Managing Data Quality Webinar Summary

© 2011 SAP AG. All rights reserved. 1
Scott Barrett
Senior Director │ Information Management │ SAP EMEA
Return on Information:
Managing Data Quality
25th September 2013
Welcome to the ‘Return on Information’ webinar series dedicated to Information Management
Solutions by SAP. This is the first of four webinars, the following webinars will be on:
9th Oct:
Managing Master Data
23rd Oct:
Managing Content
6th Nov:
Managing Information Lifecycles
Register now › bit.ly/1b26WRY

What is Information Governance?
Information Governance
A discipline that includes people, processes, policies, and metrics for the
oversight of enterprise information to improve the business value
High Value Information:
Optimized Business Processes
Smarter Business Analytics
Timely Mergers and Acquisitions
Compliance with Laws and
Regulations
ProcessPeople
Policies &
Standards
Metrics

Why Should You Care About Information Governance?
SAP Solutions are uniquely integrated with SAP Business Suite
and SAP BusinessObjects BI solutions
Utilize entire EIM stack by providing a complete solution
that can address needs at any stage of their information governance program
Talk to the business by discussing business priorities
and how high-quality information can help them
Elevate conversation above technology discussion by
showing how SAP can support information governance
1
2
3
4

What Aspects of Information Can Be Governed?
People
 Information ownership and accountability
 Information access (who, when, where, what)
Process
 Data handling (creation, updating, deleting)
 Information storing (archiving, security)
Metrics
 Data quality levels
 Reporting (who, when, what)
Policies and Standards
 Data definitions
 Allowable values
 Information architecture
Begin with the business priority that is enabled most by quality information.

Information Governance is a Key Pillar of the Global
Transformation Program Business Model For Vodafone

Information Governance and MDM
Transforming the Data Organization at Vodafone
 Single governance process for
management of SCM, Finance
and HR master data
 Creation of a data definition for each
master data object
 Enforcement of data policies and
standards
 Centralization of data management to
Global Shared Services Organization
 Establishment of Data Review Board
 Transformation from free text
purchasing to catalogues

Information Governance is a Best Practice Discipline
Required for Enterprise Information Management
Data Migration
Data Integration for
Data Warehousing
Master Data Management
Data Quality Management
A B
Before After
A
B
C
D
Mergers & Acquisitions
Business Analytics
Business Process Efficiency
Compliance
EIM Initiatives (Examples)Business Goals
InformationGovernance

SAP Information Governance Solutions
Maximize the value of your enterprise information
Easier information
stewardship
Governance
analytics
Business and
IT collaboration
Embedded in the
business process
Governance
workflow
Enrich business process with
enterprise content
Optimized
data quality
Continuous
monitoring
Unlock insights from
unstructured data
Empower the Business Govern In Process Trust Your Information

Govern Information in Process from the Point of
Information Creation to Consumption
information
information
Systems of Record
3rd Party
Systems
SAP
Business
Suite
information
information
Data
Warehouse
Systems of
Analysis
Employees
Other
Systems
Customers
Other
Systems
Is it correct
and valid?
Does it meet
standards?
Is it a new
record?
Active Information
Governance
Is it correct
and valid?
Does it meet
standards?
Is it a new
record?
Passive
Information
Governance
information
information
Information Creation Information Consumption

information
information
Govern Information in Process with SAP Solutions for
Enterprise Information Management
Systems of Record
Non SAP
Systems
SAP
Business
Suite
information
informationx
information
information
Data
Warehouse
Systems of
Analysis
Employees
Clean data at point
of user entry with
SBOP DQM for
SAP ERP/CRM
Other
Systems
Clean and check
for duplicates with
SBOP DS
Create global
master data with
SAP MDG
Customers
Other
Systems
SBOP DQM for
Oracle or
Informatica
SAP NW MDM
SBOP DS
Measure and
monitor data
quality with
SBOP
Information
Steward
Clean, match, and
integrate data with
SBOP DS
Create global
master data with
SAP NW MDM
Legend:
SBOP = SAP BusinessObjects
DS = Data Services
DQM = Data Quality
Management
NW = NetWeaver
MDG = Master Data Governance
SAP Solutions
Information Creation Information Consumption

Manage
Cleanse
Create
Monitor Integrate
Optimize Quality and Consistency of Information with
Marketing Leading Solutions for Data Quality and MDM
Information
Governance
Rated as a MARKET
LEADER IN DATA
QUALITY AND DATA
INTEGRATION by Gartner,
Forrester, and TDWI
SAP customers
KRAFT FOODS
INC., Gartner MDM
Excellence Award
2009, Lexmark
International 2011

