While companies continue to get their
feet wet with practical applications of big data, one industry has made
tremendous headway in navigating through its big data and managing it to
improve competitiveness and business excellence.
I’m talking about the retail industry — and
the improvements that smarter data use and analytics have delivered not only to
retail customers and customer order fulfillment, but to the supply chains
behind all of these business processes that take the orders and then drive down
requirements to suppliers in all corners of the world that in turn deliver a
chain of finished goods to the customer.
Here are some real “difference
incoming Internet of Things (IoT) sensor data from its network of service
stations and convenience stores, a fuel retailer knows in near real time what
its fuel product mix and consumption rates are in each area of its geographical
territory. Incoming point of sale (POS) data also tells it which mixes of
products (besides fuel) work best in different geographical areas so it can
tailor offers accordingly. The end business result? Faster time to market with
offers, and better revenue positioning and agility because offers can be
instantaneously changed to respond to customer demand shifts.
minimize exposure to risk in its global supply chain, a large manufacturer
overlays geographical supplier locations with weather statistics for tornadoes,
hurricanes, earthquakes, etc. — and then calculates the probabilities of
natural disasters occurring through a predictive analytics program. The end
business result? The company now has a way to orchestrate its suppliers so that
it has backup plans and failover to suppliers in other areas of the world if a
key supplier gets hit by a disaster and the incident takes down production. By
proactively managing its perceived risks, the company now has a way to avoid
disruption to its supply chain that can endanger revenue capture and even
impair customer perceptions of the company.
retailer implements real-time predictive analytics to give it visibility of
which types of clothing items are selling “hottest” during peak
holiday seasons. It sees that green is the most popular sweater color that is
selling and quickly relays the information to its sweater supplier. Because of
the strength of its analytics program, which captures and analyzes data from
its website, the retailer has altered its manufacturing process with suppliers.
Instead of the suppliers producing set quantities of sweaters in various
colors, the suppliers now manufacture the raw sweaters without color — and then
add color based upon the near real-time customer demand instructions they
receive from the retailer. The end business results? A sharp drop in the
post-holiday items that need to be sold out at cheap clearance
levels, and the ability of the retailer to sell more units at the highest
chain logistics operation’s largest operational expense is fuel for its
vehicles. The company trains drivers in safe and sustainable driving practices,
and then installs IoT sensors in all of its trucks that measure speeds that the
drivers travel at, how long drivers keep vehicles in idle modes, and even braking
habits. The end business result? A sizable reduction in operating expense (due to fuel
savings from better driving habits) and a positive impact on the company’s
green sustainability initiative, since it has reduced its carbon footprint.
It is situations like these that make the
retail industry a great example to others in the quest for value in big
data — and also an industry that others can borrow big data analytics practices
What are the critical success factors?
in both the end business and IT were dialed in to the urgency of their
situation (i.e., the need to compete fiercely and effectively in a global
market, and to do it quickly).
- Big data
initiatives were targeted toward very specific business aims with measurable
results that were capable of contributing to the bottom line.
processes were re-engineered because the companies realized they couldn’t
do “business as usual” if they were going to effectively use all of
the new information that their big data was delivering.
companies weren’t afraid to fail in pursuit of big data initiatives. In most of
these use cases, the ultimate big data goal didn’t change. but companies found
that they had to revise processes and systems from what they had originally
thought in order to reach the goals that they set out for themselves.