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Welcome to the Beyond Blog

As you'd expect from the winners of the Specialized Partner of the Year: Business Analytics at the Oracle UKI Specialized Partner Awards 2014, Beyond work with leading edge BI Applications primarily within the UK Public Sector. We intend to share some of our ideas and discoveries via our blog and hopefully enrich the wider discussion surrounding Oracle Business Intelligence and driving improved insight for customers

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A while back I created a post describing how to produce an organization chart in Oracle APEX using Google visualizations. If you didn't catch that then go and take a look here first before reading on as it will provide the background reading to this post.

So in this post I am going to demo how we can do this in OBIEE - and it's actually quite easy because OBIEE has already done a lot of the work for us.

First we need a level based hierarchy (or even just a representation of a hierarchy as levels across columns). This is how all BI Applications hierarchies are implemented, for example the organization and position hierarchies. I am going to use SampleApp with the "Sample Sales"."Offices" hierarchy.

Columns

Then we simply select all the columns in our hierarchy into a simple analytic. As we have multiple top level nodes I have applied a filter to restrict to just one company, however this isn't necessary - if you have multiple top level nodes then you simply get multiple trees.

Analytic

If we use the default Table view then we see something like this. Note I have changed the column order in this view simply to make the hierarchy structure clearer.

Table Results

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DV Desktop v2 has been on general release now for a week or so and I highly recommend the following Oracle video to see some of the new functionality

https://www.youtube.com/user/OracleBITechDemos/videos

There's a lot to like about this release - the hugely enhanced connectivity and workflow for example, but some things I haven't seen many people talking about yet is the SDK or the ability to use plugins to add new content; here's a quick way of doing this.

1) Goto the Oracle BI Public store

b2ap3_thumbnail_dial2.png

2) Select a plugin and download it.  Note that you DO NOT UNZIP the file and also you will need to create the /plugin directory.  By default on windows your %LOCALAPPDATA% directory is hidden so you'll probably need to unhide it to find it!

b2ap3_thumbnail_dial3.png

3) Volia - the new visualisation is available for use; here I have tweaked the setup of the dials and used them in my application.

b2ap3_thumbnail_dial1.png

...any questions ... please ask.

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We've been working with a number of customers who want to see context specific charts/graphs displayed when the mouse rolls over values in a table, rather than having to drill.  In order to show an example of this rather slick approach we have created a 30 second video as a demonstration    

Please have look here  https://www.youtube.com/watch?v=bZHzcMmLkLw

 

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I wanted to write this as an introduction to combining data from multiple facts/subject areas into a single analytic. The post is aimed primarily at end-users as there are developer techniques we can use to circumvent some of the restrictions described below.
Let us first refresh ourselves with a subject area actually is. In its most basic form it is simply a fact with associated dimensions. Consider the following simplified example for a financial fact.

Financial Star Schema

So we can easily report on financial transactions by any of the four dimensions listed.
Now let's suppose we have a completely separate subject area based on some HR Salary information.

Human Resources Star Schema

Again, we can use that star in isolation however we wish. However... what if our user decides that they would like to report on the monthly spend alongside the monthly salary cost?
Without considering any dimensions this works fine - we can simply include the measure from each fact. The difficulty comes when we want to include dimensions - the key rule being this... 
You can only report on measures from multiple facts where all dimensions that are used in the analytic are shared.
So let's look at those two facts together.

Combined Star Schema

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Oracle BI 12c (which is compatible with both the "Oracle Data Integrator" versions of BI Applications from 11.1.1.9+ and also the "Informatica" version 7.9.6.4 ) comes with many improvements over previous releases.  One I want to look at here is that of "Advanced Analytics".  There are a number of new analytic features that are built into the product based on the "R" language that allow us to simply perform analysis such as Forecasting, detect Outliers, group related items into clusters, add trendlines, etc.   
For example, there is a Forecasting function which allows quite sophisticated forecasting via a number of models such as ARIMA ( Autoregressive Integrated Moving Average ) and also ETS ( Error, Trend, Seasonal ). 
Below we show sales data for a couple of years (shown in blue) and use the Forecasting function to forecast spend for the next two years (shown in green) using the ARIMA function.


 
Toggling this to use the ETS methodology we see a slightly different forecast as we'd expect via a different model, but what i'm really highlighting is that there are of course a number of models that allow us to forecast by utilising prior data and various sensitive parameters that allow us to create a scenario that best fits the purpose to which were are looking to utilise it ( e.g. the forecasting of budgeted spend, the forecasting of absenteeism, etc ).


 Identification of statistical outliers is also very important.  I'll leave the discussion of what determines an outlier within a specific dataset for now as concepts such as "Mahalanobis distance" are somewhat statistical in nature, but as an example here we use the new Outlier function to process billed quantity for a specific product category and highlight any outlier in both a table and also in a scatter chart. 


 
Outliers would be very useful in a local authority for numerous reasons, perhaps such as identification of P2P data to ringfence customers that have unusual payment patterns or employees with interesting absences.
As a final example of just some of the different types of analysis that can be performed, here's a Trendline within some payment data.  We can plot the complex payments over a number of months and then apply a Trendline function so that we can clearly see the direction of travel. 


 
Here we can see that it is gently rising, which would be positive.  This again would be useful in a local authority to see that reduction in absenteeism is heading in the direction we would want or the speed at which SME's are paid is improving. 


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