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Determining IT team performanceEstimation and uncertainty drivers
TIME/COST=CAPACITY
When making estimations we are primarily looking for two metrics
SIZE
When making estimations we are primarily looking for two metrics
=
Mature
?
Uncertain
CAPACITYSIZE
TIME/COST
Typically the capacity of a team is based on two techniques
CAPACITY
Historical and observed performance data
Subjective evaluation of current performance
1.
2.or
Let’s look at an example with the variance in historical performance
Size: 3000 FPTeam: 5 HCCapacity: 15-60 FP/M
3000
2.750
2.500
2.250
2.000
1.750
1.500
1.250
1.000
750
500
250
0
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Year 1 Year 2 Year 3 Year 4
Spread
Function Points
Time
Function Points
Time
Let’s look at an example with the variance in historical performance
3000
2.750
2.500
2.250
2.000
1.750
1.500
1.250
1.000
750
500
250
0
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Year 1 Year 2 Year 3 Year 4
Done between 10-40 months
Cost between 0,4 and 1,6 million Euro
Size: 3000 FPTeam: 5 HCCapacity: 15-60 FP/M
Bad PM’sChanging teamsNo standardsNo comparison foundationNo transparencyChanges in context
There can be several reasons for this spread…
And therefore our best option… is often our best guess..
3000
2.750
2.500
2.250
2.000
1.750
1.500
1.250
1.000
750
500
250
0
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Year 1 Year 2 Year 3 Year 4
Function Points
Time
Function Points
Time
3000
2.750
2.500
2.250
2.000
1.750
1.500
1.250
1.000
750
500
250
0
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Year 1 Year 2 Year 3 Year 4
The question is… with IT being so central to our business
– is this an adequate measure?
Source: Standish CHAOS “Decision Latency Theory” report; Project Management Institute ”Improve Business Results” infographic
43% 40%26%
57% 60%74%
On budget On time Achievedfunctionality
Yes No
109122
2015 2018
Most IT projects don’t live up to expectations…
…resulting in increasing investment loss
12%
Lost investment in mUSD per 1 billion USD spent
FASTER
BETTER CHEAPER
MORE
I saw this a the “black box” problem
FASTER
BETTER
CHEAPER
MORE
=
…and the problem was that we were lacking causal thinking
LOCATIONEND-USER PARTICIPANTSDB COMPLEXITYCOMPETENCECONSOLIDATIONDB SIZEREQUIREMENT NOVELTYCOOPERATIONTOOLSUSER STORIESDOMAINMETHOD MATURITYCULTURELEGACYPERSONALITYEXTERNAL INVOLVEMENTTEAM STRUCTURETEAM SIZEMANAGEMENT INVOLVEMENTBUDGET PRESSUREBUREAUCRACYDECISION MAKER PROXIMITYFUNCTION POINTSINTERFACESSTANDARDIZATIONPROGRAMMING LANGUAGEARCHITECTUREPROJECT ESTIMATIONPROCESS DESIGNEQEND USER INVOLVEMENTDB COMPLEXITYCAPABILITIESBACKUP/RESTORESOFTWARE COMPLEXITYEDUCATIONS/TRAININGSKILLSCOGNITIVE ABILITY
COMMUNICATIONPROCUREMENTPEOPLEREUSESOFT SKILLSTESTINGBUREAUCRACY
LOCATION
END-USER PARTICIPANTS
DB COMPLEXITY
COMPETENCE
CONSOLIDATIONDB SIZE
REQUIREMENT NOVELTY
COOPERATION TOOLS
USER STORIES
DOMAIN
METHOD MATURITY
CULTURE
LEGACY
PERSONALITY
EXTERNAL INVOLVEMENT
TEAM STRUCTURE
TEAM SIZE
MANAGEMENT INVOLVEMENT
BUDGET PRESSURE
BUREAUCRACY
