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iWay Next Generation Data Quality

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Presentation on theme: "iWay Next Generation Data Quality"— Presentation transcript:

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

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