Advanced Analytics in Oracle Data Visualisation Desktop/Cloud Service (1)

It's been quite a while since my last post as I've been engaged in some interesting project around the world. Now I'm back and I hope I will catch up with my posts on this blog.

In the last couple of month Oracle has significantly improved it's data visualisation flagship products Data Visualisation Desktop and Data Visualisation Cloud Service.

One of the topics I particularly like with the new versions of these two products - by the way they have more or less same functionality, therefore I will not specifically focus on any of the two - is it's advanced analytics functionality.

There are "out-of-the-box" functionalities that are ready to be used when visualising data. For example, when you are analysing your time series data, only by clicking on a button, you can add a trend line to your graph, and with another click you can add forecast as well.

In this post I'm focusing on available Advanced Analytics function that are "out-of-the-box". In the next two posts I will explore how Analytical functions can be used and how you can use custom R functions.

"Out-of-the-box" advanced analytics

Oracle Data Visualisation Desktop has the following "out-of-the-box" advanced analytics built in:
  • Reference Line
  • Trand Line
  • Forecast
  • Clusters
  • Outliers
The way how do you use these analytics and add them to your visualisations is the same for all of them. 


Click on Analytics icon on the left to switch to Analytics panel. Choose and drag & drop selected function to the canvas.


When you release your left-mouse button, selected function displays:


With Properties you can set detailed properties of an analytical function. As you can see, to apply these analytics to the visualisation, there is no coding required. Just Drag&drop and set properties if needed.

Let's take a look at individual Analytics now.

Reference Line

Reference Line is a line that represents a simple descriptive statistics like Minimum, Maximum, Average and Median. There is another option to present a constant line on the graph as well. 


Additionally, users have an option to choose a method how he would like the reference line to be displayed - line or band. When Band is selected, then minimum and maximum reference lines for the band have to be selected.


Trend Line

Trend line is a line that fits a linear, exponential or polynomial model and returns the fitted values which are then displayed on the visualisation.


There are 3 different methods of how data fits to actual values and display a trend:
  • Linear,
  • Exponential and
  • Polynomial.
The example above is showing a polynomial trend line with degree of 3. The grey area around trend line is showing confidence interval of 95%. You have an option to choose among 90%, 95% and 99% confidence interval. You can also decide to switch display confidence interval off.

Forecast

With Forecast you can predict values for the next n future periods. Number of n next periods  can be set as required. 


Clusters

With Clusters Analytical function you can collect a set of records into groups based on one or more input expressions. There are two algorithms that can be used for clustering, K-Means and Hierarchical Clustering.


With Properties you can set the number of clusters, ie. marketing segments. On the chart above it is clearly seen how clusters are created and what are the rules for it.

Outliers

Outliers is a function that classifies a record as outlier based on one or more input expressions using K-Means or Hierarchical Clustering or Multi-Variate Outlier detection algorithms.

Sometimes it is required to identify outliers and exclude them from the analysis. For example if you are working on some classification algorithm which would predict withdraws from ATM machine. 


Conclusion

As you can see, Data Visualisation Desktop has built-in Analytics on "click" that can be deployed without any coding, just by dragging & dropping. In the next two posts, I will explain how to use Analytical Functions and how you can use R scripts with Data Visualisation Desktop.

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