sustaining data quality may 21, 2003 denise sanders and james ruan
Post on 18-Dec-2015
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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_____________________= Sustainability
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 > >
A majority of companies do not have a formal board approved data strategy
Source: PricewaterhouseCoopers Data Management Survey
Most important elements of organisations’ existing “data strategies” are in fact policies, not strategies
Source: PricewaterhouseCoopers Data Management Survey
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
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
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.