Transforming Oracle Analytics: Oracle Fusion Analytics Warehouse


Stefan Schmitz,
VP of Product Management at
Oracle for Analytics Applications

When I joined Oracle Analytics, my team’s goal was to help business users become more data driven by easily getting value from their data. To do that, we set out to build Oracle Fusion Analytics Warehouse: a new generation of SaaS analytics applications designed to unlock the full value of enterprise data, while leveraging the power of the Autonomous Data Warehouse plus the Oracle Analytics Cloud. With Oracle Fusion Analytics, customers of Fusion ERP and HCM cloud can now rapidly do financial and HR analytics in ways they couldn’t previously.

To create a winning solution for our customers, we ensured the following:

1) Oracle takes full responsibility for the service, in particular data movement and preparation

In the past, an IT team was needed to manage analytics applications end-to-end -- from getting data out of enterprise systems, through a data pipeline and into optimized data stores. Today, Oracle Fusion Analytics is a SaaS offering and a fully Oracle-managed service. As soon as business users log into the service, they find high-quality data that’s ready for deep analytics and rapid insight delivery.

2) A fully extensible framework

The vast majority of our customers want to bring in data from a variety of disparate sources. With previous generations of analytic applications, customers were able to pull in data from other sources using a system integrator. But while doing so, they modified and customized the out-of-the-box ETL, semantics and content, making it very difficult to implement newly released analytics applications offerings and to reconcile different versions. To address this pain point, we created Oracle Fusion Analytics with an extensibility framework in place. This framework ensures that any custom extensions carry forward from release to release. Now, business users can tap into non-Oracle data sources and seamlessly adapt to Oracle Fusion Analytics releases. This is especially vital because, thanks to the Cloud, we plan to release new content and functionality every few months.

Here’s what empowered our transformation:

1) Creating an expert teamA

Within just a year we created this new cloud service from scratch. We were able to do this thanks to the combined knowledge of Oracle team members who had experience with previous BI applications, as well as hires from the outside, who brought fresh thinking and domain expertise to the table.

2) Engaging with customers every step of the way

Speaking to customers and understanding their wants and needs was critical to creating Oracle Fusion Analytics. We began engaging with customers from Day 0 -- and have continued to do so during the entire development cycle. These constant dialogues with early users have helped us prioritize our roadmap and make changes and enhancements to it along the way. Because of our interactions with early adopters and design partners, we began rolling out offerings for core financials and have an aggressive roadmap to expand it to other areas of ERP but also for customers of HCM, SCM and CX Cloud. Now, we’re wary about laying out a 12-month roadmap because we want to be adaptive to our customers’ needs. While we have themes and priorities in place, we’re also flexible and ready to adapt based on our customers’ feedback.

Our customers’ response

Our early adopters share very positive feedback about Oracle Fusion Analytics. Many of them previously struggled with custom-built solutions that didn’t completely meet their needs and that were difficult to maintain. Our customers are thrilled that they can offload support and maintenance to Oracle, which ensures that the data out of Fusion Cloud Applications flows into the Autonomous Data Warehouse and is ready for analytics as soon as our customers log in.

Our customers are looking forward to getting more content coverage. In the area of ERP Cloud, we’ve started with core financial analytics, like general ledger, accounts payable, and accounts receivable, and now the plan is to extend it to fixed assets, projects, and procurement.

Providing more value with AI, ML, and Advanced Analytics

We started with descriptive analytics in order to give our customers full flexibility for in-depth historical analysis and to drive root cause analysis. In the near future, we plan to leverage more AI, ML, and advanced analytics in order to unlock even more valuable predictive and prescriptive insights.

For example, we already provide best practice KPIs for finance and HCM as part of our applications. Soon, our analytic applications will be able to automatically predict -- and alert business users -- when a KPI is at risk of falling outside of a normal range of values, based on historic trends. Leveraging more AI, ML, and advanced analytics will make it possible for us to provide even more value to our customers

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