DECISION MAKER PROXIMITY
FUNCTION POINTS
INTERFACESSTANDARDIZATION
PROGRAMMING LANGUAGE
ARCHITECTURE
PROJECT ESTIMATION PROCESS DESIGN
CMMI
END USER INVOLVEMENT
SECURITY
CAPABILITIES
BACKUP/RESTORE
SOFTWARE COMPLEXITY
EDUCATION/TRAINING
BIG DATA
BRANDING/MARKETING
COMMUNICATION
PROCUREMENT
PEOPLE
REUSESOFT SKILLS
TESTING
BUREAUCRACY
HARDWARE
COMMUNICATION
STRATEGIES
CYBER CRIME
SW PACKAGES
OUTSOURCING
FRAUD
SOFTWARE LICENSES
TECHNOLOGY STACKS
OPERATIONAL STABILITY
LOCATION
COMPETENCE
DB SIZE
METHOD MATURITY
CULTURE
DECISION MAKER PROXIMITY
PROGRAMMING LANGUAGE
ARCHITECTURE
CAPABILITIES
CONSOLIDATION
REQUIREMENT NOVELTY
TOOLS
TEAM STRUCTURE
MANAGEMENT INVOLVEMENT
FUNCTION POINTS
END USER INVOLVEMENT
INFRASTRUCTURE
SOFTWARE COMPLEXITY
END-USER PARTICIPANTS
SECURITY
COOPERATION
TEAM SIZE
STANDARDIZATION
PROJECT ESTIMATION
BACKUP/RESTORE
BRANDING/MARKETING
PEOPLE
DOMAIN
LEGACY
SECURITY
BIG DATA
COMMUNICATION
PROCUREMENT
REUSE
SOFT SKILLS
TESTING
USER STORIES
PERSONALITY
EXT. INVOLVEMENT
BUDGET PRESSURE
INTERFACES
PROCESS DESIGN
EQ
EDUCATIONS/TRAINING
BUREAUCRACY
BRANDING/MARKETING BIG DATA
PROCUREMENT
SOFT SKILLS
PERSONALITY
LOCATION
COMPETENCE
DB SIZE
PROGRAMMING LANGUAGE
CAPABILITIES
CONSOLIDATION
REQUIREMENT NOVELTY
TOOLS
MANAGEMENT INVOLVEMENT
FUNCTION POINTS
END USER INVOLVEMENT
INFRASTRUCTURE
SOFTWARE COMPLEXITY
END-USER PARTICIPANTS
DB COMPLEXITY
COOPERATION
STANDARDIZATION
PROJECT ESTIMATION
BACKUP/RESTORE
DOMAIN
SECURITY
COMMUNICATION
REUSE
TESTING
USER STORIES
EXT. INVOLVEMENT
BUDGET PRESSURE
INTERFACES
PROCESS DESIGN
EQ
EDUCATIONS/TRAINING
METHOD MATURITY
CULTURE
DECISION MAKER PROXIMITY
ARCHITECTURE
TEAM STRUCTURE
TEAM SIZE
PEOPLE LEGACY
BUREAUCRACY
METHOD MATURITY
CULTURE
DECISION MAKER PROXIMITY
ARCHITECTURE
TEAM STRUCTURE
TEAM SIZE
LEGACYBUREAUCRACY
1-100
~ 0-20%
~ 0-49%
~ 0-32%
~ -66% - +300%
~ 0-18%
~ 0-26%
~ 0-51%
~ 0-32%
PEOPLE
We arrived at the factors mostly influencing performance
Which could be expressed in a formula
People – 1-100x effect
Effect
• The quality of your team members
Source
• Harvard Business Review
• Internal studies from Google, Facebook, and Apple
Learning indicator
Behavior assessment
The
SECRETCODEASSESSMENT
In 7N we use the 5-layer model described in Nucleon
And use the SFIA framework to map the context
Team Size (up to -48% effect)
0
0,2
0,4
0,6
0,8
1
1,2
1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930
PR
OD
UC
TIV
ITY
FAC
TOR
TEAM MEMBERS
Effect
• The number of people on your team
• More members reduce productivity
Source
• Cognitive Load Theory
• The Mythical Man-Month – Frederick Brooks
Team dynamics (-66% to +300% effect)
0
0,5
1
1,5
2
2,5
3
3,5
1 2 3 4 5 6 7 8 9 10
PR
OD
UC
TIV
ITY
FAC
TOR
COMPOUND TEAM EFFECT
Effect
• The amount that high performers lift, and poor performers drag, your team
Source
• “Sitting Near a High Performer can Make you Better at Your Job” –Housman and Minor (2017)
Decision Maker Proximity (up to -32% effect)
0
0,2
0,4
0,6
0,8
1
1,2
1 2 3 4 5 6 7 8 9 10
PR
OD
UC
TIV
ITY
FAC
TOR
DECISION MAKER PROXIMITY
Effect
