Effective organizations want access to their data, fast, and they want it readily available for analytics. That's what makes Oracle’s Autonomous Data Warehouse such a great fit for businesses. It abstracts away the complexities of managing and maintaining a data warehouse while still making it easy for business analysts to sift through and analyze potentially millions of records.
This enables businesses to spend more time and resources on answering questions about how the business is performing and what to do next, and less time on routine maintenance and upgrades.
How Customers Use a Data Warehouse with Analytics
Here we've gathered together four customers who use the Autonomous Data Warehouse with their analytics. Watch what they have to say about their experience, and learn how a self-driving data warehouse helps them deliver more business value.
Drop Tank Fuels Growth with Autonomous Data Warehouse
The Autonomous Data Warehouse enables Drop Tank to stand up a data warehouse in about an hour, and then start pulling in useful information in around four hours. This enables them to see information and act upon it very quickly.
With a data warehouse that automatically scales, Drop Tank can run a promotion and even if there's 500 times the amount of transaction volume, the system can recognize that, make some tuning adjustments, secure systems, and deliver what Drop Tank needs without needing to hire people to manage that.
They've also found value in Oracle's universal credit model. Drop Tank CEO David VanWiggeren said, "If we decide we want to spin up the Analytics Cloud, try it for a day or two and turn it off, we can do that. That's incredibly flexible and very valuable to a company like us.
With Autonomous Data Warehouse, Drop Tank can now monetize their data and use it to drive a loyalty program to further delight their customers.
Data Intensity and Reporting with Autonomous Data Warehouse [video: https://www.youtube.com/watch?v=4TCJLhbzRFU]
Data Intensity decided to use Oracle Autonomous Data Warehouse to solve a problem they had around finance and financial reporting. Their finance team was spending around 60% of their time getting data out of systems, and only the remaining 40% generating value back to the business.
They chose Autonomous Data Warehouse because it was quick, easy, solved a lot of problems for them, and suited their agile development. In addition, they've really appreciated the flexibility of a data warehouse in the cloud, and being able to scale up and scale down the solution as needed for financial reporting periods.
Their CFO is especially delighted. With the Autonomous Data Warehouse and Oracle Analytics Cloud together, he can get the data he needs when he needs it—even during important meetings.
Since implementing Autonomous Data Warehouse, Data Intensity has had an initial savings of nearly a quarter of a million dollars and they're running on 10 times less hardware than they were previously. They also have 10 times the number of users accessing the system as they used to, and all of them are driving value rather than just spending their time getting data out of the system.
Looker: Analytics at the Speed of Thought
At Looker, they were seeing demand for a fully managed experience where people didn't have to worry about the hardware component. Because of the Autonomous Data Warehouse, users can focus on analytics from day #1 and have interactive question-answer sessions in real time.
Now, Looker can feel confident that they're fulfilling their growth while providing analytics to the entire organizations as they keep adding new users.
DX Marketing: Advanced Analytics in Autonomous Data Warehouse
DX Marketing wanted to build a data management platform people that non-technical people could build themselves. Having an Autonomous Data Warehouse makes things easier for the end user. And using Oracle Advanced Analytics with Autonomous Data Warehouse means that everything runs in the database. There's no external system pulling data down and processing it and putting it back, which alleviates any kind of network latency.