David Huffman, Solution Engineer at Oracle, explained to users how Oracle Autonomous Data Warehouse and Oracle Analytics Cloud can provide organizations and analysts with insights into accurate, comprehensive data.

During business-critical times, it’s essential that users can access their data. Finance analysts can’t do their jobs efficiently when the data they need is fragmented across several Excel files with no efficient way to bring all the data together. This causes reports to take forever to run. When this happens, they can’t guarantee the data accuracy, quality, or security of the Excel files, and IT won’t grant them additional hardware for peak usage times. Instead of working to make the business better, finance analysts are just waiting for things to load.

With Oracle Autonomous Data Warehouse (ADW) and Oracle Analytics Cloud (OAC), users can quickly and easily scale CPUs while remaining fully online—allowing for query optimization against trusted data with no service interruptions. Built-in and self-service analytics allow for insightful dashboards to be available at the business user level. Oracle Autonomous Data Warehouse is easy, elastic, and fast—helpful for both IT and business users alike.

Example of Autonomous Data Warehouse Use Case

David walked through a dashboard that a typical finance analyst would look at. It shows the overall health of the business over time and other key metrics. Let’s say that the finance analyst notices a problem when the dashboard shows him that the company’s net income has been dropping rapidly each month. He wants to answer questions like:

• Why is this happening?
• What can we do to stop it?

Another chart on the dashboard shows him rising operating expenses that appear to be causing the drop. Other graphs show him that the company’s UK location is the region with the largest OPEX growth over the last year. The finance analyst knows that Oracle Autonomous Data Warehouse has the information that he needs to find out more, but the whole team is accessing the same data at the same time—causing the response rate to be too slow to query again and again.

As a result, the analyst decides to add CPUs to their Autonomous Data Warehouse for a short period of time. He simply goes to the reservation system and selects how many CPUs he would like to add and for how long. The system will provide an estimated cost, and if the analyst is okay with the cost, he can click Submit. Once submitted, ADW will begin scaling instantly and will automatically scale back down after the selected amount of time. Autonomous Data Warehouse scales while remaining fully online, meaning you get improved query performance without interrupting other users’ access to the data.

Let’s say that the analyst is able to query the data. He pulls in more information about the UK’s operating expenses—splitting it by time, account group, and cost center. He is looking for any irregular behavior that can further refine the net income problem. The data that is pulled in shows the analyst two account groups—Travel & Expense, which peaked in Q3, and Salary & Wage, which increased steadily over time—as areas to look into further.

To look further, the analyst pulls in more trusted data specifically looking into these account groups. For Travel & Expense, the analyst can compare the data to previous years and budgeted amounts. By doing so, he can see that the peak in Q3 appears to be caused by a spike in a specific subcategory—hotels. By gathering this knowledge, he can make specific recommendations for corrective action. When the analyst compares the data for Salary & Wage to budget and previous years, he sees that this account group was actually only over budget for the last half of the year. The data shows the analyst that there was actually a drop in base salary spend and an increase in overtime cost at the same time. He may conclude that turnover is a likely culprit behind this, but he needs more data to know for sure.

He is able to pull in trusted HR data sets collocated in Oracle Autonomous Data Warehouse to supplement his financial data and explain the behavior. The HR data shows the analyst that there is a spike turnover in one specific cost center, Call Center Services, at the same time of the strange behavior he saw in the financial data. Immediately following this spike in turnover, overtime hours at this cost center rose and remained high—implying that the company is asking remaining employees to work more overtime hours to maintain the same level work as opposed to filling the empty roles.

The analyst was not only able to identify a high-level problem, but also dig in deeper visually to get to specific causes and make specific recommendations for corrective actions—all while having responsive reports and queries against trusted data located in Autonomous Data Warehouse.

Notional Architecture Behind Autonomous Data Warehouse

Data is pulled from Excel files, SaaS applications, and other legacy systems and is loaded into Oracle Autonomous Data Warehouse. A fast and secure connection is established between ADW and Oracle Analytics Cloud. Using the Analytics Reservation System, users can scale their Autonomous Data Warehouse to speed up reports as needed.

Conclusion

When you’re facing business-critical questions, you want to spend your time finding answers in the data, not waiting for it to load or asking if you can trust it. With Oracle Autonomous Data Warehouse and Oracle Analytics Cloud, users can speed up their reports for when they need them most without having to pay extra for when they don’t. With OAC seamless integrated with ADW, financial analysts can immediately get insights from their data quickly, accurately, and securely with virtually no downtime.

To walk through David’s example and see how the Oracle Autonomous Data Warehouse can provide insights into accurate, comprehensive data, check out the video below.

For more about the Oracle Autonomous Database, and specifically the Autonomous Data Warehouse, check out the additional Quest resources attached below.