Data has been the domain of science and math majors. Although an objective exercise, the creativity and subjective nature of looking for a needle in a haystack or finding patterns that could be repeatable is an art form.
Data has three major scientific components –
- Volume – Amount of data that has been collected and used
- Velocity – Rate at which the data has to be curated
- Variety – Variants of like and dissimilar information that has to be collated
But the most telling piece of information is deciphering the “Value” of data. Value of data is usually determined by professionals who have –
- Patterns that the data signal
- Collaborate on the signals with the right people
- Identify the Risks and limitations of potential actions
- Mitigation strategies to be assessed
- Actions that initial change
- Use the actions initiated and assess patterns again for predicting outcomes and initiating proactive actions
It requires science to do data engineering but the most creative ones provide the framework for better data science experimentation. Assess change for continual improvement in social sciences or in science.