Cube Analysis –or Cube-based BI tools provide simple slice-and-dice
analytical capabilities to business managers. This includes:
- OLAP Functionality Against a Data Subset
- Pre-defined Analytical Views
- Slice and Dice Analysis
Prebuilt Cube Analysis allows business users to determine the root
causes underlying data in reports, without the need to have skills
for full ad hoc investigation of the databases.
Appropriate tools are a critical factor in any Performance Management
System and using the right tool for the right purpose, is a key
enabler. Cube Analysis is ideal for basic analysis that can be anticipated
in advance, for example, analysis of of sales by region for certain
time periods, or the analysis of sales by product and by salesperson.
After reviewing initial reports, a sales manager may identify an
issue, and use predefined cubes such as those mentioned above to
investigate the root cause of the issue.
Root cause is typically found after referencing several pre-built
cubes, with one or two cubes providing the context for a primary
Once the cause of the problem is identified, most tools allow a
link to the analysis cube can be sent to parties for further review
By viewing a series of ‘report views’, using standard
OLAP features [page-by, pivot, sort, filter, and drill up/down]
a Cube of highly interrelated data can be viewed by various attributes
defined in the cube [ stores, products, customers, suppliers] with
any metrics in the cube [sales, profit, units, age], thereby creating
various 2-dimensional views [slices].
Types of Cube Databases
Most OLAP vendors use custom-made proprietary cube databases [Multidimensional
OLAP or MOLAP]. These cube databases have very small data
capacities – less than 0.01% of real relational databases
– however they are suited to the subsets required in departmental
BI applications which are typically limited to between 10MB and
100MB of detailed and summary data.
Once a company needs to deploy hundreds of overlapping cube databases
all the combinations of data subsets, summarization levels, and
security privileges for different user groups
across multiple applications “cube farms” resulted,
creating a drain on IT resources.
An alternate approach was developed by modeling the relational
database as a “virtual multidimensional cube” with a
technique known as Relational OLAP or ROLAP.
This enables OLAP against an entire relational database, without
limiting what what data can be analyzed. The trade off is in slower
response-times and overloading users with too many options or too
much data, rather than a simple subset.
MicroStrategy resolved these tradeoffs with its Intelligent Cubes,
providing functionality of small-scale MOLAP cubes with significant
enhancements available only with a ROLAP underlying architecture.
This overcame the speed constraints and allowed automatic ‘on
the fly’ creation of cubes and manipulation of functionality
such as filters.
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