Oracle Analytics Cloud is a powerful tool that can help you make sense of large amounts of data. With its easy-to-use interface, you can quickly and easily view, analyze, and transform data. One of the best things about Oracle Analytics Cloud is that it’s constantly being updated with new features and enhancements. So, you can still benefit from using it even if you’re not a data analyst. If you’re ready to get started, read on for our guide to transforming and enriching data in Oracle Analytics Cloud.

Suppose you want to successfully transform and enrich data in Oracle Analytics Cloud (OAC). In that case, these are a few key steps: First, it is essential to understand the data and how it is currently stored. Once this is understood, the data can be properly formatted for OAC. Finally, your data must be enriched with the appropriate context to provide the most insights.

Understand the data

The first step to properly transforming and enriching data in OAC is understanding the data, including where it comes from, what it represents, and how it is currently stored. With this understanding, the data can be appropriately formatted for OAC.

Format the data

Once the data is understood, it can be formatted for OAC by ensuring that the data is in the correct format and structure for OAC. Additionally, any necessary data cleansing should be performed at this stage.

Enrich the data

The final step is to enrich the data with the appropriate context, including relevant metadata, such as geographical data, to the data set. It will help OAC to provide more insights and make better recommendations.

Data transformation through an OAC interface

The Data Transformation interface in Oracle Analytics Cloud (OAC) is a web-based interface that lets you quickly and easily transform data from various sources into the format required for OAC. The interface provides a drag-and-drop option for mapping source data to OAC data fields and also supports SQL and JavaScript for more complex data transformation. The Data Transformation interface is a vital part of the OAC platform and enables you to quickly and easily load data into OAC for analysis.

You can easily convert data from one format to another, change data types, and even merge multiple data sources into a single data set. Select the data source you wish to transform, and then select the transformation you want to perform. You can specify the transformation details, such as the data type and the target data set. Once you have determined the transformation, click the “Apply” button, and the transformation will be applied to your data.

If you need to transform multiple data sources, you can use the “Batch Transformation” feature to quickly and easily transform all of your data sources simultaneously. To use this feature, select the data sources you wish to transform and select the “Batch Transformation” option. You can also specify the transformation you want to perform, and the transformation will be applied to all of your selected data sources. Data Transformation in Oracle Analytics Cloud can save you time and effort when transforming your data.

Read Also: Oracle Analytics Cloud (OAC): Your Questions, Answered!

Steps to transform data in OAC

The data transformation interface is divided into two sections: the left panel is where you select the source and target tables, and the right panel is where you specify the transformation rules.

To begin, select the source and target tables. The source table is the table that contains the data that you want to transform. The target table is the table that will hold the transformed data. In our example, we will use a customer table and an order table.

Next, specify the transformation rules. There are three transformation rules: column mapping, value mapping, and SQL transformation.

  • Column mapping rules specify how columns in the source table should be mapped to columns in the target table.
  • Value mapping rules specify how values in the source table should be mapped to values in the target table.
  • SQL transformation rules allow you to specify a SQL query that will be used to transform the data.

In our example, we will use a column mapping rule to map the customer_id column in the source table to the customer_id column in the target table. We’ll also use a value mapping rule to map the customer_name column in the source table to the customer_name column in the target table. We will use a SQL transformation rule to transform the data in the order table. Once you’ve specified the transformation rules, click the “Apply” button to apply the rules and transform the data.

Oracle even provides helpful recommendations to transform your data quickly. Adding data to a workbook can be profiled, transforming and enriching the column based on recommendations provided. Oracle offers the following recommendations for point-and-click transformations:

  • Geographic coordinates to enrich your data with latitude and longitude for cities or zip codes.
  • Change data properties. For example, you can quickly transform the data property if you want your numerical values to be treated as an attribute instead of a measure.
  • Create visualizations and graphs of your data sets.
  • Mask sensitive and personal information in your data either fully or partially.

How to enrich data in Oracle Analytics Cloud

Enriching data is critical to getting the most out of Oracle Analytics Cloud. There are many ways to enrich data, but it can be challenging to know where to start. Here’s how you can use Oracle Analytics Cloud to transform and enrich data to use actionable insights for better decision-making.

First, you need to understand the different types of data that exist. Then, you can think about how you want to transform and enrich that data. Some of the most common approaches to enriching data include:

  • Adding new columns: You can add new columns to existing data sets to add more information. For example, you might add a column for customer location or product category.
  • Joining data sets: Joining data sets allows you to combine information from multiple sources. It is beneficial when you have complementary information in different data sets. For example, you might join a customer list with purchase history to better understand customer behavior.
  • Pivoting data: Pivoting data allows you to reorganize information so it is easier to analyze. For example, you might pivot a sales data set by-product to understand better which products are selling well.

These are just a few of the many ways that you can enrich data. Here’s an example of enriching customer data with information from an external source.

We’ll first need to create a new table containing external data to do this. We’ll name this table “external_customer_data”. Next, we’ll need to specify a column mapping rule that maps the customer_id column in the source table to the customer_id column in the target table. Finally, we’ll need to specify a SQL transformation rule that queries the external data and inserts it into the target table. Once you’ve specified the transformation rules, click the “Apply” button to apply the rules and transform the data.

Wrapping up

Data transformation and enrichment are critical to any data analytics solution. OAC provides a comprehensive set of tools to support these activities. The Transformation and Enrichment feature offers an easy-to-use interface. Also, it includes several pre-built transformation and enrichment activities that can be used to quickly and easily optimize data. The basic steps in this article should ensure that your data is of the highest quality possible and that it provides insights that will help you improve your business.

Related blogs:

Elevate your business with Oracle Analytics Cloud-driven data-rich insights

Oracle Integration Cloud (OIC): Evolution and Best Practices

The power of Oracle Integration Cloud’s process automation with RPA

Explore new capabilities in Oracle Analytics Cloud Release 5.9

Latest posts by Abhijit Ramachandran (see all)