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THE DATA ANALYTICS
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Competing on Analytics

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The “Database Analytics” field encompasses a spectrum of applications and the systems deployed by enterprises are variously referred to as Business Intelligence (BI) systems, Decision Support Systems (DSS), Data Warehouses (DW), and Data Marts (DM). The goal of analytics is to extract information useful to the business. This is achieved by collating operational data with historical summaries and third party databases and performing analytics on this dataset. The analytics and the resulting output may be broadly categorized as falling into two main groups - Operational Reporting and Ad-Hoc Data Exploration.
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USAGE Operational efficiency management, Performance monitoring
USAGE Strategic campaign management, Pattern discovery
USERS Business line managers
USERS Skilled analysts, statisticians
OWNERS Enterprise IT
OWNERS Departmental group leaders
As summarized above, these two groups have significant differences in the usage model, the end-users of the systems and the owners/buyers of such systems.

Historically, the vast majority of enterprise spending has been directed towards “traditional BI” – the Operational Reporting group of applications. In recent years the focus has		 shifted towards ad-hoc data exploration. The reasons for this shift are simple. Traditional BI—canned queries generating operational reports—are now well-established in all enterprises, and have been so for about a decade. This level of analytics is the assumed baseline for all enterprises, a bare necessity for survival. Beyond survival, however, enterprises need to compete strategically, and “Competing on Analytics” is the goal today. It is now possible to create and maintain a sustainable strategic advantage through analytics by discovering patterns hidden within the data. Pattern discovery implies unconstrained data exploration in an ad-hoc manner.