Night School
Using
AI for Data Audit and Quality
Barclays Bank Case Study: Benchmarking Organizational Data Flow Quality
Adrian
McKeon
Managing Director
Infoshare Limited
The traditional approach to 99% of large IT projects is to invest in warehouse/ERP/CRM’s
linked to legacy systems by a network.
The flaw with this approach is the impossibility of forecasting how data records relate to each other at the start of the project. Final business rules rarely reflect initial assumptions. Small flaws at the start of the project magnify into inexplicable defects in end user outputs and degraded system performance.
- Barclays integration projects were frequently based on assumed but unproven rules and relationships. Decisions on how much cash to set aside to cover debts to customers – should the bank fail - were fraught with uncertainty
- Barclays decided to separate data from IT and resolve issues offline rather than scrap/rework existing systems
- Artificial intelligence technology was used to audit trail the history of each data record at sub field, field and record level from each source system to the warehouse
- End users now use audit trails to validate outputs, identify errors and track back to fix them within the existing IT infrastructure.
One advantage of AI is to be able to prove the accuracy of output from any IT. The application of AI as a data quality tool is mostly unexplored. The technique deserves wider consideration.
- The problem: dynamic business rules in a static IT environment
- Separating data from IT and creating a virtual model of the bank from its data flows
- Using audit trails to calculate how much cash to set aside to cover liabilities if the bank fails
- Other uses of audit
trails:
- Proving ROI on IT investment
- Value driven mergers and acquisitions
- Improved corporate governance
- Better risk and fraud management
- Evidence based cross selling
- Using unique identifiers for data protection in people tracking
Using SAP’s Business Intelligence Solution for Enterprise Data Warehousing
Kevin
McDonald
CEO
Compendit
Many organizations that have implemented SAP have achieved operational
efficiency but have had difficulty accessing this information to compete
more effectively. The old adage "You can't control what you don't
measure" is only the beginning. In today's business environment a
more appropriate saying may be "You can't optimize if you don't analyze".
Business requirements and priorities are shifting with the competitive
landscape. These rapid changes require a flexible decision processing
architecture where information is not only accessible but also actionable.
Attend this session with Kevin McDonald, consultant and author of "Mastering
the SAP Business Information Warehouse", where you'll learn how some
of the world’s leading organizations are implementing enterprise data
warehouses.
- Learn how SAP’s BI architecture supports analytic applications.
- Investigate the strengths and weaknesses of the SAP BI solution
- See how SAP BW may be implemented to co-exist with already implemented data warehousing infrastructures
- Hear lessons learned from two customer case studies (VW Bank and NASA) where SAP BW plays a key role in enterprise data warehouse architecture.
Enterprise
Modeling and Metadata
Steps (and Mis-steps) from a Real-World Project
Ray McGlew
Director, Data Administration
IMS Health
IMS Health began a process to create a logical data model of the data it collects, manages, and distributes to clients. This model was designed to assist the company in consolidating many stove-piped applications in several countries.
This is a real-world example of introducing enterprise modeling and comprehensive metadata management into a company. We have made significant progress in creating this enterprise data model, and have added goals to bring some organization to the process. We have also started a comprehensive Global Metadata Repository implementation to complement the model. This presentation will outline the steps (and mis-steps) the team made including the activities to keep management excited enough to keep funding this infrastructure project.