Suppliers End ConsumerRetailerDistributorWarehousePlant
Social media data for
segmentation,
sentiment, behavior information
Geo spatial data identifies best
routes/ number of trucks/timing
Warehouses use bar code data
to speed shipping
Plant operators use carbon
input data to identify
compliance
Manage suppliers relationships to
optimize purchasing
Decrease
COGS
Decrease days
sales outstanding
Increase promotion
effectiveness
Decrease compliance issues Decrease time to delivery
Benefits of End-to-End Information Governance
throughout the Business Process

Considering what the Experts Say
Lower Profits
Poor Customer
Relations
Low Productivity
Average organization loses $8.2 million annually
through poor data quality.
Gartner
“
“
55% of all CRM projects failed to meet software
customers' expectations. Poor customer data is
one of the biggest factors.
Gartner
“
“
50% to 70% of ERP implementations are
reported as “challenged” in part due to data
integrity and/or data accuracy problems.
=Adaptive Growth, Inc.
“
“

How does Poor Data Quality Impact Business Process?
Problems experienced when data quality
practices are not followed
 Difficult to determine the right recipients for
marketing campaigns
 Inaccurate order information causes delayed or lost
shipments and lower customer satisfaction
 Sales representatives are not able to identify relevant
accounts
 Costs are high due to account duplications, while
response rates are low
 Potential customers are annoyed by redundant mails,
e-mails and phone calls
 Reporting uses wrong data and this leads to wrong
conclusions/decisions

What Kind of Data Are We Dealing with?
Materials/
Products
Customers
Financial
Business Partners
Suppliers
Distributors
Retailers
“What kind of data is most susceptible to data quality problems?”
Enterprise
Information

What Are the Sources of Bad Data Problems?
Enterprise
Information
Employee
Data Entry
Customer
Self-
Service
Data
Migration
Projects
IT
Application
Updates
Purchased
or Rented
External
Data

People & Process Maturity
ValueBuilding a Roadmap for Data Quality is Key for Success
1. Data
READINESS
4. Data
GOVERNANCE
Understand what data
assets you have and how
they are being used
Deliver trusted information
repeatable and reliably at
the right form, to the right
place at the right time
2. Data
INTEGRATION &
CLEANSING
3. Data
CONSOLIDATION
Understand
Govern
Consolidate
Understand
Consolidate
Understand Understand
Consolidate diverse
master data landscapes
and increase trust and
reliability in information
Technology enabling
people to implement a
repeatable process to
manage the use, quality
and lifecycle of
information
Cleanse Cleanse Cleanse

Data Quality Provides Value Throughout
Portfolio maps to the People and Process Maturity
END-TO-END Data
Management
Full Enterprise
COVERAGE
1. Data
READINESS
4. Data
GOVERNANCE
2. Data
INTEGRATION &
CLEANSING
3. Data
CONSOLIDATION
Information
Steward
Enterprise MDM
MDM
Information
Steward
MDM
Information
Steward
Information
Steward
Data Quality Data Quality Data Quality
People & Process Maturity
Value

The Data Quality Framework
Continuous
Monitoring CONTINUOUS
MONITORING
MEASURE
ANALYZE
PARSE
STANDARDIZE
CORRECT
ENHANCE
MATCH
CONSOLIDATE
Data
Assessment
Enhance
Data
Cleansing
Match &
Consolidate
YOUR DATA

Gartner Magic Quadrant for Data Quality Tools
www.sap.com/campaign/na/usa/Gartner_Data_Quality/index.html
SAP ranked as a LEADER
in the
“Gartner Magic Quadrant
for Data Quality Tools”
for the 7th consecutive year.

Goal: Achieving Pristine Data Quality with SAP
Bob oldstead
175 Riviington Ave suite 2
Manhatten, new yourk
10002
INPUT
PARSE1
First Name: Bob
Last Name: Oldstead
AddressL1: 175 Rivington
Ave AddressL2: Suite 2
City: Manhattan
State: New York
Zip Code: 10002
CORRECT3
First Name: Robert
MiddleName: E
Last Name: Oldstead
AddressL1: 175 Rivington Ave
AddressL2: Suite 2
City: Manhattan
State: New York
Zip Code: 10002
Phone: (847) 442-5555
Email: robo@tcabuilders.com
ENHANCE
First Name: Robert
MiddleName: E
Last Name: Oldstead
AddressL1: 175 Rivington Ave AddressL2:
Suite 2
City: Manhattan
State: New York
Zip Code: 10002-2517
Longitude: 40.7325525
Latitude: -74.004970
Phone: (847) 442-5555
Email: robo@tcabuilders.com
6
MATCH
Robert E. Oldstead
Manhatten, NY
10002
robo@tcabuilders.co
m
847 442-5555
4
CONSOLIDATE
STANDARSIZE2
5

Technologies Required for Effective Information
Governance
Technologies EIM Solutions
Extract transform load (ETL) SAP BusinessObjects Data Services
Data quality SAP BusinessObjects Data Services
Data profiling SAP BusinessObjects Information
Steward
Metadata Management SAP BusinessObjects Information
Steward
Workflow management SAP MDG
Business rules engine SAP MDG
Master data management SAP MDG
BI dashboards and scorecards SAP BusinessObjects Information
Steward or SAP BusinessObjects
Dashboards

