The speed at which new data is created,

and the need for the new data to be considered and analyzed.

The speed at which the data is generated and processed to meet the expectations.

Covid Examples:

The speed at which the pandemic was spreading to new geographies.

The pace at which the new variants and their infections were spreading.

The nature of data, their Heterogeneous sources of structured, semi-structured and unstructured data.

Diversity of Data Distribution.

Can lead to different Interpretations.

May call for Specialization and Standardization.

Covid Examples:

The correlation of various Age Factors and Pre-existing health conditions affecting the spread.  

Inconsistencies and Uncertainties in Data.

Depends on factors of data, like - sampling methods, mode of capture, place, time, intervals, parameters, accuracy, completeness, etc.

Can lead to variations in meaning, interpretation, processes, and challenges involved.

Covid Examples:

Completeness, Correctness of data relating to symptoms and pre-existing conditions. And their interpretations.

Experience of personnel recording the data.

Types of equipment and tools used to record and capture.

How the data is put to work?

Can lead to informed decisions and measurable improvements based on the data being collected, analyzed, researched, and interpreted at a global-scale.

Covid Examples:

Leading to informed decisions like -

• Governments imposing restrictions like lockdowns travel bans, etc.,
• Pharma companies planned on scaling up vaccine production, etc.
• Temporary testing, healthcare, and vaccination centers sprung across, to cater to pandemic needs