful data quality program

2. Data Stewardship - Best practices and concepts

3. Data quality technology solutions – the options and the use in sustaining data quality

Survey says…

We believe data quality is important– 75% of companies report significant problems, costs, or

losses as a result of poor quality data– And the same proportion said they had realized clear

commercial benefits from effective data management– Half of companies consider data management a

strategic issue

But don’t act like it – Most companies do not have a formal data strategy– Responsibility for driving data strategy lies mainly with

IT professionals Source: PricewaterhouseCoopers Data Management Survey

Elements of success

• Accountability• Strategy• Methodology• Technology

Elements of success

Accountability+ Strategy+ Methodology+ Technology_____________________= Sustainability

Elements of success


Implementing Accountability

• Identify responsibility from backroom to boardroom

• Assign the right people for the right role

• Assume responsibility at the business unit level

• Monitor the data as well as assuring the data

• Set KPIs to measure and report on behaviors

Data Stewardship: Taking responsibility for the content

and quality of a defined part of the company’s data

Best Practice DQ Roles & Responsibilities

SponsorCentre of

Excellence

CustodianConsumerProducer

Data Quality Manager

Owner

Establish a business-driven data quality management process with clearly defined roles and responsibilities

Balance short-term tactical data cleansing and data conversion efforts and long-term sustainable ongoing data quality management

Integrate data quality roles and responsibilities into the existing job functions

Business

IT

Data Quality

DQ Roles - Example

EnterpriseProject

SVP Global Supply Chain

Data Steward(s)• Customer

• Order History

Data Steward(s)• Material Master

• BOM

Demand Planning Leader

Order Management

Leader

Manufacturing Leader

Sourcing Leader

Global SC Data Management

Leader

People Leader

IT Organization

Data Management

Leader

DM Lead

DM Strategists & Architects

DQ Specialists

DQ Technical Specialists

Data Management

Group

Data Steward(s)• Vendor Master•Material Master

t r a n s i t i o n > >

Elements of success


A majority of companies do not have a formal board approved data strategy


Most important elements of organisations’ existing “data strategies” are in fact policies, not strategies


What to do now - ask some questions

• Why is data important to you?• What do you know about your

data?• What does managing data mean

to you?• What are the detail issues -

quality, information etc?• Are there regulatory

considerations?• How do I lay the foundations for

a data management culture?• What about charting progress

for change?• How do we measure up to ‘a

successful company’?

Roadmap to a Data Strategy

• Identify areas most critical to your business

• Link data to business plans to correctly prioritize

• Consider all data opportunities that touch business goals

• Understand where you are and where you want to be

• Determine the transition plan

• Measure results

Elements of success


Data Quality Process Overview

DEFINE ASSESS SUSTAIN

Meet or Exceed Quality Requirements

Below Quality Requirements

Changes to Requirements

Key Risks To Be Addressed

Quality requirements are unknown or are not being addressed

Business perceives data quality levels are higher than actuality

Source data does not meet the increased level of data quality required

•Overall data quality degrades over time•No adequate control environment in place

Data Quality Process

IMPROVE

2003 PricewaterhouseCoopers

ExampleDefine

Assess Improve

Elements of success


Data Integration Challenges

R/3

Collaborative Engineering

e-Procurement

Sourcing

ERP non-SAP

Legacy

e-Sales

ManufacturingControl

Master DataMaintenance

SCM

CRM

Sample DQ Vendors/Products

Focus Vendor/Products

Profiling Avellino’s DiscoveryEvoke’s Axio Product SuiteAscential’s MetaRecon

Matching & cleansing DataFlux’s dfPower StudioTrillium’s Data Quality SuiteAscential’s IntegrityInnovative Systems’ i/LyticsFirst Logic’s Information Quality SuiteGroup 1’s DataSight

DQ Tool Considerations

1. Preventing duplicate master data entries in both real-time and batch modes

2. Enforcing predefined data standards in production environment

3. Improving/enriching data in both production and off-production environment

4. Ability to infer rules, relationships, definition and quality of data based on an analysis of content

5. Ability for the tool to be effectively utilized by a non-technical business person

6. Integration and synchronization of data across necessary platforms and databases

7. An integrated architecture capable of encompassing such data management components as ETL, EAI and meta data management

8. International support in terms of user interface and processing capability

File-based communication with SAP

• Data Quality Analysis outside the production environment is most common and very important.

• SAP has developed a number of adapter/integration options available for SAP customers and partners that include both real-time and file-based communication

– Info at http://ifr.sap.com

SAP R/3, CRM, APO, BW, etc.

Application Layer

Customer-defined ABAP Extract/Load

programs

ALE/EDIIDocs

Files Files

Data Quality Tool/Infrastructure

SAP-supplied Load programs

(direct input and BDC)

Files

Real-time integration with SAP

Real-time integration and DQ monitoring:1. provide end-users a better mean to

proactively manage and sustain DQ, 2. enable the true ownership and

accountability at each touch point of data entry

SAP R/3, CRM, APO, BW, etc.

Application Layer

Data Quality Tool/Infrastructure

Communication Layer

Business Object RepositoryBAPI (Business Application

Programming InterfaceALE (Application Link

Enabling)

RFC (Remote Function Call)

Summary

1. Key elements of a successful data quality program– Accountability– Strategy– Methodology– Technology

2. Data Stewardship– Data Quality is not just an IT responsibility. The business users

have the most stake in data quality and need to be involved.

3. Technology– There are many Data Quality Tool Vendors, but tool expertise

is only 20% of the story. Process improvements must be made in order to maintain proper Data Quality.

Questions?

Thank you for attending!

Please remember to complete and return your evaluation form following this session.

Session Code: 2911

Sustaining Data Quality May 21, 2003 Denise Sanders and James Ruan.

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Transcript of Sustaining Data Quality May 21, 2003 Denise Sanders and James Ruan.