17 Sep 2015
by Chris Roe
Breaking down your data into easily analysed dimensions or
interesting segments can provide you and your team with
invaluable insights into your data. This blog post explores how
you can get the most out of your data in this way using the
powerful analytical “Cube” tool within FastStats Discoverer.
What are Cubes?
Cubes are a powerful analytical tool for gaining insight from
your data. Breaking down the data by multiple dimensions gives
the analyst a way of looking for interesting segments. Using date
dimensions allows us to see what is happening over time. Adding
different measures based on numeric transactional values can give
insight into valuable segments in the data.
Examples of insights provided...
There are some questions we could ask which require producing
figures which are calculated from those returned by the analysis.
- What is the difference in the average holiday spend compared
to this quarter last year?
- How is the relative popularity of destinations to each other
changing over time?
- What is the sum of our revenue for the year to date?
In the cube below, we are looking at the different destinations
visited over a 2 year period banded into quarters. It shows the
number of people in each cell and also the average cost of their
bookings for that destination/quarter combination.
To answer question (1) above we could set the following
definition to compare the Mean(Cost) to value from the year
Then the results on the cube would look like this:
From the portion of the cube that we can see above it is clear
that for the four visible destinations our average holiday spend
in each quarter of 2012 was better than in 2011 (with only one
Questions (2) and (3) posed earlier in this blog can be answered
in a similar way by defining a measure that is calculated from
information that already exists within the cube.
How will this capability be included in the latest release of
For the Q3 2015 release of Discoverer we have added functionality
to expand the analytical capabilities of cubes with a number of
new ‘Calculated Measures’.
The following new functions are available:
- Running – cumulative function across a particular dimension
on a measure.
- Rolling – smoothing fluctuations in the data by calculating a
function across the last few values.
- Period To Date – work out the value of a function across a
date dimension from the start of a date period.
- Comparison – compare the current value to a previous value on
a dimension. For a date dimension this can compare to a relative
- Rank – compare the relative position of a category amongst
other categories across a dimension and return as a number.
- Percentile – compare the relative position of a category
amongst other categories across a dimension and return as a
percentage showing the number of values lower than or the same as
the current one.
Furthermore, these useful calculations can be chained together to
create more advanced results - for instance we could work out the
difference between the Sum of the Year to Date against the Sum of
the Previous Year To Date.
These new functions will add a lot of power for the analyst to be
able to investigate questions that were not previously possible
- Breaking down the data by multiple dimensions enables
in-depth analysis of interesting segments.
- Data calculations can be chained together to create even more
- The new functions of Discoverer will enable you to obtain an
ever deeper level of analysis from your data.
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