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Using Oracle Analytics with the Autonomous Data Warehouse


For the airline industry, flight delays are a serious problem. This problem ultimately cripples customer satisfaction and retention, wreaks havoc on schedules, and reduces revenue. What if airlines could move past expensive, time-consuming, legacy IT approaches? This could help them harness their data to better analyze, understand, and mitigate flight delays. Oracle Analytics and Oracle Autonomous Data Warehouse can help them do exactly that.

In a recent Oracle YouTube video, we walk through an example of how an Airline Data Analyst uses the Autonomous Data Warehouse to quickly provision a database and easily upload data for analysis without specialized DBA skills. While this example relates to the airline industry, the same idea could be applied to other industries relatively the same.

The Autonomous Data Warehouse will provide the Data Analyst with immediate answers to better understand flight delays so she can provide executives with insights that they need to inform critical business decisions.

Autonomous Data Warehouse + Oracle Analytics

The first step of leveraging Autonomous Data Warehouse and Oracle Analytics is to log into Oracle Cloud. After providing a few details like the database name, password, and initial size, you can start leveraging the Autonomous Data Warehouse in just a few minutes.

With the database ready to go, the Data Analyst can download the Credentials Wallet. Next, she will load the data into Object Storage—creating a new, custom named bucket. Then, she will upload her data file directly into Oracle Analytics. She will then complete the setup and has uploaded one month of data to analyze flight delays. However, with Oracle Autonomous Data Warehouse scalability, she could easily upload several months or years of data for analysis if she wanted to.

Now the Data Analyst can leverage Oracle Analytics to craft compelling views of her data. To do so, she will create a new connection. Oracle Analytics can connect to dozens of sources, including the Oracle Autonomous Data Warehouse Cloud—which is the connection type that she will select. Once she has entered the relevant details, she will save the connection.

She can preview her entire data set, but to make sense of over half a million rows of data, she will need to click “Create Project” to start visualizing. First, she wants to better understand flight delay types. By dragging data collections to the canvas, she can easily compare different data sets in a visual format. She can also quickly customize her graph with a title and axis labels. This lets her quickly create more insightful visualizations.

The Data Analyst will be able to create multiple canvases, compare different data sets, select different graph types, choose her time frame, custom colors for visual appeal, and add trend lines to help clearly show the correlation between flights and delays within the last month. By repeating this process, she can create limitless visualizations like:

  • Delays by state
  • Delays by airline
  • A custom count of flights
  • Other quick comparisons

These types of visualizations allow her to create a compelling, data-driven presentation that has transformed data, that was once trapped in spreadsheets, into a clear picture for her boss. This helps her boss make informed, executive-level decisions to drive business forward. All of this information is also shareable with teams, so everyone has a consistent, single source of truth.

By leveraging the power of their data, the team can better identify and reduce delays, optimize costs, and improve the overall customer experience for the airline.

Put your data to use today by using the Oracle Autonomous Data Warehouse and Oracle Analytics, and watch a test drive to see Oracle Autonomous Data Warehouse in action.

For more information, check out the video below or the additional Quest resources attached at the bottom of the page.

Using Oracle Analytics with the Autonomous Data Warehouse