Synthetic Data for Data Scientists
The Data Scientist is responsible for the adoption of data-driven innovation within the organisation. While achieving this goal, the Data Scientist typically faces various pains that could potentially be solved with Synthetic Data. This blog describes the role of a Data Scientist, indicates typical pains together with the gains that could be achieved with Synthetic Data.
The Data Scientists job includes
Arrange access to relevant data
Realize data-driven innovation (for example with AI, predictive modelling, data visualization)
Guide the organization to become a data-driven frontrunner
Data collection, cleaning and preparation
Realize and implement proof of concepts
Typical pains that Data Scientists face
A maze of various extensive internal processes to get access to data
Access to the data is prohibited due to legal, privacy or risk constrains
Applying classic anonymization techniques results in the ‘garbage-in garbage-out’ principle
No solution resulting in choosing between a project-stop or questionable data-access
Insufficient or conflicted training data
Biased and unbalanced data increasingly pose ethical discussions
Untouched valuable datasets that cannot be transformed into valuable insights
Loss of energy from involved parties
How could Data Scientists gain from using Synthetic Data?
Focus on core data science tasks
Access to more data
Faster data access
Overcome time consuming (and energy draining) internal data access policies
Reduced situations with questionable data-access