Workshop Guide slides from the Atlanta ITFMA conference- Presented July 13, 2015. How to assess your data readiness for Service Costing/TCO activities. Note, the second half of the workshop was in spreadsheets note included here. Please contact us for more information or templates.

1. Data Prep for Service Costing
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2. Agenda
• Introductions
• Overview
• What is Data Maturity?
• Assessment-
– Why
– When
– How
• Opportunities for small group data evaluations
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3. INTRODUCTIONS
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4. Introductions
• Us: Nan Braun
• CEO Thavron Solutions
• nbraun@thavronsolutions.com/ @thavronsol / http://thavron.com/blog
• Corporate IT – many roles- “hands” on to BRM to architecture
• CTO, ontological modeling
• Director, TBM Practice
Terry Dawkins
• TBM Consultant/Data Architect Thavron Solutions
• tdawkins@thavronsolutions.com
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5. Introductions
• You?
– Name
– Role
– Company/Organization
– Did you bring data to work on?
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6. Thavron Biases
• Systems Thinking
• Universal Business Service Model
• Manufacturing
– Lean (MESA Lean Manufacturing Strategic Initiative Guidebook author, Toyota Method via GM/Delphi)
• Modeling/Frameworks
• Common Sense
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7. OVERVIEW
Data Model for Service Costing
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8. Data for Service Costing
• Dependent on Service Costing Philosophy
– Defined Allocation
– Calculated Allocation
– Consumption – Based Model
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9. Assessment Philosophy
Design First, then Assess for Capability
• Design ideal model based on
Stakeholder input
• Pros:
– Stakeholder buy in /ownsership
– Ideal solution from the start
• Cons:
– Data not capable of supporting model
– Project “hangs” or collapses
Assess first, Design to Capability
• Understand current data status, design
model to capability with specific
maturity goals
• Pros:
– Design will succeed
– Rapid transparency to data
“imperfections”
• Cons:
– Resistance to evolving/maturing model
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10. Types of data for a Consumption Based Model
• Service Cost Components:
– Labor
– Infrastructure
– Software
– Maintenance & Support
– Ongoing Development
– Depreciation
– Service Consumption Records
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11. Where does the data come from?
• Finance System
• Depreciation Tracking
• CMDB
• Asset Management
• Storage Management System(s)
• Data Center Management
• Project Management/Tracking
• Ticketing System(s)
• Server Management Software
• VCenter or equivalent
• Cloud Provider Invoicing
• HRIS
• Network Management System(s)
• License Management System(s)
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12. How Does the Data Fit Together?
• Not a direct linkage to Service in all data sources-
– Layered model / Cost Components that are assembled into Services
• Does need key field linkage between data sources within
components or Business Rules that link them together.
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13. What to measure?
• Stability
• Completeness
• Correctness
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14. Data Stability
• Column order and headers are
unchanging
• Values in a column are consistent
in meaning – no context sensitive
values
• Format of data is stable over time
and between data sources
– Automated versus hand entered
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15. Data Completeness
• 80-90 % of data needed for
common linkages are present
• Are your required fields also
required data fields in source
system?
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16. Data Correctness
• Effort to measure before
modeling begins almost never
worth the “win”
• Ownership belongs with Source
Data Owners
– Engage them early and share
reports/results frequently
– Build a partnership
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17. The Myth of Perfect Data
Myth:
You need perfectly clean and aligned
data to get insight to service costing
Fact:
You need data that is “clean enough”
to allow your model to function
consistently to get insight to
service costing
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18. What is Clean Enough to Build?
• Contains key fields needed to model
• Data has good Stability
• Data has reasonable Completeness
• Correctness will come with validation
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19. Creating a Data Assessment Plan
• Coordinate with Source System owners/SMEs
• Extracts in raw format for assessment
• Careful measurement of Stability and completeness
• Calculation of maturity and readiness
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20. Pitfalls
• Data Samples can miss errors/problems
• Perception of “problem data” can slow
down your project
• Data Extracts change when you go to
launch
• System changes/updates/upgrades
impact extracts
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21. DATA ASSESSMENT: ASSESSMENT HOW TO
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