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but also provide a shield to future crises. The company planned to set up an analytics
organization with four goals in mind:
Deliver information tailored to meet specific needs across the
organization.
Build the skills needed to answer the competition.
Create a collaborative analytical platform across the
organization.
Gain a consistent view of what is sold across channels and
geographies.
Build future-state capability scenarios
. The retailer was eager to develop scenarios
for future capabilities, which were evaluated in terms of total costs, risks, and flexibility and
determined within the context of the corporate culture. For example, is the business data
driven? Or is the company comfortable with hypothesis-based thinking and experimentation?
Both are the essence of big data. The company critically reviewed their existing IT architecture
in the context of crucial business opportunities, such as leveraging leading-edge technologies
and providing a collaboration platform, integrating advanced analytics with existing and new
architecture, and building a scalable platform for multiple analytic types. The new technology
architecture was finalized to enable the following five key capabilities:
•
Predicting customers' purchasing and buying behaviors.
•
Developing tailored pricing, space, and assortment at stores.
•
Identifying and leveraging elasticity, affinities, and propensities
used in pricing.
•
Optimizing global data sourcing from multiple locations and
business units.
Define benefits and road map
. Armed with these capabilities, the next questions
revolve around cost-benefit analysis and risks to be mitigated. Does the company have
skills in-house or would it be more cost effective to have external resources provide
the big data analytics, at least initially? Would it make financial sense to outsource, or
should the company persist with internal resources? For each one, do the company and
the analytics team have a clear view of the data they need? All these mean significant
investment: is there a ROI plan prepared with clear milestones?
The analysts put together a data plan that clearly outlined data needs from
acquisition to storage and then to presentation using a self-serve environment across
both structured and unstructured data. The systems architecture roadmap was developed
consisting of a hybrid Hadoop-based architecture leveraging existing data warehouse
platforms. A business road map outlined a multi-million-dollar investment plan that
would deliver a positive payback in less than five years.