• The ease with which your team can interact with its decision maker and make fast and precise decisions
Source
• Harvard Business Review
• Standish CHAOS “Decision Latency Theory” report
Bureaucracy (up to -20% effect)
0,7
0,75
0,8
0,85
0,9
0,95
1
0% 20% 40% 60% 80% 100%
PR
OD
UC
TIV
ITY
FAC
TOR
PERCENTAGE BUREAUCRACY
Effect
• The amount of time spent not working on production tasks
Source
• “Team mental models and team performance” – Lim and Klein (2006)
• “Relationships among team ability composition, team mental models, and team performance” – Edwards and Day (2006)
Architecture (up to -26% effect)
0
0,2
0,4
0,6
0,8
1
1,2
1 2 3 4 5
PR
OD
UC
TIV
ITY
FAC
TOR
ARCHITECTURE SCORE
Effect
• How well you company's enterprise architecture is documented and understood to support ease of change/ implementation and re-use.
Source
• “The Relationship between Enterprise Complexity, Business Complexity and Business Performance” – Roest (2014)
• “Familiar Metric Management” –Putnam and Myers (1995)
Legacy (up to -18% effect)
0,5
0,6
0,7
0,8
0,9
1
1,1
1 2 3 4 5 6 7 8 9 10 11
PR
OD
UC
TIV
ITY
FAC
TOR
LEGACY SCORE
Effect
• How many hidden resources you invest in maintenance of obsolete systems
Source
• Beyond Legacy Code: Nine Practices to Extend the Life (and Value) of Your Software – David Scott Bernstein
• QSM databases
Culture (up to -32% effect)
Effect
• To which degree your team’s culture accelerates or decelerates productivity
0,5
0,6
0,7
0,8
0,9
1
1,1
1 2 3 4 5 6 7 8 9 10
PR
OD
UC
TIV
ITY
FAC
TOR
CULTURAL SCORE
Source
• Primed to Perform: How to Build the Highest Performing Cultures – Doshi and McGregor (2015)
• “The Relationship between Corporate Culture and Performance” – Dizik (2016)
Methods Maturity (up to -51% effect)
Effect
• The length of time your team has been working together
PR
OD
UC
TIV
ITY
IND
EX
MONTHS OF PRACTICAL EXPERIENCE
Source
• “Managing the Development of Large Software Systems” – Royce (1970)
• “Agile & Waterfall Methodologies – A Side-By-Side Comparison” – Base36
So what does this mean in practice?
So what does this mean in practice?
Effect
77% increase in effectivity -equivalent to a potential 195 million Euro saving
So what does this mean in practice?
Effect
25,5% increase in effectivity -equivalent to a potential 91 million Euro saving
So what does this mean in practice?
Effect
14,2% increase in effectivity -equivalent to a potential 55 million Euro saving
So what does this mean in practice?
So based on the Nucleon analysis one of the largest Scandinavian banks could look at a total saving of 341 million Euros
- With a prioritized roadmap suggested for the implementation
- And an ability to get detailed, real-time performance knowledge and better estimation and simulation capabilities
”In fact, it is not just a formula – that is the summary – it is a complex family of measurements and analysis that arecompared against best practices in a structured way to reveal all of the major, minor and micro fractures and defects in your IT organizational crystal”
Jim Ditmore, COO Danske Bank
www.nucleonformula.com
Jeppe Hedaa: [email protected]
Thanks for your time