1. iWay Next Generation Data Quality
Brent Bruin
iWay Systems Architect

2. iWay Software Next Generation Data Quality
One industry study estimated the total cost to the US economy of data quality problems at over US$600 billion per annum (Eckerson, 2002).

3. iWay Software Next Generation Data Quality
Push, Pull, Event Based
Up-stream, In-stream and Down-stream processing
Integrated Data Quality, Data Profiling and Master Data Management
Integrated ETL and ESB Framework
Event Capture
Rich Data Profiling solution powered by WebFOCUS
“I have to say that I like this approach. Not just in terms of the CEP engine but also the whole idea of embedding capabilities into an ESB. In effect, this is a turn-around from traditional approaches. Other vendors in this space primarily started as batch vendors and now offer real-time or near-real-time extensions but they are still basically batch products. iWay, on the other hand, has designed its EIM to target real-time requirements and, yes, it can do batch too. Companies whose primary requirements are for real-time processing could do worse than to take a good look at iWay EIM.”
Philip Howard, Bloor Research
Copyright 2007, Information Builders. Slide 3 3

4. Data Quality Deployment Points
Data Quality can be implemented in all information touch points.
Customer/Patient touch points
Application touch points
Up-Stream
In-Stream
Down-Stream
Customer/Patient touch points
A touchpoint is defined as, all of the communication, human and physical interactions your customers experience during their relationship lifecycle with your organization. Whether an Web site, sales person, store or office, Touchpoints are important because customers form perceptions of your organization and brand based on their cumulative experiences. If a customer enters data but is incorrect, this should be highlighted at this touchpoint to ensure the data is accurately recorded.
It is important that Data Quality processes ensure the information is validated at this stage, this is the earliest stage in processes, also at this stage it can be the root cause for bad quality data being process and spread through out your organisation.
Application Touchpoints
Touchpoints is defined with the communication with an application, the cause can be the result/mid-operation of an event or service, this results in an operation being carried out in the target appliciation and source application. Within a single Business process this can involve many application touchpoints. It is important to be able to apply the data processes as early as possible in your business processes. CRM/ERP or simple database.
Mention: importance of decoupling DQ processes from implementation knots. DQ processes should be re-useable and callable from anywhere.

5. Upstream Data Data Enters from Multiple Points Manual Data Entry
B2B Gateway
Call Center
Self-Service Portal
EIM Issues
Accuracy
Completeness
Business Rule Validation
Correlation
B2B
Portal
Call
Center
CRM
FIN
ERP

6. In-Stream Data Data is a Flowing, Dynamic thing Complex Processes
Derived Data
Evolving Semantics
Operational BI
EIM Issues
Error Detection and Correction
Lost or Mismatched Information
Duplication
Validation as Evolves
Order
BOM
Invoice
Payment
Receipt
Ship
Notice
Data is usually flowing. Is evolves and changes, data is enriched and transformed. Ensuring the data is valid thru all this stage is important to ensure the validation and result of the service and business process is fullfilling its purpose. The business process may be fulfilling its activities but the data is not good to serve its purpose.

7. Downstream Data Data is collected, manipulated, and analyzed
DM/DW/Cubes/Analytical BI
Performance Management
Compliance
Auditing
EIM Issues
Access
Accuracy
Completeness
Mismatched Semantics
Order
BOM
Invoice
Payment
Receipt
Ship
Notice DM DW
Cube
When data is applied to target systems and warehouse, BI reporting, customer and patient are usually experiencing the information and viewing the information. The reports would not generate an accurate view of the business. The customer/patient will not see the correct information or received the best possible treatment and service. The business performance and drilldown capability of the performance management informantion may not be accurate as it could be.
Compliance – risk management
This may not raise or not make the company aware of compliance and regulatory rules accurately and early as possible.Risk management is not held properly.
Auditing – this can effect the auditing information, incorrect/insufficient representation of information for an entity.

8. Data Quality Management Cycle
Monitoring &
Reporting
KPI Definition
Ongoing Monitoring
Deviance Identification
Data Understanding
Profiling
Data Impact Analysis
Issue Causes Identification
Data Cleansing
Parsing
Format Correction
Content Evaluation
Automatic Correction
Content Based Cleansing
Data Enhancement
Standardization
Enrichment
Unification
Duplication Identification
Association
Business User
Business User
IT Professional
IT Professional

9. Master Data Management Defined
MDM for customer data systems are software products that:
Support the global identification, linking and synchronization of customer information across heterogeneous data sources
Create and manage a central, database-based system of record
Enable the delivery of a single customer view for all stakeholders
MDM architectural styles vary in:
Instantiation of the customer master data — varying from the maintenance of a physical customer profile to a more-virtual, metadata-based indexing structure
The latency of customer master data maintenance — varying from real-time, synchronous, reading and writing of the master data in a transactional context to batch, asynchronous harmonization of the master data across systems
An MDM program potentially encompasses the management of customer, product, asset, person or party, supplier and financial masters.

10. Why iWay? Open and flexible One product Modern Components
Integrates easily in any environment
Supports all architectural approaches and styles
One product
Data Profiling -> DQ -> MDM
Start small - grow big by enabling components
Modern
Architecture
Best practice methodology
Iterative approach and fast deployment
Components
Customers like it (Source: Information Difference)
Developers like it