service costing/tco data assessment

Data Prep for Service Costing

Copyright 2014 Thavron Solutions, LLC

Agenda

• Introductions• Overview• What is Data Maturity?• Assessment- – Why– When– How

• Opportunities for small group data evaluations


INTRODUCTIONS


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]


mailto:[email protected]/

Introductions

• You? – Name– Role– Company/Organization– Did you bring data to work on?


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


OVERVIEWData Model for Service Costing


Data for Service Costing

• Dependent on Service Costing Philosophy– Defined Allocation– Calculated Allocation– Consumption – Based Model


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


Types of data for a Consumption Based Model • Service Cost Components:– Labor– Infrastructure– Software – Maintenance & Support– Ongoing Development– Depreciation – Service Consumption Records


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)


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.


What to measure?

• Stability• Completeness• Correctness


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


Data Completeness

• 80-90 % of data needed for common linkages are present

• Are your required fields also required data fields in source system?


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


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


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


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


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


DATA ASSESSMENT: ASSESSMENT HOW TO
