Data In, Insights Out: Loading and Enriching Your Dataset in Oracle Analytics

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Author: Philip Godfrey

Oracle Analytics Cloud is a powerful tool that enables organizations to make data-driven decisions by providing advanced analytics capabilities.

You are able to work with data that exists in the database, or another common way to get started with Oracle Analytics Cloud, is by loading your own dataset into the platform.

In this blog post, I will walk you through the process of loading a CSV dataset into Oracle Analytics Cloud and utilizing enrichments to enhance the value of your data.

Loading Data into OAC

Select your dataset, which could be a local csv or xlsx file for example. In my case, this will be a Disneyland Review document that has been sourced from openly available data. 


Once this has been selected, you will be presented with an overview of the dataset, and you can confirm the data is as you would expect.

From this quick view, I can see Review ID and Rating are Measures, with the remaining fields Year_Month, Reviewer_Location and Review_Text are Attributes

 
What are the key differences between Attributes and Measures?

  • Attributes are typically categorical in nature, while measures are numerical.
  • Attributes are used to describe the "what", while measures are used to measure the "how much" or "how many".

As Rating is a numerical value with a specific scale (1 – 5) as it represents a quantitative measure, we will keep this as a Measure.


Enriching your dataset with Oracle Recommendations

Oracle Analytics provides additional advanced analytic capabilities with the Recommendations feature.

This provides a list of in-built data recommendations which can enrich our dataset, this can include breaking out dates into days / months / quarters / weekends etc or adding geo-spatial features such as Long and Lat coordinates.


In our example, we will utilize the Reviewer Location with capital, which provides additional context which may be useful when presenting the analytics.


Some best practices to keep in mind when loading and enriching your dataset in OAC:

  • Use consistent column names and data types across all datasets.
  • Define clear data quality rules to ensure data accuracy.
  • Use data profiling to identify missing values and anomalies.
  • Apply data transformation and aggregation functions carefully to avoid losing valuable information.

Now we’re happy with the dataset, we’ll go ahead and save it once we provide a name and a description.


This will then appear in our Data tab from the hamburger menu, ready to be utilized within OAC via a workbook canvas. But before we do that, we want to create a Data Flow, based on this dataset.

Look out for the next blog in the series to see how we utilise this dataset in a Data Flow and apply a Data Preparation step in Analyze Sentiment. 



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