Synthetic Data for Data Scientists

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

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