6. Learning from Experience Flashcards
Organisational Learning
- shapeless, lifeless and formless entities
- requires systems and processes to be put in place
- collecting, reflecting and sharing information to improve their performance
- methods: single and double loop learning, learning organisations and analytics
Single Loop Learning
repeated attempts at the same problem with no variation of method and without ever questioning the goal
- doing things right
Double Loop Learning
having attempted to achieve the goal on various occasions, to modify the goal in light f experience or possibly even reject it
- constantly challenging the assumptions and goals about the world (for potential transformative changes)
- doing the right thing
Difficulties in Double Loop Learning [SBP]
Status Quo Bias
- required to challenge and question business models and organisational assumptions
- managers think they are always right since they have experience
Bandwidth
- managers are not given enough time and resources to think about the bigger issues and implement fundamental changes
Power and Social Equation Dynamics
- lack of power and authority to implement changes (balance of top down and bottom up approach)
- system-wide change is necessary
Creation of Learning Organisations
- recognise that learning is a system-level phenomenon (knowledge management and codification; actionable intelligence)
- to institutionalise knowledge and making sure it stays in the system even when people come and go
- learning organisations = learning orientations + facilitating factors
Learning Organisations: 3 Stages of Learning
Knowledge Acquisition
- development or creation of skills, insights, relationships
- through discussions, surveys, interviews, etc
Knowledge Sharing
- dissemination of what has been learnt
Knowledge Utilisation
- integration of learning so it is broadly available and can be generalised into new situations
- inscribing knowledge into organisational processes
Learning Organisations: Assumptions [CALF]
- learning conforms to culture (the need to pose a learning method that is relevant to your culture)
- all organisations are adaptive learning systems (with well-developed core competencies, continuous learning attitude and the ability to renew and revitalise)
- style varies between learning systems (depends on learning orientations)
- generic processes that facilitates learning (facilitating factors, always enhanced regardless of learning orientations)
Learning Organisations: Learning Orientations
[1 source, 4 focus, 2 modes]
Values and practices that reflect where learning takes place and the nature of what is learnt (highly contextualised to operating climate)
- knowledge source: internal vs external; innovate vs imitate
- product-process focus: accumulation of knowledge about what products are vs the process of developing products and services
- learning focus: incremental vs transformational; corrective learning vs transformative/ radical learning
- value chain focus: design vs deliver
- skill development focus: individual vs group
- documentation mode: personal vs public knowledge
- dissemination mode: formal vs informal
Learning Organisations: Facilitating Factors [O LEVElS]
Structures and Processes that affect the difficulty in learning to occur and the amount of effective learning that takes place
- *climate of openness
- *involved leadership
- *experimental mindset
- *operational variety
- *continuing education
- *systems perspective
- scanning imperative
- performance gap
- concern for measurement
- multiple advocates
Analytics 1.0
The era of business intelligence
Data - rudimentary, sourced in-house
- about production processes
- small and static data sets
Outcome - objective, deep understanding of important business phenomena; give managers the fact-based comprehension to go beyond intuition
- no predictions, no explanation
- descriptive analysis
Skills - more time was spent preparing for analysis than the analysis itself; low computing and analytical power
Analytics 2.0
The era of big data
Data - generated in-house, sourced externally
- the help of the internet
- large data sets
Outcome - to build models and make sense of data to transform to strategy
- predictive analysis
- analyse data quicky
Skills
- the need for new methods
- the need for data scientists: computational and analytical skills
- to generate, curate and consolidate data vs to interpret
Analytics 3.0
The era of data-enriched offerings
Data
- everywhere due to the Internet of Things
- seamless integration of data into our lives and decisions
- online footprint
Outcome
- generate algorithms to analyse browsing patterns
- information providers –> insight providers
- for enriched offerings: the benefit of the customer and for creating more valuable products and services
Skills
- prescriptive analysis: foresees what will happen, when it will happen and explains why it will happen
- provides recommendations on how to act upon it in order to take advantage of predictions
- modelling
Challenges in Big Data Analysis [D-HHAM]
Figuring out which data to use
- mixed-method analysis of quantitative and qualitative data
- quantitative: strength and direction of relationship
- qualitative: underlying explanations of those relationships
The need for a hypothesis
- to give a direction and focus, but also need to let data speak for itself
- balancing between direction and exploration
Balancing data with a human touch
Transforming data into actionable plans
Analytics and Modelling
- specialised training is required