4. Virtual Data

The smallest operations can now afford financial control programs that account
for their finances with greater speed and sophistication that even the largest
corporations could have achieved through their production hierarchies a few
decades ago.
James Dale Davidson and Lord William Rees-Mogg

Business intelligence will become increasingly based upon “virtual data” as we proceed into the twenty-first century. Virtual data is the information that is produced by a computer from more primitive data stored in the computers’ databases.

Examples of powerful information that will soon be virtual data are the monetary amounts that move and shake our financial markets. Earnings, income, and liquidity are the critical numbers that give us a measure of the success and economic viability of a company. These numbers, typically represented as a few data points for each quarter of a year of business, are really the sums of millions of individual transactions that the company has incurred during that time period. They have been traditionally computed and stored each quarter and become the primary financial data of the company as the records of the transactions themselves are relegated to the information background.

However, these pieces of summary data will become virtual, or produced on demand, rather than primary data, for the simple reason that the production and reproduction of the data is extremely cheap and accurate with the advent of the modern computer.

The product of arithmetic operations, particularly where large volumes of data are concerned, has historically been turned into stored data because of the cost involved in manually performing these operations. However, the arithmetic that humans have produced laboriously and erratically can now be performed perfectly and effortlessly by a computer.

The inexpensiveness of performing arithmetic on a computer is the essential factor in determining when information becomes virtual as opposed to being kept as stored data. It is just a matter of fundamental economics – if it costs nothing to reproduce the data, it has no value as stored information and can just as well be reproduced on demand.

This virtualization of critical data is the key behind the increasing power of business intelligence and the star schema architecture that defines a data warehouse. The arithmetic sums that are produced by an OLAP data warehouse are produced on the fly from underlying primitive information (typically, atomic financial transactions). By “virtualizing” these arithmetic sums from the underlying atomic data, we are able to use the same underlying atomic data to produce other arithmetic sums. This is basically how we use a data warehouse: we slice the data along combinations of dimensions to produce, for example, not only earnings, but earnings by business unit, store type, or customer demographics.

We are now in the era of the ubiquitous computation engine and the cost of performing computations is approaching zero, minimizing the need to store and save the result and maximizing the value of primitive atomic data. See Banking the Past.