7. Unified BI

For a large corporation to have a reliable business intelligence (BI) initiative it needs to have a way of integrating information from various parts of the company. The company needs what is called a “single version of the truth.” Various information architectures have been designed to support the idea of a unified BI, however, none have proven to be a real solution to a growing problem. The question to be answered in this post is:

How can a company unify its various data warehouses, CRM databases, and other elements of a BI environment?

There is only one solution and, luckily, it is a simple one. The unifying principle of BI is to have universal object identifiers. The company unifies its BI environment by providing unique identities for each and every significant object in the BI environment. Allow the various business units within the company to evolve their own data mart with their own architectures as long as they provide the same unique, company-wide, identification for each object in their database.

By objects, I mean those identifiable entities that are relevant to the business, including the customers, business units, and products that are involved in the day-to-day transactions of business. Different departments of the company, each trying to satisfy its own business needs, may keep different data about these business objects, but the data can be integrated and shared among the departments as long as the customers, business units, and products have a common company-wide identity throughout the company.

The primary class of objects that is of interest to the company’s business, however, is the transaction itself. Each commercial transaction, representing the sale of item, the payment of an expense, or the trading of assets, is an object that must have a unique identity, produced as it occurs to the business. With each transaction being assigned a unique identifier at the time of its creation (i.e. a point of sale), the information in a marketing data warehouse can be integrated and analyzed with related information in the finance, sales, and customer relations departments.

Universal object identifiers are the key to a unified BI, providing the company with a version of the facts that transcends the boundaries of various departmental boundaries. From a single identity of for each fact comes a single version of the truth.

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.

2. The Information Age

The logical instrument which Aristotle used for the analysis of actual fact into more abstract elements was that of classification into species and genera.
Alfred North Whitehead

Financial analysis is an information-based undertaking. It determines the value of a company by the data that measures the company’s assets, income, and cash flow. The quality of the financial analysis is a product of the quantity, quality, and timeliness of the information that is used in making the analysis. Better and more current information makes better financial analysis possible and leads to better investment decisions.

In the age of computers, the volume of data and the tools used to analyze data have grown enormously. Companies, both large and small, are now capable of generating huge amounts of data from automated cash registers and scanning devices and inexpensive databases are available that allow that data to be searched, selected, and summed into digestible morsels of wisdom. The future of financial analysis lies in the ability to analyze the large volumes of data that are now being produced by the ubiquitous personal computer. See The Tao of Financial Information.