1 A Model for Optimizing Process Efficiency in a Multi-stream Data Keying Environment Presented by: Arun Jain, SVP/GM, Data Management Services, ICT Group Candice Blom, VP, National Wholesale Lockbox, J.P. Morgan Treasury Services Dan Hillman, VP, BPO Services, ICT Group
Presentation from TAWPI 2009 annual meeting, Washington DC. Co-authors Arun Jain, ICT Group, and Candice Blom, JPMorgan Chase
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1
A Model for Optimizing Process
Efficiency in a Multi-stream Data
Keying Environment
Presented by:
Arun Jain, SVP/GM, Data Management Services, ICT Group
Candice Blom, VP, National Wholesale Lockbox, J.P. Morgan Treasury Services
Dan Hillman, VP, BPO Services, ICT Group
2
Summary
• Data Keying Challenges
• Root Causes and Obstacles to Change
• Co-mingling – The Key to Optimizing Processes
• Identifying the Best Strategy for Your Organization
• Case Study – Data Keying Co-mingling at J.P.
Morgan Treasury Services
• Question and Answer
3
ICT Group: Overview
• Leading global provider of outsourced
customer care and related technology &
business processing services
• $428.1 million revenue (2008)
– Publicly traded (NASDAQ: ICTG)
• Deep vertical expertise
• 40+ onshore/near-shore/offshore centers,
including India and the Philippines
• 18,000 employees worldwide
• ISO- and Six Sigma-guided operational best practices
• Data management considered critical business function that crosses multiple LOBs
• Evolution/growth/M&As can result in multiple (and often duplicate) data management processes
• Metadata created during the work-stream life cycle further complicates ability to efficiently organize and manage data
Data
Capture
Data
Analysis
Data
Manipulation
Data
Storage
6
Enterprise Challenges: Data Keying
• True costs of data management difficult to quantify and track
– Results in redundant costs and reduced productivity
• Difficult to understand full cost of enterprise-wide data management operations
– Inability to assess fixed and variable costs at process level
Results In:
• Overestimated efficiency levels
• Underestimated costs
• Lost opportunities for process improvement
7
Data Keying Inefficiencies: Root Causes
• Similar or identical processes performed in multiple
locations by separate management teams
• Decentralized processes tend to multiply over time:
• New products/services launched by multiple LOBs
• Varying turnaround times lead to over-segregation
of duties and inefficient resource utilization
8
Obstacles to Change
Stay out of
my sandbox.
We don’t know
what we don’t
know.
We’ve always
done it this way.
Without a top-down or cross-functional inventory of data management processes, difficult to identify and leverage opportunities for process improvements.
Different management structures (by different LOB or geography) wish to maintain direct control of data and its use.
Ad-hoc process evolution to meet dynamic business rules leads to less effective ways of doing work.
9
Process View of Back-Office Work
Batch ModeContinuous
10
The Ideal Scenario
• Monitor overall SUR as key indicator of process efficiency
• Standalone operations would have required 169 seats for steady state volume of all four processes.
• By co-mingling four processes at one site under a single management team, 68 seats were eliminated.
• Co-mingling supports a robust disaster recovery solution. An additional 25% capacity is available for short term disruptions at J.P. Morgan Treasury Services sites.
Reduction in total seats = 40%
At an average annual cost of $6,759 per seat/year, J.P. Morgan Treasury Services avoids over $400,000 in annual costs.