Connecting with customers over a multitude of different commerce
channels and understanding what they want and how they feel isn’t easy. It’s
certainly an area where big data harvesting and a little bit of outside industry
expertise can help.

This was one reason eBay
Enterprises was organized by eBay as an
independent company and a cloud provider of both expertise and big data management
for customer experience technologies.

The question, of course, quickly became how to commandeer
all of this big data management and database expertise to meet the standards of
reporting timeliness that clients expected.

eBay’s story

“We had clients that were looking for trends information,”
said Kevin Struckhoff, senior IT manager at eBay Enterprises’ Los Angeles Direct
Response Operation. “Some wanted to look at what their customer direct
response trends had been over the past ten years, but our database reporting
limitations were making it a challenge to deliver two years of historical
results in a timely manner.”

To meet the big data analytics needs of its clients, eBay Enterprises
needed an alternate data base approach that enabled it to get to the depth of
the data it was processing quicker so it could deliver more timely reports to
clients. These clients wanted information on consumer trends, as well as data
for back year taxes.

“One obstacle we were up against was that we didn’t
always have sufficient disk space on the system to run large jobs quickly,”
said Struckhoff. “This forced us to work on weekends to get the work done.”

Extended cycle times didn’t fit well with eBay Enterprise’s
service culture. “We have a very short time to market cycle in application
development, and when it comes to maintenance, we are normally fixing a bug
within one half hour and certainly within a day,” noted Struckhoff. “We
wanted to match this level of performance on the reporting side of our operation.”

The temptation is always there to replace servers and find
an entirely new suite of software, but eBay Enterprises elected instead to
stick with the x86 servers in its data center that were already processing
reports for clients with the help of an Informix database.

“We had experienced success with this combination, and
we already understood the technology,” said Struckhoff, “But what we
wanted to do now was to improve the performance of our big data processing and
analytics reporting.”

Retaining its servers and its database technology, eBay found
a relational database software application
designed for data analytics processing that was able to solve some of the processing
limitations in the x86 chipset by more effectively using in-memory cache with
the CPU.

“For our proof of concept, we benchmarked twelve different
queries that we commonly ran for our clients,” said Struckhoff. “In some
cases, queries that had taken 4.5 to five minutes to process in the past were
now taking under ten seconds to complete. Needless to say, we also solved the problem
of processing large analytics reports that could span ten or twelve years of data.”

What is the takeaway from the eBay Enterprises experience?

That whether or not big data is a new frontier for organizations,
the preference for many is to stick with what they know.

There is common sense in this – because until organizations
get a handle on the full set of needs and processes for big data, they could be
well served by making existing assets work for them if there is the ability to
do so.