Oracle Analytics Cloud, a data platform

In the previous post we talked about the possibilities that Autonomous Datawarehouse offers us to create advanced analytics algorithms. In that same post we worked with a small dataset and use the Data Mining APIs to generate a forecast or sales forecast. You can read the post here.

 

In this post we will take advantage of this data to show you some of the features that  Oracle Analytics Cloud platform offers us. I want to emphasize the word “platform.” Today the market is saturated with data visualization tools, some even free that offer great capabilities showing data: tables, charts, KPIs, etc.… But is that really what we are looking for? What differentiates these tools from Microsoft Excel? Really not too much. That is why I am interested in the platform concept introduced by Oracle Analytics Cloud, OAC from now on. OAC offer us all those self-service capabilities that all other tools already offer us, but it goes a step further by allowing us to work with the data: import, replicate, transform and persist data. OAC offers the end user the ability to become independent from IT departments and be able to generate and work with the data independently.

 

Let’s show an example of all this. In our Autonomous Database we have the sales table of an office supply chain (SALES) and the table where we generate sales forecast for the subsequent 12 months. Apart from these two tables, the marketing department, which wants to perform an analysis of his customers, has an Excel document with data from them.

 

Let’s see how to do this. The first thing we will do is create the 3 DataSet. It consists of creating the connections and importing that data to the platform. In the following screenshots you can see that the process is simple.

 

 

 

 

 

 

Once all 3 data sources have been imported, it is time to join them to work together. For this, we create a Data Flow starting from the SALES table:

 

 

Now we add the customer information that we have in Excel, whose relationship comes by the customer ID. Finally, we save the result, which may already be within the platform or in a database table that we provide:

 

 

 

We have already created our dataflow by mixing 2 data sources. It is time for our marketing partner to start creating his analysis, for this he will add the result of the DataFlow that we just created and the sales forecast that we already have to the project.

 

 

From this moment, different analyzes will be created using these self-service functionalities that OAC offers us.

 

 

Most interesting thing about this small exercise is not the self-service capabilities, which are already a commodity today, but that OAC goes further and offers us a data platform, with this and many other interesting features.

 

 

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