SAP Business Objects Data Services
Data integration, data quality, stewardship, and text analytics
Move
Improve
Govern
Unlock
One Runtime
Architecture
&
Services
Business UI
(Information
Steward)
Unified Metadata
Technical
UI
(Data
Services)
SAP BusinessObjects Data Services
4.0
ETL
Data Quality
Profiling
Text Analytics
One Administration Environment
(Scheduling, Security, User Management)
One Set of Source/Target Connectors

Monitor
Quality
continuously
Improve
Data quality
and
governance
SAP BusinessObjects Information Steward
Collaborative environment for your IT and business users
Empower business and IT users with a single environment
to manage the quality of their enterprise data assets
Discover
Understand
and catalog
enterprise
data
Assess
Overall
data quality
Define
Rules and
ownership

Validation Rule for
Business Process
Centric Data
Profiling, Advanced
Data Profiling
System landscape
and architecture
metadata
cataloging
(Dependency
analysis, lineage
etc.)
DQM Functionality
for party and
non-party data
(Create/MaintainCle
ansing Packages)
Comprehensive
Business Taxonomy
(Search, tagging,
Ownership etc.)
Data Profiling
DQ Monitoring
Metadata
Analysis
Cleansing
Rules
Business
Term
Taxonomy
Single Solution with Integrated Data Stewardship
Capabilities

Master Data is the “DNA” of the Enterprise
Enterprise MDM
 Master data domains
across Business
Applications and
Business Analytics
 Line of Business
specific and cross
Line of Business
processes
 Cross Value Chain in
Business Network
Enterprise
Master Data
Procurement Supply Chain
CRM PLM
Finance Manufacturing

Lack of Single View Hinders Business Decisions,
Business Processes, and Business Transformation
Lack of single
view and data
quality
New systems
 Complexity to consolidate
existing and new systems
Reporting/analytics
 Lack of trusted decisions
Business partners
 Lack of business insight
and collaboration
Lines of business
 Inefficient business processes
Cloud
 Complexity to bridge cloud
and corporate master data
Business Impact
 Diminished revenue and service
 Uncontrolled costs
 Lack of compliance

Enterprise MDM Capabilities
Centralized ownership
LoB Customer LoB Procurement other LoB
Decentralized ownership
New systems Reporting/analytics Business partners Cloud
LoB Finance LoB Manufacturing
Enterprise
Master Data
Consolidation and high
velocity integration
Central creation
Data quality
stewardship
Data quality
continuous
monitoring

Seamlessly Support Key Data Quality Best Practices
Data Quality Management, version for SAP Solutions (DQM for SAP)
Improve the quality of customer, partner, and supplier data from within SAP ERP, SAP CRM, and SAP MDG
applications by utilizing SAP DQM’s address cleansing, matching and de-duplication capabilities
EnforceVerify
Real-time data validation
(address reference data
available for 230+ countries)
Batch data cleansing
(ensure ongoing data
accuracy and completeness)
Pre-built best practice data
quality templates
(tuned specifically for SAP
ERP, SAP CRM, and SAP
MDG apps)
Detect pre-existing records
during data loads
(Prospect lists, trade show
leads, webinars, etc.)
Improve
Duplicate record detection
(Potential duplicates are
identified and tracked for best
record resolution)
Clearer, more efficient
customer segmentation
(with the ability to match
on marketing attributes
for SAP CRM)

Automatically Completes and Corrects Entries,
then Presents to User for Approval
In-complete
Address entered.
Automatic
Accurate address
returned in each
address
component

Previously Unknown Entries are Identified,
Before Another Duplicate can be Created
List of pre-existing
entries are presented

9th Oct:
Managing Master Data
A single version of the truth is critical
when governing the data in your
operational systems – whether ERP,
CRM or HCM. Learn how to improve
efficiency and reduce costs across six key
data domains: Customer, Supplier,
Finance, Material, Vendor, and Employee.
Register now ›
bit.ly/1b26WRY
23rd Oct:
Managing Content
Seventy per cent of all data in your
organization resides outside of databases.
By governing unstructured content such as
documents, invoices, notes and images
from directly within your Operational
System, you can reduce costs while
boosting productivity and user adoption.
Register now ›
bit.ly/18nbeRF
6th Nov:
Managing Information Lifecycles
Information Governance is a cradle-to-the-grave exercise. Learn how to understand the data
in legacy systems, migrate it into your Operational System, and decommission legacy
systems. Also discover how to effectively meet regulatory and statutory requirements around
data retention and removal, by managing the entire information lifecycle.
Register now › bit.ly/1dGeXgw
Stay connected
Exchange with experts on
communities and social
networks.
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@SAPEIM ›
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Managing Data Quality Webinar Summary

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