Big data moves faster, changes more rapidly, and has the potential to provide deep insights that weren’t previously possible. It makes sense, therefore, to adapt our business processes to handle big data and data velocity.
Success today is all about data: collecting it, harmonizing it, analyzing it, and quickly making the smartest business moves in response to it. In fact, a McKinsey study revealed that organizations that embrace customer analytics strategies have been shown to outperform their competitors and improve their corporate performance. An elastic data hub architecture can make those benefits a reality for your organization through greater speed, power and flexibility.
For consumer packaged goods companies serving retailers, forecasting the true demand for perishable products is the key to maximizing revenue while reducing the costs associated with salvaged goods. This can translate into millions of dollars of savings and additional revenue annually for some companies.
For enterprise companies, data can be a double-edged sword. On one hand, well-curated and organized data has the potential to unlock great insights, from unexpected customer behaviors to missed market opportunities. On the other hand, poorly handled data can mislead and distract—a problem that only grows with the more data you have at your disposal.
There are two extreme ends of the retail business model, each with its pros and cons. At one end all stores are virtually identical (think chains like Starbucks, Gap, Subway, etc.), which makes for simpler operations and a consistent, predictable customer experience. On the flip side are chains where the owner operator has a great deal of discretion on assortment, pricing and promotions. For example, retailers like Aeon Group in Japan, ICA in Sweden and Metro in Canada give significant control to store operators.
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