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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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Oracle have recently released Data Visualization Desktop (http://www.oracle.com/technetwork/middleware/oracle-data-visualization/downloads/oracle-data-visualization-desktop-2938957.html) which “gives decision-makers their own personal desktop application to access, explore, blend, and share data visualizations”.  If you already know Oracle Data Visualization in the cloud ( aka DVCS ) then this is pretty much a desktop version of that but can feed data from your BI analytics, easy access to data in on-premise databases, etc. 

So, if your organisation is not ready for the cloud or you have data that can’t reside in the cloud or perhaps you just want a private tool to perform analysis from data held in an on-premise E-Business system, then DV Desktop is the tool for you.

1st image
 
Let me show you how easy it is to install

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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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With a lot of E-Business Suite customers looking to upgrade to R12.2.x within the near future (if they haven't already), I thought it might be useful to illustrate some of the key differences. I'll spread this out over a number of different posts, however for today I am going to look at database tables. Prior to Release 12.2, the following structure was used for E-Business Suite tables.

Old Table Structure

So we have tables created within their application owning schema which are then granted up to the APPS user. Typically the process for doing this (for a custom development) would be:

-- As your custom application user, i.e. XXJK.
Create Table xxjk_demo (
  id       Number,
  val      Varchar2(100),
  a_field  Varchar2(10)
);

Grant All On xxjk_demo To Apps With Grant Option;

-- As Apps
Create Synonym xxjk_demo For xxjk.xxjk_demo;
-- Apps grants out any further privileges required.

However... this changes with R12.2 due to Online Patching. We no longer have the simple model above, but something (only slightly) more complicated.

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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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