Session 8 Flashcards
Data monetization
is the process of using data to increase revenue.
Companies can take three approaches to monetizing their data:
(1) Improving internal business processes and decisions
(2) Wrapping information around core products and services
(3) Selling information offerings to new and existing markets
The four V’s of data
● Volume: scale of data
● Variety: different forms of data
● Velocity: speed
● Veracity: quality
● Value → missing
Correlation means…
that something is related, it says nothing about cause and effect
Two ways to infer causality
- Observational data
2. Experimental data
Observational data
○ Assumptions and data to deal with the lack of random assignment
Experimental data
○ Random assignment to different conditions
○ AB TESTING
For both Observational data and Experimental data
- Two or more “treatment” or “control” conditions
- Measurement of some outcome (“dependent variable”) of interest
- “Causal effect” is how different levels of treatment (or control) lead to different levels of the outcome
There are two major obstacles to monetising data
(1) The accessibility and quality of the data.
(2) the lack of accountability.
Three levels of analytics prowess
1 Aspirationals
2 Experienced
3 Transformed
Aspirationals
These organizations are the furthest from achieving their desired analytical goals. Often they are focusing on efficiency or automation of existing processes and searching for ways to cut costs.
Aspirational organizations currently have few of the necessary building blocks — people, processes or tools — to collect, understand, incorporate or act on analytic insights.
Experienced
Having gained some analytic experience — often through successes with efficiencies at the Aspirational phase — these organizations are looking to go beyond cost management.
Experienced organizations are developing better ways to collect, incorporate and act on analytics effectively so they can begin to optimize their organizations.
Transformed
These organizations have substantial experience using analytics across a broad range of functions. They use analytics as a competitive differentiator and are already adept at organizing people, processes and tools to optimize and differentiate.
Transformed organizations are less focused on cutting costs than Aspirational and Experienced organizations, possibly having already automated their operations through effective use of insights. They are most focused on driving customer profitability and making targeted investments in niche analytics as they keep pushing the organizational envelope.
According to Vidgen et al. (2017)
It is important to create a strategy around big data and analytics. this should be done on a top-down approach.
The firm should put analytics in the middle of the strategy of the firm, being the overlap and connection of the available data, the organizations and its structures and the business with its value proposition/ strategy.
Besides this, it is important to continually address the importance of data and analytics in decision making amongst employees and departments.
Vidgen et al. (2017) conducted a Delphi study with dozens of experts to investigate two central questions:
• What challenges do organizations face on the path towards creating value from (big) data?
- Data
- Value
- People
- Technology
- Process
- Organization
