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  • May 2, 2014

3 Keys For Using Big Data Effectively for Enhanced Customer Experience

Peter Schutt
Senior Director

As organizations focus on growth and differentiation by improving the customer experience, they are looking into ways to leverage and integrate big data. In the webcast, Turning Big Data into Real-Time Action for a Greater Customer Experience, there are three key practices to maximize the value of big data:

  1. Know your customer leveraging big data: Leverage all relevant data (internal and external; structured, semi-structured, and unstructured) to understand and predict customers needs & preferences accurately.
  2. Capture, analyze, act on data fast to create value: Achieve accurate insight and take the right action fast to be appropriate and relevant to the customer’s situation.
  3. Empower employees & systems with insight & smarter decisions: Ensure that the capability to act right and fast is not limited to a few in the organization but everyone and every system and channel that interacts and influences the customers’ experience.

Oracle has a complete big data analytics portfolio to accomplish the three key areas from discovering new, comprehensive insights in operational reporting, interactive dashboards and scorecards, predictive analytics to automate and embed smarter decisions within business processes to prescribe and take real-time action.

The big data analytics are enhanced by foundational Oracle Data Integration and quality tools and are scaled by a big data management platform to combine relational and nonrelational systems.

An example of real-time analytics in action, is in mobile analytics. Oracle BI Mobile App Designer empowers business users to easily create interactive analytical applications on any device without writing a single line of code and to also take action and respond to events in the context of their day-today business activities.

Another real-time example is in the Internet of Things (IoT). As more physical things and people are connected to the Internet, often wireless with RFID tags or other sensors and Java they can record where they are and what they are doing (or not doing). The IoT will be more practical by automating the information process from capture to analysis to determine and execute appropriate and immediate action with contextual and geo-spatial location based information.

These real-time analytic systems must be agile to be managed by business users in order to respond, experiment, and adapt in real-time as the environment or consumer behavior changes. The systems also have to be intuitive for users with the business content and context who can easily visualize, understand, and change the patterns and rules or policies that are being enforced.

Will the Oracle big data analytics solutions work in a non-Oracle environment? Oracle Advanced Analytics for in-database predictive analytics offers an enterprise version of R and Oracle Real-Time Decisions that is outside of a database or application and can source and publish scores from other statistical tools such as R and SAS to prescribe and take optimal action. Oracle Endeca Information Discovery empowers business users to combine and explore qualitative and quantitative information beyond the data warehouse with the governance that IT demands. Both Oracle Data Integrator and Oracle GoldenGate similarly source and target non-Oracle data warehouses including Teradata, DB2, Netezza, Greenplum.

For complementing an existing Oracle relational environment for real-time analytics with nonrelational Hadoop, there is an Oracle Data Integrator Application Adapter and for testing there is the Oracle Big Data Lite Virtual Machine, a pre-built environment reflecting the core software of the Oracle Big Data Appliance that contains a Cloudera Hadoop distribution, an Oracle Database 12c, Oracle Big Data Connectors, and Oracle Data Integrator (ODI) 12.1.2. Use this environment to see ODI 12c in action integrating big data with the Oracle database with declarative graphical design, efficient EL-T loads, and Knowledge Models designed to optimize big data integration.

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