- Nascent stage → many companies don’t have awareness of the data
- Pre-adoption stage → companies already aware of the data on their companies. How they will organize the data, analyze the data, start studying and sort of experimentation, and start to think about what can data do (decision making)
- Early adoption → already implemented at least one or two business processes that involved big data analytics
- Corporate adoption → can make use of any data in many forms
- Mature adoption → the data is being involved as crucial information, the data is used to drive and give insight, well-prepared risk management if a disaster happens
- Analytical cycle (only use several techniques rather than implement all of them)
- Descriptive analytics
- Predictive analytics
- prescriptive analytics
additional information
- Extract is the process of identifying data sources and acquiring the data
- Such as we need to consolidate every field (on the database) that required to go to the next step
- One of the main challenges is: if there’s no proper documentation regarding the scheme of a database
- Transformation are the process of mapping and harmonizing from the data that has already been acquired so it can be cleansed, consistent, and reliable
- Loading is the process of moving the data from source systems to their final destination after the data has been cleansed
- Needs more data not just single sources
- Define and strengthen the argument of “goods”, “quality”, “efficient”
- Put another point of view, a holistic viewpoint to gain better insights