Frustration, anger, a deep-seated desire to throw it away - these are just some of the feelings you might encounter when you're reminded of a Rubik's cube.
Nonetheless, interest in solving Rubik's cubes resulted in many guides and tutorials. Human nature, and its boundless drive for conquest, I suppose. It's impressive when you think that a Rubik's cube can have 43,252,003,274,489,856,000 combinations and yet only one correct solution. This is why solving them aren't just interesting for humans but also robots.
It's no surprise that the Rubik's cube is a synonym for complexity. But in Business Intelligence, it's the opposite - the cube becomes a metaphor for decoding large amounts of data and creating forecasts. To Business Intelligence insiders, thoughts of a Rubik's cube is followed by another word - OLAP. (Find out how Business Intelligence fits in the retail industry perfectly in our free ebook: Business Intelligence in Retail)
OLAP stands for On-Line Analytical Processing. So what is OLAP Cube? It's a complex and interactive set of techniques used to analyze of large amounts of data. OLAP allows you to achieve this through efficient data research. It also enables you to make sophisticated requests for multidimensional analyses - new data relationships and correlations are established and revealed. It's Business Intelligence all ready to be unboxed.
A cube is used to represent the many capabilities of OLAP. You can think of it as an extension of a spreadsheet. Any data-representing cell in a spreadsheet only has two dimesions (represented by the column and row titles). But in an OLAP cube, the spreadsheet is just a surface of the cube. The cell can correspond to other data (if only their relationships can be established). With OLAP, instead of data that only shows you two dimensions, it can have up to an infinite number of them.
If you think of it, the OLAP cube isn't just an ordinary cube but a hypercube! Organizing your data this way means access to different functions, each with a specific objective:
- Pivoting: You rotate the cube view to see its other faces and see additional dimensions to the data you are viewing.
- Slicing: Literally, "slicing" the cube on a specific value of a dimension to obtain a subset of the data.
- Dicing: Produces a smaller "subcube" by eliminating specific values of multiple dimensions. Basically, you're filtering the information of interest.
- Drill-down / up: Allows you to browse data in two ways: you can "dig deep" towards increasingly detailed data or "going up" and getting a wider view of the collective groups of data.
- Roll-up: Data is summarized based on a dimension.
As you can see, the cube is the basis of OLAP analysis. It provides an efficient interface for data visualization. Its strength lies in the many possible configurations: just "turn" one of the faces of the cube and you have a completely different point of view of the exact same set of data.
Decision-making processes in organizations need to be supported by relevant information. Analyzing data through an OLAP cube means giving decision-makers less time-consuming and innovative analysis. Sometimes, its easy to forget that the same figure can have so much more insights when viewed from a different angle.
Since a cube can give us many dimensions, the combinations are almost endless. Each combination corresponds to a specific analysis that can be valuable to management and business. It's possible that all the answers businesses seek are contained within a single configuration and only needs correct handling of the OLAP cube to be revealed.
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