Throughput: quality of your data science. Lack of understanding business context (why), algorithms
(how) and outcomes (what).
Essential for delivering on the promise
- Quality of data: organize & manage your data (governance)
- Processing of data: understand context & algorithms (black-box)
- Outcome sensitivity: retain a healthy suspicion
In general, understanding the why, what and how of data-driven is essential. Without this, it is like
playing with fire as outcomes can be so convincing.
Do we need to transform our organizations (structures & cultures), or just introduce data science
into our organizations to become data-driven?
Many incumbent organizations (still) ground on bureaucratic principles (2 nd industrial revolution).
72
(how) and outcomes (what).
Essential for delivering on the promise
- Quality of data: organize & manage your data (governance)
- Processing of data: understand context & algorithms (black-box)
- Outcome sensitivity: retain a healthy suspicion
In general, understanding the why, what and how of data-driven is essential. Without this, it is like
playing with fire as outcomes can be so convincing.
Do we need to transform our organizations (structures & cultures), or just introduce data science
into our organizations to become data-driven?
Many incumbent organizations (still) ground on bureaucratic principles (2 nd industrial revolution).
72
