Ellerine Holdings has invested in an enterprise data warehouse, powered by a high performance in-memory analytical platform to streamline its merchandising and customer relationship activities and underpin its role as a growth engine for listed parent company, African Bank Investments (Abil).
Ellerine Holdings consists of six furniture brands – Geen & Richards, Beares, Ellerines, Wetherlys, Furniture City and Dial-a-Bed. It has the largest furniture retail footprint in Southern Africa, operating more than 1 000 furniture stores and employing over 12 000 people.
When Abil took over the debtors book of Ellerine Holdings in 2010, the group restructured its operations so as to focus on its core business – furniture retailing. Operational excellence in merchandise planning and supply chain management was identified as key focus areas for improvement in the group’s retail efficiencies.
Ellerine Holdings’ Chief Information Officer Ian Child says an enterprise data warehouse was seen as the foundation for integrating information across the group’s operations and improving data flows in an unsynchronised environment. The Kognitio Analytical Platform was selected as the underlying technology because of its massive parallel processing ability, relatively low cost of implementation and limited support requirements. “It was also possible to get-up and running faster than with traditional technology,” he says.
Ellerines embarked on a proof of concept exercise, simulating some data crunching challenges that had been experienced during the debtors’ ledger conversion to African Bank. “We were very impressed with Kognitio’s ability to grind through masses of data in minutes compared to the many hours and sometimes days it took on high-end relational databases,” says Child.
According to Ellerines IT Executive: Business Intelligence, Stefan Terblanche, the group had been struggling to deliver on-time, trustworthy data to business managers on the legacy infrastructure. “The original system had been designed to aggregate decentralised key performance measures in our 1 000 stores, and batch them overnight into our central information systems. We were running out of night-time and could not land actionable data by start of business the next day.
“This frustrated users and was at odds with our aim of creating operational excellence. The Business Intelligence (BI) Department spent lot of time following up queries and reconciling numbers that balanced at one point but did not match to another point. The focus on improving merchandise and supply chain processes and systems identified weaknesses in the efficiency of legacy systems that we could no longer tolerate.”
Some significant enhancements were made in moving data from the branches to a central data store developed by our enterprise resource planning (ERP) vendors. This environment has since matured and st