Part 2: Digitalisation Flashcards
Digitization
The process of transforming analogue information into digital bits
Digitalization
Organizational and social changes as a consequence of digitization
Some examples of digitalization trends
- Mobility
- Cloud computing
- Social media
- Internet of Things
- Big data
- Data Analytics
- Artificial intelligence
- Machine learning
- Blockchain
Big Data challenges in Accounting
Assurance services
Financial and Managerial accounting
Research and education
Challenges common to all areas
Pace of change drivers according to Oracle
- Sustainability
- Brand reputation
- Security
- Compliance
- Talent
- Competition
- Customer expectations
- Innovation
- Technology governance
Ability to respond constraints according to Oracle
- IT infrastructure
- Data
- Processes
- People & Culture
- Operating model
Accountant’s work has according to Oracle
Shifted from accounting activities to analysis and actions
Business Intelligence
A broad category of applications and technologies for:
1. gathering
2. storing
3. analysing
4. sharing
5. providing access to data
in order to help users make better business decisions
Data Analytics
Synonym for business intelligence
Broader term that includes statistical and mathematical processing capabilities
What is our position and responsibility as accountants?
Risk & responsibilities
Boundaries & protection
Performativity of data (illusion of objectivity)
Analytics three dimensions
Descriptive
Predictive
Prescriptive
Challenges for the accounting profession
- Automation of small, structured tasks
- More analytical tasks
- Structured tasks are needed to understand analytical tasks
- Humans are hesitant to decide against analysis
- System only as good as the humans programming it
- Disregarding of implicit knowledge
Accountants can
- Be aware
- Become an interface between technology, regulation, and business
- Get involved in standard setting
- Educate about performativity of quantification
- Develop control mechanisms
- Enable transparency through reporting and giving account in different forms
Digitalizational logics to create value
- Automize
- Inform
- Transform
- Visualize
- Analyse
What are the implications of big data on Assurance services
Audit risk
Service organisation controls
Compliance audits
What are the implications of big data on financial and managerial accounting?
Strategic partner role
Convince managers of analytics benefits
Monetisation of big data
What are the implications of big data on accounting research and education?
Student’s career path
Course credits limitation
What are the big data challenges common to all areas within accounting
Analytical skills
Privacy and security
Creative thinking
Automation threat
What does the accountants risk & responsibilities mean?
Security & quality (from variety, volume, and details of data)
Ethical conduct & human rights (appropriate use of data)
Shift in responsibility and power relations
Cybersecurity
What does boundary & protection mean?
- External regulation and enforcement (coercive pressure)
- Self-regulation of the industry (reputation and customer trust)
(Who is capable of regulating and enforcing?)
(Upstream and downstream monitoring of data) - Transparency
- Extent of customer control over data use
What does performativity of data (illusion of objectivity) mean?
- Numbers and quantification give the illusion of objectivity
- Through representing reality we also create reality
- Bias in use
- Bias in design
What’s bias in use?
Anchoring
Availability
Unintentional blindness
Confirmation bias
What’s bias in design?
What is collected
How it is transformed
How it is analysed
How can Simons (1995) four levers of control be used in a digital environment? (From Capgemeni lecture)
Cultural controls
- Relate analytics initiatives to organisational culture, what are the gaps and the risks?
Boundary controls
- Data security and governance
Interactive controls
- Business & IT forums and collaborations for cross functional exchanges
- Exchange of data and information
- Data literacy capabilities
- External partnerships
Diagnostic controls
- Proactive solutions (predictions, cause & effects)
What are smart, connected products? (Consist of) (Porter & Heppelmann, 2014)
Physical components
Smart components
Connectivity components (one-to-one, one-to-many, or many-to-many)
What can smart, connected products do? (Four levels) (Porter & Heppelmann, 2014)
Monitoring
Control
Optimization
Autonomy
Porters five forces in a digitalisation setting (Porter & Heppelmann, 2014)
- Bargaining power of buyers
- Digitalisation expands opportunities for product differentiation.
- Closer customer relationship through customer data - Rivalry among competitors
- Product differentiation and value-added services - Threat of new entrants
- Barriers to entry in the form of high fixed costs, complex product design, embedded technology, and IT infrastructure
- First mover advantage in collecting data
- Barriers can go down when digitalisation invalidates strengths of assets - Threat of substitutes
- New types of substitution threats, such as wider product capabilities
- Product-as-a-Service models - Bargaining power of suppliers
- New types of suppliers
- Connectivity components deliver more value relative to physical components
Redefining industry boundaries (Porter & Heppelmann, 2014)
Product Smart product Smart, connected product Product system System of systems
The net effect of smart, connected products on industry structure will vary across industries, but some tendencies seem clear (Porter & Heppelmann, 2014)
- Rising barriers to entry thanks to first mover advantage in data collection and analysis
- Consolidation pressures will be amplified in industries whose boundaries are expanding (a single product manufacturer will be in disadvantage to a multiproduct company)
The implications of smart, connected products for the value chain (Porter & Heppelmann, 2014)
- Product design
- Hardware standardisation through software customization
- Enable personalization
- Support ongoing product upgrades
- Enable predictive, enhanced, or remote service - After-sale Service
- Predictive maintenance and service productivity
- Take advantage of product data that can reveal existing and future problems - Marketing
- New kinds of relationships with customers - Human resources
- Recruit new skill sets - Security
- Need for robust security management
Strategic risks with smart connected products (Porter & Heppelmann, 2014)
- Adding functionality that customers don’t want to pay for
- Underestimating security and privacy risks
- Failing to anticipate new competitive threats
- Waiting too long to get started
- Overestimating internal capabilities
How is data reshaping the value chain at its core? (Porter & Heppelmann, 2015)
The new data resource
Data analytics
