Data Management made easier with Oracle’s All New Autonomous Data Warehouse Cloud

Data Management made easier with Oracle’s All New Autonomous Data Warehouse Cloud

No secret! For making better business decisions, Data is a massive asset.  Businesses are becoming data-driven, accessing & analyzing more data by more users than ever before. They need automated tools to connect & integrate, store & process as well as analyze & visualize that information. They’re looking to empower users with easy & fast ways to uncover valuable insights that help drive business transformation – without overburdening an already strained IT department. But gaining the valuable insights required to make those decisions requires quality data that we can trust. And to accomplish this we need a data strategy.

Oracle Autonomous Data Warehouse (ADW) provides a single platform that allows any user to easily connect & explore any data from anywhere, rapidly query it, use powerful analytical tools that transform it into meaningful insights, then use it to drive smarter business decisions. A modern Data warehouse that runs itself, secures itself, saves up to 80% administration costs, with virtually no human intervention.

When I looked up for the meaning of the word “Autonomous” I got this “having the freedom to govern itself or its own affairs”. Let’s peek in to understand the true capabilities of Oracle Autonomous Data Warehouse and what it has for us in the ground.

Oracle Autonomous Data Warehouse

Oracle’s Autonomous Database Cloud is self-driving, self-securing, and self-repairing, designed to eliminate manual, error-prone management processes. Data warehouse can be deployed in the Oracle cloud in few seconds, and Oracle customer benchmark studies demonstrate performance of up to 14 times faster than few other similar services. Provisioning, patching, updating, backing up, tuning, and applying security patches are all taken care of while the system is running, giving DBAs more time to focus on obtaining insights, architecting new applications, and solidifying security.

A fully autonomous, fully managed data warehouse cloud service, purpose-built for running high performance analytical queries. For quick understanding, have summarized the key features though each of these points can be described elaborately.

OneGlobe’s Customer Case-Study

For one of our BFSI Customers, we have leveraged the power of ADW with OAC and implemented an Analytics Platform.

Business Need

The Bank was looking to optimize its operational investments by analyzing and understanding performance of its network of ATM Assets spread nationwide using visual analytics & predictive models.

Our Solution

OneGlobe leveraged Oracle Analytics Cloud with Autonomous Data Warehouse to effectively identify, visualize, and analyze data to understand key performance metrics from ATM assets and their locations.

• ATM Data Analytics
• Zone wise Analytics
• Benchmark Analysis

Components

• Oracle Autonomous Datawarehouse Cloud
• Oracle Analytics Cloud – Enterprise Edition
• Oracle DataSync/ETL Tools

Results

The solution provided the ability to understand metrics from ATM assets and its branch locations to make informed decisions. Advanced Analytics capabilities delivered for the Digital Banking team enabled them to analyze ATM usages in a self-service model as a monolith solution providing the business users with multi- channel access to the solution including AI embedded voice enabled mobile analytics capabilities.

Our solution included sourcing data from Bank’s operational and transactional DBs to ADW with the help of ETL tools that seamlessly took care of the data movement. The autonomous capabilities of Analytics Cloud and Autonomous Data warehouse combinedly provided Insights, Visualization, Narration, Data discovery, Data preparation, and personalized insights in few clicks which made the solution unique. Additionally, the out-of-the-box Data Modelling capabilities in ADW helped us to stimulate the Business use-cases narrow up from the ground. We also utilized the add-on features like the ML Notebooks which aided in building on-the-fly ML Models to quickly unwrap the data patterns and secrets from ADW.

Some of the business insights delivered,

• ATM profitability analysis to identify the right locations to set-up new ATMs.
• Bank Services that customers are likely to leverage in ATMs.
• ATM usage analysis by customers and off-customers
• Approximate Expenses to other Banks due to interchange fees
• Potential Savings from Customers using Top Other Bank ATMs
• Expected profitability and withdrawal trends at a new ATM location using predictive analysis
• Benchmark analysis based on Transaction volume, Transaction amount and Income