Analytics Scope Indicator

Sizing Big Data Problems with the Analytics Requirements Index (ARI)

What is “Big Data”? Skytree provides clarity with a quantitative approach for sizing Big Data Analytics challenges.

“Big Data” is an imprecise term, and is even less precise when you consider “Big Data Analytics.”

How big is “big”, exactly? Skytree introduced the Analytics Requirements Index (ARI) in order to eliminate the confusion and help organizations quantify their analytics requirements. It is a simple formula that can be used to estimate the scale of the analytics problem and thus the power of the analytics engine required to solve the problem.


The Analytics Scope Indicator (ASI) has a simple definition:

Analytics Scope Indicator =

# Rows × # Columns Time (secs)

Where # Rows = Number of records being analyzed
# Columns = Number of variables captured in each record
Time (secs) = The timeframe within which to complete the analysis

A simple way to interpret this definition is to say that a certain number of data elements (ie. # Rows × # Columns) must be processed within a given period of time.


To bring the ASI to life, consider a recommendation engine that suggests additional purchases to a user before checkout from an online store. In this case:

# Rows = 2MM previous purchases at the store
# Columns = 33 fields including previous items, price, etc.
Time (secs) = The recommendation should be generated in half a second

The resulting ASI is 132MM per second.

In effect this means that the data analytics engine must process 132MM data elements per second to optimally address this problem.


Heinrich Group Server scales to handle virtually any ASI – contact us and we can help you understand your implementation options for your Big Data Analytics challenge.

Low Medium High
Volume (# rows) < 10M < 100M > 100M
Velocity (time) Hours Minutes Real-time (Sub-Seconds)
Variety/Complexity (# columns) < 100 < 1000 > 1000