Download - Service Costing/TCO Data Assessment
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Data Prep for Service Costing
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Agenda
• Introductions• Overview• What is Data Maturity?• Assessment- – Why– When– How
• Opportunities for small group data evaluations
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INTRODUCTIONS
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Introductions
• Us: Nan Braun• CEO Thavron Solutions• [email protected]/ @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• [email protected]
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Introductions
• You? – Name– Role– Company/Organization– Did you bring data to work on?
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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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OVERVIEWData Model for Service Costing
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Data for Service Costing
• Dependent on Service Costing Philosophy– Defined Allocation– Calculated Allocation– Consumption – Based Model
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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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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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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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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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What to measure?
• Stability• Completeness• Correctness
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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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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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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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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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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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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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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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DATA ASSESSMENT: ASSESSMENT HOW TO
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