Today, Oracle is using big data technology and concepts to significantly
improve the effectiveness of its support operations, starting with its
hardware support group. While the company is just beginning this
journey, the initiative is already delivering valuable benefits.
2013, Oracle’s hardware support group began to look at how it could use
automation to improve support quality and accelerate service request
(SR) resolution. Its goal is to use predictive analytics to automate SR
resolution within 80% to 95% accuracy.
Oracle’s support group
gathers a tremendous amount of data. Each month, for example, it logs
35,000 new SRs and receives nearly 6 TB of telemetry data via automated
service requests (ASRs)—which represent approximately 18% of all SRs.
Like many organizations, Oracle had a siloed view of this data, which
hindered analysis. For example, it could look at SRs but could not
analyze the associated text, and it could review SRs and ASRs
separately, but not together.
Oracle was conducting manual
root-cause analysis to identify which types of SRs were the best
candidates for automation. This was a time-consuming, difficult, and
costly process, and the company looked to introduce big data and
predictive analytics to automate insight.
The team knew that it
had to walk before it could run. It started by taking information from
approximately 10 silos, such as feeds from SRs and ASRs, parts of
databases, and customer experience systems, and migrating the
information to an Oracle Endeca Information Discovery
environment. Using the powerful Oracle Endeca solution, Oracle could
look at SRs, ASRs, and associated notes in a single environment, which
immediately yielded several additional opportunities for automation. On
the first day of going live with the solution, Oracle identified 4% more
Next, Oracle focused its efforts on
gaining insight in near real time, leveraging the parallel processing of
Hadoop to automatically feed Oracle Endeca Information
Discovery—dramatically improving data velocity. Oracle’s first
initiative with this new environment looked at Oracle Solaris
SRs. In the first few weeks of that project, Oracle identified
automation opportunities that will increase automated SR resolution from
less than 1% to approximately 5%—simply by aggregating all of the data
in near real-time.
Once Oracle proved via these early proofs of
concept that it could process data more efficiently and effectively to
feed analytical projects, it began to deploy Oracle Big Data Appliance and Oracle Exalytics In-Memory Machine.