The Analytics Gap

The Gap Between the Amount of Data Collected and the Amount of Data that Can Be Analyzed with Current Tools

Every enterprise relies on data to make business decisions. The business analytics software market is large – over $US30B – and growing at a rapid pace as analytics tops the list of CIO priorities year after year. The amount of data that enterprises collect is growing rapidly too – 60% a year, or more thanks to the biannual doubling of computing power known as Moore’s Law, the rapidly growing adoption of multimedia data formats and the declining unit costs of physical storage.

Yet the data that can be made readily available for business analytics, so-called structured data, is a mere fraction of the data collected. It is a strange reality that at least 85% of enterprise data – e.g., e-mail, Web logs, XML and other compound electronic documents – is “unstructured” and not readily accessible from relational databases, the gold standard for analyzing data. Arguably, at least 85% of business analytics investment is spent to analyze less than 15% of enterprise data. Effectively, therefore, enterprise data is still largely untapped.

Insufficiency of the Relational Data Model

The Relational Data Model was a breakthrough when it was developed 40 years ago. It allowed a data application (e.g., a sales transaction) to be represented in a conceptual way inside a database – a group of two-dimensional tables (rows and columns) integrated from whatever data sources were necessary. When performance became a problem during input or output, table structures were adjusted or server settings tuned, or both. Unfortunately, this application dependence led to rampant silos of data because it is extremely difficult to design a single database to handle multiple applications with acceptable performance. Furthermore, unstructured data didn’t exist when the Relational Data Model was created, and it has proven to be ineffective for managing unstructured data.

Analyzing enormous and rapidly increasing amounts of data, from different sources, in different formats, in time to make a difference in business operations, is practically impossible using conventional relational databases. Consequently, as data is collected over time, the gap between the amount of data collected and the amount of data that can be analyzed continues to increase; our technical ability to collect data outpaces our ability to derive business intelligence from it. This is the Analytics Gap.

Closing the Analytics Gap

To close the Analytics Gap requires a radically different technology that can harness unstructured and structured data wherever it exists, break the limits of application dependence, and enable backward compatibility with relational tools and techniques to take advantage of prior investments. Patented ALGEBRAIX® technology is just such a technology; it closes the Analytics Gap by modeling all data mathematically and by providing high-performance access to any enterprise data regardless of its structure or location.

Learn more about ALGEBRAIX technology.