Volume, Velocity or Variety, which V imports the most?
A lot has been written and said bout the multiple Vs that define big data. Three were used initially: Volume, Variety and Velocity. Then appeared Veracity, Value and Visualization.
As the list of Vs grows, it becomes impossible to tick all boxes. Then a question arises: which V imports the most?
Of course, context matters and I will present below one recent research that tackled that question.
When it comes to innovation, Volume has no impact and Velocity matters most
Ghasemaghei and Calic tested a model that relates the three Vs (Volume, Velocity and Variety) to the firm performance through its innovation performance (innovation efficacy and efficiency). You can see the article here.
What are the main insights of this study:
- when firms process data that are high in volume or variety, but low in velocity, they generally achieve outcomes inferior to those when firms process data that are high in velocity
- analyzing and interpreting data in real-time to quickly generate new insights plays a more important role in innovating successfully and efficiently than does focusing on integrating large sizes of different types of data
- data velocity plays a more critical role than data variety and volume in enhancing firm performance
- if firms wish to enhance their outcomes but lack sufficient resources to process data high in all three big data characteristics, they should focus first on data velocity
