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Online analytical processing (OLAP) is a high-level concept that describes a category of tools that aid in the analysis multi-dimentional queries.
OLAP came about because of the tremendous complexity and sheer growth associated with business data during the 1970s as the volume and type of information became too heavy for adequate analysis through simple structured query language (SQL) queries.
Traditional SQL’s data-comparison ability is limited. For example, SQL can manage queries, such as a list of sales agents, versus sales volume histories. However, with larger data volumes, it can be overwhelming just to use SQL and tough to translate data into information that easily facilitates decision making. It is difficult to answer certain questions in SQL, such as why product sales are higher mid-month, or why female sales agents consistently outsell their male counterparts during the summer.
Recognizing that relational databases have inherent limitations, manufacturers created new ways to represent complex data relationships and analyze results to discern hidden and previously unknown patterns and trends.
A case study about OLAP’s potential grew from one large retailer’s use of OLAP tools for data mining. This retailer noticed that late-night baby product purchases correlated with increased late-night beer purchases. Initially, this seemed like a coincidence, but deeper customer analysis revealed that late-night customers were mostly young fathers in their mid to late twenties or early thirties - a demographic also associated with late-night disposable income. Based on this data, retailers began cross merchandising baby products and beer, and combined sales for both product lines skyrocketed.
This case study proved how OLAP equips researchers to delve and uncover data relationships between seemingly unrelated events and trends, thus enhancing business decision making.